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Gan X, Liu X, Cai D, Zhang R, Li F, Fang H, Huang J, Qiu C, Zhan H. Wearable accelerometers reveal objective assessment of walking symmetry and regularity in idiopathic scoliosis patients. PeerJ 2024; 12:e17739. [PMID: 39035168 PMCID: PMC11259127 DOI: 10.7717/peerj.17739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/23/2024] [Indexed: 07/23/2024] Open
Abstract
Background Scoliosis is a multifaceted three-dimensional deformity that significantly affects patients' balance function and walking process. While existing research primarily focuses on spatial and temporal parameters of walking and trunk/pelvic kinematics asymmetry, there remains controversy regarding the symmetry and regularity of bilateral lower limb gait. This study aims to investigate the symmetry and regularity of bilateral lower limb gait and examine the balance control strategy of the head during walking in patients with idiopathic scoliosis. Methods The study involved 17 patients with idiopathic scoliosis of Lenke 1 and Lenke 5 classifications, along with 17 healthy subjects for comparison. Three-dimensional accelerometers were attached to the head and L5 spinous process of each participant, and three-dimensional motion acceleration signals were collected during a 10-meter walking test. Analysis of the collected acceleration signals involved calculating five variables related to the symmetry and regularity of walking: root mean square (RMS) of the acceleration signal, harmonic ratio (HR), step regularity, stride regularity, and gait symmetry. Results Our analysis reveals that, during the walking process, the three-dimensional motion acceleration signals acquired from the lumbar region of patients diagnosed with idiopathic scoliosis exhibit noteworthy disparities in the RMS of the vertical axis (RMS-VT) and the HR of the vertical axis (HR-VT) when compared to the corresponding values in the healthy control (RMS-VT: 1.6 ± 0.41 vs. 3 ± 0.47, P < 0.05; HR-VT: 3 ± 0.72 vs. 3.9 ± 0.71, P < 0.05). Additionally, the motion acceleration signals of the head in three-dimensional space, including the RMS in the anterior-posterior and vertical axis, the HR-VT, and the values of step regularity in both anterior-posterior and vertical axis, as well as the values of stride regularity in all three axes, are all significantly lower than those in the healthy control group (P < 0.05). Conclusion The findings of the analysis suggest that the application of three-dimensional accelerometer sensors proves efficacious and convenient for scrutinizing the symmetry and regularity of walking in individuals with idiopathic scoliosis. Distinctive irregularities in gait symmetry and regularity manifest in patients with idiopathic scoliosis, particularly within the antero-posterior and vertical direction. Moreover, the dynamic balance control strategy of the head in three-dimensional space among patients with idiopathic scoliosis exhibits a relatively conservative nature when compared to healthy individuals.
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Affiliation(s)
- Xiaopeng Gan
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xin Liu
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Danxian Cai
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Rongbin Zhang
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Fanqiang Li
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Haohuang Fang
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Jingrou Huang
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Chenguang Qiu
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Hongrui Zhan
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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2
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Koker O, Sahin S, Yildiz M, Adrovic A, Kasapcopur O. The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence. Rheumatol Int 2024:10.1007/s00296-024-05661-x. [PMID: 39012357 DOI: 10.1007/s00296-024-05661-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 07/05/2024] [Indexed: 07/17/2024]
Abstract
Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.
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Affiliation(s)
- Oya Koker
- Department of Pediatric Rheumatology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - Sezgin Sahin
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Mehmet Yildiz
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Amra Adrovic
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ozgur Kasapcopur
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.
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Wang L, Ni Z, Huang B. Extraction and Validation of Biomechanical Gait Parameters with Contactless FMCW Radar. SENSORS (BASEL, SWITZERLAND) 2024; 24:4184. [PMID: 39000963 PMCID: PMC11244313 DOI: 10.3390/s24134184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024]
Abstract
A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range-velocity-time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland-Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.
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Affiliation(s)
- Linyu Wang
- School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
| | - Zhongfei Ni
- College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China;
| | - Binke Huang
- School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
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Xiang K, Liu M, Chen J, Bao Y, Wang Z, Xiao K, Teng C, Ushakov N, Kumar S, Li X, Min R. AI-Assisted Insole Sensing System for Multifunctional Plantar-Healthcare Applications. ACS APPLIED MATERIALS & INTERFACES 2024; 16:32662-32678. [PMID: 38863342 DOI: 10.1021/acsami.4c04467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
The pervasive global issue of population aging has led to a growing demand for health monitoring, while the advent of electronic wearable devices has greatly alleviated the strain on the industry. However, these devices come with inherent limitations, such as electromagnetic radiation, complex structures, and high prices. Herein, a Solaris silicone rubber-integrated PMMA polymer optical fiber (S-POF) intelligent insole sensing system has been developed for remote, portable, cost-effective, and real-time gait monitoring. The system is capable of sensitively converting the pressure of key points on the sole into changes in light intensity with correlation coefficients of 0.995, 0.952, and 0.910. The S-POF sensing structure demonstrates excellent durability with a 4.8% variation in output after 10,000 cycles and provides stable feedback for bending angles. It also exhibits water resistance and temperature resistance within a certain range. Its multichannel multiplexing framework allows a smartphone to monitor multiple S-POF channels simultaneously, meeting the requirements of convenience for daily care. Also, the system can efficiently and accurately provide parameters such as pressure, step cadence, and pressure distribution, enabling the analysis of gait phases and patterns with errors of only 4.16% and 6.25% for the stance phase (STP) and the swing phase (SWP), respectively. Likewise, after comparing various AI models, an S-POF channel-based gait pattern recognition technique has been proposed with a high accuracy of up to 96.87%. Such experimental results demonstrate that the system is promising to further promote the development of rehabilitation and healthcare.
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Affiliation(s)
- Kaiyuan Xiang
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Department of Physics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
| | - Mengjie Liu
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Jun Chen
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Yingshuo Bao
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Zhuo Wang
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Department of Physics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
| | - Kun Xiao
- Department of Physics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
| | - Chuanxin Teng
- Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
| | - Nikolai Ushakov
- Institute of Electronics and Telecommunications, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia
| | - Santosh Kumar
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522302, India,
| | - Xiaoli Li
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Rui Min
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
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Voisard C, de l'Escalopier N, Ricard D, Oudre L. Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients. J Neuroeng Rehabil 2024; 21:104. [PMID: 38890696 PMCID: PMC11184826 DOI: 10.1186/s12984-024-01405-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.
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Affiliation(s)
- Cyril Voisard
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France.
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France.
| | - Nicolas de l'Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Chirurgie Orthopédique, Traumatologique et Réparatrice des Membres, Service de Santé des Armées, HIA Percy, Clamart, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France
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Peiffer M, Duquesne K, Delanghe M, Van Oevelen A, De Mits S, Audenaert E, Burssens A. Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults. Front Bioeng Biotechnol 2024; 12:1348977. [PMID: 38515625 PMCID: PMC10956131 DOI: 10.3389/fbioe.2024.1348977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 02/19/2024] [Indexed: 03/23/2024] Open
Abstract
Background: Given the inherent variability in walking speeds encountered in day-to-day activities, understanding the corresponding alterations in ankle biomechanics would provide valuable clinical insights. Therefore, the objective of this study was to examine the influence of different walking speeds on biomechanical parameters, utilizing gait analysis and musculoskeletal modelling. Methods: Twenty healthy volunteers without any lower limb medical history were included in this study. Treadmill-assisted gait-analysis with walking speeds of 0.8 m/s and 1.1 m/s was performed using the Gait Real-time Analysis Interactive Lab (GRAIL®). Collected kinematic data and ground reaction forces were processed via the AnyBody® modeling system to determine ankle kinetics and muscle forces of the lower leg. Data were statistically analyzed using statistical parametric mapping to reveal both spatiotemporal and magnitude significant differences. Results: Significant differences were found for both magnitude and spatiotemporal curves between 0.8 m/s and 1.1 m/s for the ankle flexion (p < 0.001), subtalar force (p < 0.001), ankle joint reaction force and muscles forces of the M. gastrocnemius, M. soleus and M. peroneus longus (α = 0.05). No significant spatiotemporal differences were found between 0.8 m/s and 1.1 m/s for the M. tibialis anterior and posterior. Discussion: A significant impact on ankle joint kinematics and kinetics was observed when comparing walking speeds of 0.8 m/s and 1.1 m/s. The findings of this study underscore the influence of walking speed on the biomechanics of the ankle. Such insights may provide a biomechanical rationale for several therapeutic and preventative strategies for ankle conditions.
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Affiliation(s)
- M. Peiffer
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Foot & Ankle Research and Innovation Lab (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - K. Duquesne
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - M. Delanghe
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - A. Van Oevelen
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - S. De Mits
- Department of Rheumatology, Ghent University Hospital, Ghent, Belgium
- Smart Space, Ghent University Hospital, Ghent, Belgium
| | - E. Audenaert
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Department of Trauma and Orthopaedics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Electromechanics, Op3Mech Research Group, University of Antwerp, Antwerp, Belgium
| | - A. Burssens
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
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Chen J, Fernandes J, Ke J, Lu F, King B, Hu YH, Jiang H. OptiGait: Gait Monitoring Using An Ankle-Worn Stereo Camera System. IEEE SENSORS JOURNAL 2024; 24:6888-6897. [PMID: 38476583 PMCID: PMC10927005 DOI: 10.1109/jsen.2024.3351566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
We developed an ankle-worn gait monitoring system for tracking gait parameters, including length, width, and height. The system utilizes ankle bracelets equipped with wide-angle infrared (IR) stereo cameras tasked with monitoring a marker on the opposing ankle. A computer vision algorithm we have also developed processes the imaged marker positions to estimate the length, width, and height of the person's gait. Through testing on multiple participants, the prototype of the proposed gait monitoring system exhibited notable performance, achieving an average accuracy of 96.52%, 94.46%, and 95.29% for gait length, width, and height measurements, respectively, despite distorted wide-angle images. The OptiGait system offers a cost-effective and user-friendly alternative compared to existing gait parameter sensing systems, delivering comparable accuracy in measuring gait length and width. Notably, the system demonstrates a novel capability in measuring gait height, a feature not previously reported in the literature.
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Affiliation(s)
- Jiangang Chen
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Jayer Fernandes
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Jianwei Ke
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Francis Lu
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Barbara King
- School of Nursing, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Yu Hen Hu
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Hongrui Jiang
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA
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Cimolin V, Premoli C, Bernardelli G, Amenta E, Galli M, Donno L, Lucini D, Fatti LM, Cangiano B, Persani L, Vitale G. ACROMORFO study: gait analysis in a cohort of acromegalic patients. J Endocrinol Invest 2024:10.1007/s40618-024-02340-3. [PMID: 38416368 DOI: 10.1007/s40618-024-02340-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE In acromegaly, skeletal complications resulted to be associated with low quality of life (QoL) and high risk of falls. The aim of the present study was to perform a quantitative assessment of movement through gait analysis technique in patients with acromegaly. STUDY POPULATION Thirty-three acromegalic patients [9 with active disease (AD), 14 with controlled disease (CD) and 10 with disease remission (RD)] and 20 healthy subjects were enrolled for the study. MEASUREMENTS Kinetic and kinematic data were collected with 3D-gait analysis. Kinematic data were processed to compute the Gait Profile Score (GPS), a parameter that summarizes the overall deviation of kinematic gait data relative to unaffected population. RESULTS The acromegalic group showed longer stance phase duration (p < 0.0001) compared to controls. The GPS and several gait variable scores resulted to be statistically higher in the acromegalic group compared to healthy controls. GPS values were significantly higher in AD compared to CD (p < 0.05) and RD groups (p = 0.001). The AD group presented significantly higher values in terms of hip rotation and ankle dorsiflexion compared to CD and RD groups and with regard to the foot progression compared to RD. Interestingly, patients with RD exhibited a more physiological gait pattern. CONCLUSION Acromegalic patients showed quantitative alterations of gait pattern, suggesting instability and increased risk of falls. Arthropathy, along with its associated abnormal joint loading, proprioceptive impairment and hyperkyphosis could be contributing factors. Disease control and remission appear to improve postural balance. A better knowledge on walking performance in acromegaly would help to develop specific rehabilitation programmes to reduce falls' risk and improve QoL.
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Affiliation(s)
- V Cimolin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
- IRCCS Istituto Auxologico Italiano, San Giuseppe Hospital, Strada Luigi Cadorna 90, 28824, Piancavallo, Italy
| | - C Premoli
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20122, Milan, Italy
| | - G Bernardelli
- DISCCO Department, University of Milan, 20122, Milan, Italy
- Exercise Medicine Unit, Istituto Auxologico Italiano, IRCCS, 20135, Milan, Italy
| | - E Amenta
- DISCCO Department, University of Milan, 20122, Milan, Italy
| | - M Galli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - L Donno
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - D Lucini
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20122, Milan, Italy
- Exercise Medicine Unit, Istituto Auxologico Italiano, IRCCS, 20135, Milan, Italy
| | - L M Fatti
- Department of Endocrine and Metabolic Medicine, IRCCS, Istituto Auxologico Italiano, 20149, Milan, Italy
| | - B Cangiano
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20122, Milan, Italy
- Department of Endocrine and Metabolic Medicine, IRCCS, Istituto Auxologico Italiano, 20149, Milan, Italy
| | - L Persani
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20122, Milan, Italy
- Department of Endocrine and Metabolic Medicine, IRCCS, Istituto Auxologico Italiano, 20149, Milan, Italy
| | - G Vitale
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20122, Milan, Italy.
- Laboratory of Geriatric and Oncologic Neuroendocrinology Research, IRCCS, Istituto Auxologico Italiano, 20145, Milan, Italy.
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Ramli AA, Liu X, Berndt K, Goude E, Hou J, Kaethler LB, Liu R, Lopez A, Nicorici A, Owens C, Rodriguez D, Wang J, Zhang H, Aranki D, McDonald CM, Henricson EK. Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:1123. [PMID: 38400281 PMCID: PMC10892016 DOI: 10.3390/s24041123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.
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Affiliation(s)
- Albara Ah Ramli
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Xin Liu
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Kelly Berndt
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Erica Goude
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Jiahui Hou
- Department of Electrical and Computer Engineering, School of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Lynea B. Kaethler
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Rex Liu
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Amanda Lopez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Alina Nicorici
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Corey Owens
- UC Davis Center for Health and Technology, University of California, Davis, CA 95616, USA;
| | - David Rodriguez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Jane Wang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Huanle Zhang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Daniel Aranki
- Berkeley School of Information, University of California Berkeley, Berkeley, CA 94720, USA;
| | - Craig M. McDonald
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Erik K. Henricson
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
- Graduate Group in Computer Science (GGCS), University of California, Davis, CA 95616, USA
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10
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Küderle A, Ullrich M, Roth N, Ollenschläger M, Ibrahim AA, Moradi H, Richer R, Seifer AK, Zürl M, Sîmpetru RC, Herzer L, Prossel D, Kluge F, Eskofier BM. Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:163-172. [PMID: 38487091 PMCID: PMC10939318 DOI: 10.1109/ojemb.2024.3356791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/15/2023] [Accepted: 01/17/2024] [Indexed: 03/17/2024] Open
Abstract
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.
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Affiliation(s)
- Arne Küderle
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Martin Ullrich
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Nils Roth
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Malte Ollenschläger
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Alzhraa A. Ibrahim
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
- Department of Molecular NeurologyFAU Erlangen91054ErlangenGermany
- Computer Science Department, Faculty of Computers and InformationAssiut UniversityAssiut Governorate71515Egypt
| | - Hamid Moradi
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Robert Richer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Matthias Zürl
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Raul C. Sîmpetru
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Liv Herzer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Dominik Prossel
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Felix Kluge
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
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11
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He Y, Chen Y, Tang L, Chen J, Tang J, Yang X, Su S, Zhao C, Xiao N. Accuracy validation of a wearable IMU-based gait analysis in healthy female. BMC Sports Sci Med Rehabil 2024; 16:2. [PMID: 38167148 PMCID: PMC10762813 DOI: 10.1186/s13102-023-00792-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system. METHODS In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems. RESULTS The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable. CONCLUSIONS The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.
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Affiliation(s)
- Yi He
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yuxia Chen
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jing Chen
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Jing Tang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Xiaoxuan Yang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Songchuan Su
- Chongqing Orthopedics Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Chen Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
| | - Nong Xiao
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China.
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12
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Tham LK, Al Kouzbary M, Al Kouzbary H, Liu J, Abu Osman NA. Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs. Phys Eng Sci Med 2023; 46:1723-1739. [PMID: 37870729 DOI: 10.1007/s13246-023-01332-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/06/2023] [Indexed: 10/24/2023]
Abstract
Assessment of the prosthetic gait is an important clinical approach to evaluate the quality and functionality of the prescribed lower limb prosthesis as well as to monitor rehabilitation progresses following limb amputation. Limited access to quantitative assessment tools generally affects the repeatability and consistency of prosthetic gait assessments in clinical practice. The rapidly developing wearable technology industry provides an alternative to objectively quantify prosthetic gait in the unconstrained environment. This study employs a neural network-based model in estimating three-dimensional body segmental orientation of the lower limb amputees during gait. Using a wearable system with inertial sensors attached to the lower limb segments, thirteen individuals with lower limb amputation performed two-minute walk tests on a robotic foot and a passive foot. The proposed model replicates features of a complementary filter to estimate drift free three-dimensional orientation of the intact and prosthetic limbs. The results indicate minimal estimation biases and high correlation, validating the ability of the proposed model to reproduce the properties of a complementary filter while avoiding the drawbacks, most notably in the transverse plane due to gravitational acceleration and magnetic disturbance. Results of this study also demonstrates the capability of the well-trained model to accurately estimate segmental orientation, regardless of amputation level, in different types of locomotion task.
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Affiliation(s)
- Lai Kuan Tham
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
| | - Mouaz Al Kouzbary
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Hamza Al Kouzbary
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Jingjing Liu
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Noor Azuan Abu Osman
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
- The Chancellery, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.
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13
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Bao T, Gao J, Wang J, Chen Y, Xu F, Qiao G, Li F. A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022. Front Robot AI 2023; 10:1265543. [PMID: 38047061 PMCID: PMC10691112 DOI: 10.3389/frobt.2023.1265543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/06/2023] [Indexed: 12/05/2023] Open
Abstract
Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.
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Affiliation(s)
- Tong Bao
- School of Medicine, Tsinghua University, Beijing, China
- Institute for Precision Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Jiasi Gao
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - Jinyi Wang
- School of Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Yang Chen
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Feng Xu
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Guanzhong Qiao
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Fei Li
- Institute for Precision Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
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14
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Günaydın B, İkizoğlu S. Multifractal detrended fluctuation analysis of insole pressure sensor data to diagnose vestibular system disorders. Biomed Eng Lett 2023; 13:637-648. [PMID: 37872983 PMCID: PMC10590336 DOI: 10.1007/s13534-023-00285-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 03/20/2023] [Accepted: 05/14/2023] [Indexed: 10/25/2023] Open
Abstract
The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long walking time. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short walk. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an average accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.
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Affiliation(s)
- Batuhan Günaydın
- Department of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Maslak-Istanbul, Turkey
- Present Address: Calibration Engineer at AVL Research and Engineering TR, Abdurrahmangazi Mah., Atatürk Cad. No: 22 /11-12, 34885 Istanbul, Turkey
| | - Serhat İkizoğlu
- Department of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Maslak-Istanbul, Turkey
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15
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Shimamura T, Ishikawa H, Fujii H, Katoh H. Smoothness Evaluation Indices during Sit-to-Stand-to-Sit Motions in Healthy Older Females and after Hip Fracture Using an Accelerometer: A Pilot Study. Geriatrics (Basel) 2023; 8:98. [PMID: 37887971 PMCID: PMC10606243 DOI: 10.3390/geriatrics8050098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/27/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Studies that quantify the quality of sit-to-stand-to-sit (STS) motions, particularly in terms of smoothness, are limited. Thus, this study aimed to investigate the possibility and usefulness of quality evaluation during STS motions. METHODS This cross-sectional study enrolled 36 females aged >60 years, including 18 females each in the healthy and hip fracture groups. Measurements were performed at two different speeds: five STS as fast as possible (STSF) and two seconds for each motion (STS2s). Indices of smoothness, including harmonic ratio (HR) and power spectrum entropy (PSE), were calculated and compared from the measured data in each of the three axial directions. RESULTS HR in the vertical direction was significantly higher in the healthy group (STSF: 3.65 ± 1.74, STS2s: 3.42 ± 1.54) than in the hip fracture group (STSF: 2.67 ± 1.01, STS2s: 2.58 ± 0.83) for STSF and STS2s. Furthermore, PSE for all directions and triaxial composites were significantly lower for STS2s (the healthy group (mediolateral (ML): 7.63 ± 0.31, vertical (VT): 7.46 ± 0.22, anterior-posterior (AP): 7.47 ± 0.15, triaxial: 7.45 ± 0.25), the hip fracture group (ML: 7.82 ± 0.16, VT: 7.63 ± 0.16, AP: 7.61 ± 0.17, triaxial: 7.66 ± 0.17)). CONCLUSIONS This study suggests the usefulness of HR and PSE as quality evaluations for STS motions.
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Affiliation(s)
- Takeshi Shimamura
- Department of Rehabilitation, Kumamoto Health Science University, 325 Izumi-machi, Kita-ku, Kumamoto 861-5598, Japan
- Graduate School of Health Sciences, Yamagata Prefectural University of Health Sciences, 260 Kamiyanagi, Yamagata 990-2212, Japan
| | - Hitoshi Ishikawa
- Graduate School of Health Sciences, Yamagata Prefectural University of Health Sciences, 260 Kamiyanagi, Yamagata 990-2212, Japan
| | - Hiromi Fujii
- Graduate School of Health Sciences, Yamagata Prefectural University of Health Sciences, 260 Kamiyanagi, Yamagata 990-2212, Japan
| | - Hiroshi Katoh
- Graduate School of Health Sciences, Yamagata Prefectural University of Health Sciences, 260 Kamiyanagi, Yamagata 990-2212, Japan
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16
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Aznielle-Rodríguez T, Galán-García L, Ontivero-Ortega M, Aguilar-Mateu K, Castro-Laguardia AM, Fernández-Nin A, García-Agustín D, Valdés-Sosa M. Relationship between gait parameters and cognitive indexes in adult aging. PLoS One 2023; 18:e0291963. [PMID: 37733718 PMCID: PMC10513272 DOI: 10.1371/journal.pone.0291963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
PURPOSE This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns. METHODS The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients' neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject's cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure. RESULTS The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value. CONCLUSIONS The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.
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Affiliation(s)
| | | | - Marlis Ontivero-Ortega
- Department of Neuroinformatics, Cuban Center for Neuroscience, Havana, Cuba
- Department of Data Analysis, Faculty of Psychological and Educational Sciences, Ghent University, Ghent, Belgium
| | - Karen Aguilar-Mateu
- Department of Cognitive Neuroscience, Cuban Center for Neuroscience, Havana, Cuba
| | | | - Ana Fernández-Nin
- Department of Cognitive Neuroscience, Cuban Center for Neuroscience, Havana, Cuba
| | - Daysi García-Agustín
- Centro de Investigaciones Sobre Longevidad, Envejecimiento y Salud, CITED, Havana, Cuba
| | - Mitchell Valdés-Sosa
- Department of Cognitive Neuroscience, Cuban Center for Neuroscience, Havana, Cuba
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García-Liñeira J, Leirós-Rodríguez R, Romo-Pérez V, García-Soidán JL. Accelerometric analysis of trunk acceleration during gait analysis in children between 6 and 11 years old: A cross-sectional study. Heliyon 2023; 9:e17541. [PMID: 37455952 PMCID: PMC10338309 DOI: 10.1016/j.heliyon.2023.e17541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
Background Gait analysis in children with accelerometers is of special interest in daily clinical practice, as it eliminates possible biases related to the assessor and is not very sensitivity of visual analysis. The sensitivity of data collection by these instruments makes it possible to evaluate the efficiency of body movements during gait and to better understand the degree of motor development in childhood, assessing progress within normal developmental parameters or detecting possible deficits. Research question What are the accelerations of the center of mass during normal gait in children aged 6-11 years? Methods Descriptive cross-sectional study conducted with a total of 283 school children (girls = 142). The analyzed variables were the mean and maximum values obtained in each of the three body axes and their root mean square during normal gait 10 m out, turn and 10 m back over firm ground in a straight line three times. Results The accelerometric data obtained showed similar values between sexes in each of the age sub-groups analyzed. Except for the medial-lateral axis in children aged 10-11 years where differences between sexes were detected (being significantly lower in girls). A reduction in medial-lateral axis average values over the years was also identified in both sexes. The regression models generated for the average accelerometric values showed significant values only in the average value of the medial-lateral axis. However, the maximum values were significant in all cases. Significance The preferred motor strategies of boys and girls during gait include developing mainly control and adjustment movements in the frontal plane (hence the high magnitudes recorded there). Flexion-extension movements are the most reduced over the six years of age analyzed, particularly in girls. Conversely, rotational movements are the most constant in speed in both sexes and all age subgroups.
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Affiliation(s)
- Jesús García-Liñeira
- Faculty of Education and Sport Sciences, University of Vigo, Campus a Xunqueira, s/n, 36005, Pontevedra, Spain
| | - Raquel Leirós-Rodríguez
- SALBIS Research Group, Faculty of Health Sciences, Nursing and Physical Therapy Department, University of León, Ave. Astorga, 15, 24401, Ponferrada, Spain
| | - Vicente Romo-Pérez
- Faculty of Education and Sport Sciences, University of Vigo, Campus a Xunqueira, s/n, 36005, Pontevedra, Spain
| | - Jose L. García-Soidán
- Faculty of Education and Sport Sciences, University of Vigo, Campus a Xunqueira, s/n, 36005, Pontevedra, Spain
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18
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Ng G, Gouda A, Andrysek J. Convolutional Neural Network for Estimating Spatiotemporal and Kinematic Gait Parameters using a Single Inertial Sensor . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083203 DOI: 10.1109/embc40787.2023.10340904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Lower limb disability severely impacts gait, thus requiring clinical interventions. Inertial sensor systems offer the potential for objective monitoring and assessment of gait in and out of the clinic. However, it is imperative such systems are capable of measuring important gait parameters while being minimally obtrusive (requiring few sensors). This work used convolutional neural networks to estimate a set of six spatiotemporal and kinematic gait parameters based on raw inertial sensor data. This differs from previous work which either was limited to spatiotemporal parameters or required conventional strap-down integration techniques to estimate kinematic parameters. Additionally, we investigated a data segmentation method which does not rely on gait event detection, further supporting its applicability in real-world settings.Preliminary results demonstrate our model achieved high accuracy on a mix of spatiotemporal and kinematic gait parameters, either meeting or exceeding benchmarks based on literature. We achieved 0.04 ± 0.03 mean absolute error for stance-time symmetry ratio and an absolute error of 4.78 ± 4.78, 4.50 ± 4.33, and 6.47 ± 7.37cm for right and left step length and stride length, respectively. Lastly, errors for knee and hip ranges of motion were 2.31 ± 4.20 and 1.73 ± 1.93°, respectively. The results suggest that machine learning can be a useful tool for long-term monitoring of gait using a single inertial sensor to estimate measures of gait quality.
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Gabriel CL, Pires IM, Coelho PJ, Zdravevski E, Lameski P, Mewada H, Madeira F, Garcia NM, Carreto C. Mobile and wearable technologies for the analysis of Ten Meter Walk Test: A concise systematic review. Heliyon 2023; 9:e16599. [PMID: 37274667 PMCID: PMC10238910 DOI: 10.1016/j.heliyon.2023.e16599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/06/2023] Open
Abstract
Physical issues started to receive more attention due to the sedentary lifestyle prevalent in modern culture. The Ten Meter Walk Test allows measuring the person's capacity to walk along 10 m and analyzing the advancement of various medical procedures for ailments, including stroke. This systematic review is related to the use of mobile or wearable devices to measure physical parameters while administering the Ten Meter Walk Test for the analysis of the performance of the test. We applied the PRISMA methodology for searching the papers related to the Ten Meter Walk Test. Natural Language Processing (NLP) algorithms were used to automate the screening process. Various papers published in two decades from multiple scientific databases, including IEEE Xplore, Elsevier, Springer, EMBASE, SCOPUS, Multidisciplinary Digital Publishing Institute (MDPI), and PubMed Central were analyzed, focusing on various diseases, devices, features, and methods. The study reveals that chronometer and accelerometer sensors measuring spatiotemporal features are the most pertinent in the Gait characterization of most diseases. Likewise, all studies emphasized the close relation between the quality of the sensor's data obtained and the system's ultimate accuracy. In other words, calibration procedures are needed because of the body part where the sensor is worn and the type of sensor. In addition, using ambient sensors providing kinematic and kinetic features in conjunction with wearable sensors and consistently acquiring walking signals can enhance the system's performance. The most common weaknesses in the analyzed studies are the sample size and the unavailability of continuous monitoring devices for measuring the Ten Meter Walk Test.
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Affiliation(s)
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, 6201-001 Covilhã, Portugal
- Department of Informatics and Quantitative Methods, Research Centre for Arts and Communication (CIAC)/Pole of Digital Literacy and Social Inclusion, Polytechnic Institute of Santarém, 2001-904 , Santarém, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- INESC Coimbra, University of Coimbra, Department of Electrical and Computer Engineering, Pólo 2, 3030-290, Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000, Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000, Skopje, Macedonia
| | - Hiren Mewada
- Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Kingdom of Saudi Arabia
| | - Filipe Madeira
- Department of Informatics and Quantitative Methods, Research Centre for Arts and Communication (CIAC)/Pole of Digital Literacy and Social Inclusion, Polytechnic Institute of Santarém, 2001-904 , Santarém, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, 6201-001 Covilhã, Portugal
- Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
| | - Carlos Carreto
- Research Unit for Inland Development, Polytechnic of Guarda, Guarda, Portugal
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Álvarez-Millán L, Castillo-Castillo D, Quispe-Siccha R, Pérez-Pacheco A, Angelova M, Rivera-Sánchez J, Fossion R. Frailty Syndrome as a Transition from Compensation to Decompensation: Application to the Biomechanical Regulation of Gait. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5995. [PMID: 37297599 PMCID: PMC10253052 DOI: 10.3390/ijerph20115995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/17/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023]
Abstract
Most gait parameters decrease with age and are even more importantly reduced with frailty. However, other gait parameters exhibit different or even opposite trends for aging and frailty, and the underlying reason is unclear. Literature focuses either on aging, or on frailty, and a comprehensive understanding of how biomechanical gait regulation evolves with aging and with frailty seems to be lacking. We monitored gait dynamics in young adults (19-29 years, n = 27, 59% women), middle-aged adults (30-59 years, n = 16, 62% women), and non-frail (>60 years, n = 15, 33% women) and frail older adults (>60 years, n = 31, 71% women) during a 160 m walking test using the triaxial accelerometer of the Zephyr Bioharness 3.0 device (Zephyr Technology, Annapolis, MD, USA). Frailty was evaluated using the Frail Scale (FS) and the Clinical Frailty Scale (CFS). We found that in non-frail older adults, certain gait parameters, such as cadence, were increased, whereas other parameters, such as step length, were decreased, and gait speed is maintained. Conversely, in frail older adults, all gait parameters, including gait speed, were decreased. Our interpretation is that non-frail older adults compensate for a decreased step length with an increased cadence to maintain a functional gait speed, whereas frail older adults decompensate and consequently walk with a characteristic decreased gait speed. We quantified compensation and decompensation on a continuous scale using ratios of the compensated parameter with respect to the corresponding compensating parameter. Compensation and decompensation are general medical concepts that can be applied and quantified for many, if not all, biomechanical and physiological regulatory mechanisms of the human body. This may allow for a new research strategy to quantify both aging and frailty in a systemic and dynamic way.
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Affiliation(s)
- Lesli Álvarez-Millán
- Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico;
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico
| | - Daniel Castillo-Castillo
- Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico; (D.C.-C.); (R.Q.-S.); (A.P.-P.)
| | - Rosa Quispe-Siccha
- Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico; (D.C.-C.); (R.Q.-S.); (A.P.-P.)
| | - Argelia Pérez-Pacheco
- Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico; (D.C.-C.); (R.Q.-S.); (A.P.-P.)
| | - Maia Angelova
- School of Information Technology, Melbourne Burwood Campus, Deakin University, Burwood, VIC 3125, Australia;
| | - Jesús Rivera-Sánchez
- Servicio de Geriatría, Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico;
| | - Ruben Fossion
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico
- Instituto de Ciencias Nucleares (ICN), Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico
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Pirscoveanu CI, Oliveira AS. Sensitiveness of Variables Extracted from a Fitness Smartwatch to Detect Changes in Vertical Impact Loading during Outdoors Running. SENSORS (BASEL, SWITZERLAND) 2023; 23:2928. [PMID: 36991637 PMCID: PMC10053772 DOI: 10.3390/s23062928] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Accelerometry is becoming a popular method to access human movement in outdoor conditions. Running smartwatches may acquire chest accelerometry through a chest strap, but little is known about whether the data from these chest straps can provide indirect access to changes in vertical impact properties that define rearfoot or forefoot strike. This study assessed whether the data from a fitness smartwatch and chest strap containing a tri-axial accelerometer (FS) is sensible to detect changes in running style. Twenty-eight participants performed 95 m running bouts at ~3 m/s in two conditions: normal running and running while actively reducing impact sounds (silent running). The FS acquired running cadence, ground contact time (GCT), stride length, trunk vertical oscillation (TVO), and heart rate. Moreover, a tri-axial accelerometer attached to the right shank provided peak vertical tibia acceleration (PKACC). The running parameters extracted from the FS and PKACC variables were compared between normal and silent running. Moreover, the association between PKACC and smartwatch running parameters was accessed using Pearson correlations. There was a 13 ± 19% reduction in PKACC (p < 0.005), and a 5 ± 10% increase in TVO from normal to silent running (p < 0.01). Moreover, there were slight reductions (~2 ± 2%) in cadence and GCT when silently running (p < 0.05). However, there were no significant associations between PKACC and the variables extracted from the FS (r < 0.1, p > 0.05). Therefore, our results suggest that biomechanical variables extracted from FS have limited sensitivity to detect changes in running technique. Moreover, the biomechanical variables from the FS cannot be associated with lower limb vertical loading.
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Shi LF, Liu ZY, Zhou KJ, Shi Y, Jing X. Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:849. [PMID: 36679646 PMCID: PMC9867501 DOI: 10.3390/s23020849] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/03/2023] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.
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Affiliation(s)
- Ling-Feng Shi
- School of Electronic Engineering, Xidian University, Xi’an 710071, China
| | - Zhong-Ye Liu
- School of Electronic Engineering, Xidian University, Xi’an 710071, China
| | - Ke-Jun Zhou
- School of Electronic Engineering, Xidian University, Xi’an 710071, China
| | - Yifan Shi
- Department of Mechanical and Materials Engineering, Queen’s University, 130 Stuart Street, Kingston, ON K7L 3N6, Canada
| | - Xiao Jing
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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23
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Sepas-Moghaddam A, Etemad A. Deep Gait Recognition: A Survey. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:264-284. [PMID: 35167443 DOI: 10.1109/tpami.2022.3151865] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.
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Ganguly A, Olmanson BA, Knowlton CB, Wimmer MA, Ferrigno C. Accuracy of the fully integrated Insole3's estimates of spatiotemporal parameters during walking. Med Eng Phys 2023; 111:103925. [PMID: 36792249 DOI: 10.1016/j.medengphy.2022.103925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 09/23/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
Abstract
This study investigated the accuracy of the Insole3 wireless shoe device in estimating several clinically useful spatiotemporal parameters (STPs). Eleven subjects walked at slow (0.8-1.0 m/s) and moderate-paced (1.2-1.4 m/s) speeds. Data were simultaneously recorded using the Insole3 and an industry-standard, three-dimensional motion capture (MOCAP) system. An error analysis compared the resulting STP data from the two systems. The mean bias error (MBE) was generally lower for temporal variables, and somewhat higher, but acceptable, for spatial variables. The MBE for temporally-related cadence and cycle time were the lowest (less than ±0.45%), with 100% (110/110) of slow-paced walking trial values and 99.1% (109/110) of moderate-paced walking trial values within 5% of the MOCAP estimates. The MBE was highest for speed (3.23-4.91%) and stride length (3.68-4.63%), with between 52.7 and 69.1% of trial values falling within the 5% error range. Stance time and swing time ranged between -0.98 and 4.38% error for both walking conditions. The results of this study suggest that the Insole3 is a potential alternative to MOCAP for estimating several STPs, namely cadence, stance time, and cycle time, particularly for use outside of the laboratory setting.
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Affiliation(s)
- Abhiroop Ganguly
- Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA
| | - Bjorn A Olmanson
- Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA
| | - Christopher B Knowlton
- Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA
| | - Markus A Wimmer
- Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA; Department of Anatomy and Cell Biology, Rush University Medical Center, 600 S. Paulina Street, Suite 507, Chicago, IL 60612, USA
| | - Christopher Ferrigno
- Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA; Department of Anatomy and Cell Biology, Rush University Medical Center, 600 S. Paulina Street, Suite 507, Chicago, IL 60612, USA.
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25
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Hulleck AA, Menoth Mohan D, Abdallah N, El Rich M, Khalaf K. Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:901331. [PMID: 36590154 PMCID: PMC9800936 DOI: 10.3389/fmedt.2022.901331] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. Research objective This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. Methods A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Results and significance Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.
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Affiliation(s)
- Abdul Aziz Hulleck
- Mechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Dhanya Menoth Mohan
- School of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, Australia
| | - Nada Abdallah
- Weill Cornell Medicine, New York City, NY, United States
| | - Marwan El Rich
- Mechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kinda Khalaf
- Biomedical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates,Health Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates,Correspondence: Kinda Khalaf
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26
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OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data. INFORMATICS 2022. [DOI: 10.3390/informatics9040097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.
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27
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Momin MS, Sufian A, Barman D, Dutta P, Dong M, Leo M. In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9067. [PMID: 36501769 PMCID: PMC9735577 DOI: 10.3390/s22239067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.
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Affiliation(s)
- Md Sarfaraz Momin
- Department of Computer Science, Kaliachak College, University of Gour Banga, Malda 732101, India
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Abu Sufian
- Department of Computer Science, University of Gour Banga, Malda 732101, India
| | - Debaditya Barman
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Paramartha Dutta
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Mianxiong Dong
- Department of Science and Informatics, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan
| | - Marco Leo
- National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy
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28
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Creagh AP, Dondelinger F, Lipsmeier F, Lindemann M, De Vos M. Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:202-210. [PMID: 36578776 PMCID: PMC9788677 DOI: 10.1109/ojemb.2022.3221306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/11/2022] [Accepted: 09/26/2022] [Indexed: 11/12/2022] Open
Abstract
Goal: Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. Methods: Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. Results: This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score ([Formula: see text]: 0.56,[Formula: see text]0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. Conclusion: Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.
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Affiliation(s)
- Andrew P Creagh
- Institute of Biomedical EngineeringUniversity of Oxford Oxford OX1 2JD U.K
| | | | | | | | - Maarten De Vos
- Department of Electrical EngineeringKatholieke Universiteit Leuven 3000 Leuven Belgium
- Department of Development and RegenerationKatholieke Universiteit Leuven 3000 Leuven Belgium
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29
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di Biase L, Pecoraro PM, Pecoraro G, Caminiti ML, Di Lazzaro V. Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:8486. [PMID: 36366187 PMCID: PMC9656920 DOI: 10.3390/s22218486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.
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Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Giovanni Pecoraro
- Department of Electronics Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
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Xia Y, Yang R, Hou Y, Wang H, Li Y, Zhu J, Fu C. Application of mesenchymal stem cell-derived exosomes from different sources in intervertebral disc degeneration. Front Bioeng Biotechnol 2022; 10:1019437. [PMID: 36277386 PMCID: PMC9585200 DOI: 10.3389/fbioe.2022.1019437] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/26/2022] [Indexed: 12/12/2022] Open
Abstract
Intervertebral disc degeneration (IVDD) is a main cause of lower back pain, leading to psychological and economic burdens to patients. Physical therapy only delays pain in patients but cannot eliminate the cause of IVDD. Surgery is required when the patient cannot tolerate pain or has severe neurological symptoms. Although surgical resection of IVD or decompression of the laminae eliminates the diseased segment, it damages adjacent normal IVD. There is also a risk of re-protrusion after IVD removal. Cell therapy has played a crucial role in the development of regenerative medicine. Cell transplantation promotes regeneration of degenerative tissue. However, owing to the lack of vascular structure in IVD, sufficient nutrients cannot be provided for transplanted mesenchymal stem cells (MSCs). In addition, dead cells release harmful substances that aggravate IVDD. Extracellular vesicles (EVs) have been extensively studied as an emerging therapeutic approach. EVs generated by paracrine MSCs retain the potential of MSCs and serve as carriers to deliver their contents to target cells to regulate target cell activity. Owing to their double-layered membrane structure, EVs have a low immunogenicity and no immune rejection. Therefore, EVs are considered an emerging therapeutic modality in IVDD. However, they are limited by mass production and low loading rates. In this review, the structure of IVD and advantages of EVs are introduced, and the application of MSC-EVs in IVDD is discussed. The current limitations of EVs and future applications are described.
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Affiliation(s)
- Yuanliang Xia
- Department of Spine Surgery, The First Hospital of Jilin University, Changchun, China
| | - Ruohan Yang
- Cancer Center, The First Hospital of Jilin University, Changchun, China
| | - Yulin Hou
- Department of Cardiology, Guangyuan Central Hospital, Guangyuan, China
| | - Hengyi Wang
- Department of Spine Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yuehong Li
- Department of Spine Surgery, The First Hospital of Jilin University, Changchun, China
| | - Jianshu Zhu
- Department of Spine Surgery, The First Hospital of Jilin University, Changchun, China
| | - Changfeng Fu
- Department of Spine Surgery, The First Hospital of Jilin University, Changchun, China
- *Correspondence: Changfeng Fu,
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Boekesteijn RJ, van Gerven J, Geurts ACH, Smulders K. Objective gait assessment in individuals with knee osteoarthritis using inertial sensors: A systematic review and meta-analysis. Gait Posture 2022; 98:109-120. [PMID: 36099732 DOI: 10.1016/j.gaitpost.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/16/2022] [Accepted: 09/01/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Objective assessment of gait using inertial sensors has shown promising results for functional evaluations in individuals with knee osteoarthritis (OA). However, the large number of possible outcome measures calls for a systematic evaluation of most relevant parameters to be used for scientific and clinical purposes. AIM This systematic review and meta-analysis aimed to identify gait parameters derived from inertial sensors that reflect gait deviations in individuals with knee OA compared to healthy control subjects (HC). METHODS A systematic search was conducted in five electronic databases (Medline, Embase, Web of Science, CINAHL, IEEE) to identify eligible articles. Risk of bias was assessed using a modified version of the Downs and Black scale. Data regarding study population, experimental procedures, and biomechanical outcomes were extracted. When a gait parameter was reported by a sufficient number of studies, a random-effects meta-analysis was conducted using the inverse variance method. RESULTS Twenty-three articles comparing gait between 411 individuals with knee OA and 507 HC were included. Individuals with knee OA had a lower gait speed than HC (standardized mean difference = -1.65), driven by smaller strides with a longer duration. Stride time variability was slightly higher in individuals with knee OA than in HC. Individuals with knee OA walked with a lower range of motion of the knee during the swing phase, less lumbar motion in the coronal plane, and a lower foot strike and toe-off angle compared to HC. SIGNIFICANCE This review shows that inertial sensors can detect gait impairments in individuals with knee OA. Large standardized mean differences found on spatiotemporal parameters support their applicability as sensitive endpoints for mobility in individuals with knee OA. More advanced measures, including kinematics of knee and trunk, may reveal gait adaptations that are more specific to knee OA, but compelling evidence was lacking.
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Affiliation(s)
- R J Boekesteijn
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands; Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J van Gerven
- Department of Orthopedic Surgery, Sint Maartenskliniek, Nijmegen, the Netherlands.
| | - A C H Geurts
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - K Smulders
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands.
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Prochazka A, Charvatova H, Vysata O. The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2467-2473. [PMID: 36001515 DOI: 10.1109/tnsre.2022.3201487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k -nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.
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Aznielle-Rodríguez T, Ontivero-Ortega M, Galán-García L, Sahli H, Valdés-Sosa M. Stable Sparse Classifiers predict cognitive impairment from gait patterns. Front Psychol 2022; 13:894576. [PMID: 36051195 PMCID: PMC9425080 DOI: 10.3389/fpsyg.2022.894576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. Purposes To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. Methods A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified. Results The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. Conclusions The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.
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Affiliation(s)
- Tania Aznielle-Rodríguez
- Department of Electronics, Cuban Center for Neuroscience, Havana, Cuba
- Electronics and Informatics Department, Vrije Universiteit Brussels, Brussels, Belgium
| | - Marlis Ontivero-Ortega
- Department of Neuroinformatics, Cuban Center for Neuroscience, Havana, Cuba
- Department of Data Analysis, Faculty of Psychological and Educational Sciences, Ghent University, Ghent, Belgium
| | | | - Hichem Sahli
- Electronics and Informatics Department, Vrije Universiteit Brussels, Brussels, Belgium
- Interuniversity Microelectronics Centre, Heverlee, Belgium
| | - Mitchell Valdés-Sosa
- Department of Cognitive Neuroscience, Cuban Center for Neuroscience, Havana, Cuba
- *Correspondence: Mitchell Valdés-Sosa
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Integration of persuasive elements into exergames: Application in the development of a novel gait rehabilitation system for children with musculoskeletal conditions. J Biomed Inform 2022; 132:104130. [PMID: 35820597 DOI: 10.1016/j.jbi.2022.104130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/07/2023]
Abstract
The main contribution of this paper is the application of the Persuasive System Design (PSD) model for the analysis and development of exergame systems to stimulate pediatric patients to adhere to short-term gait rehabilitation. It resulted in a novel therapy consisting of a video gaming and virtual reality (VG/VR) biofeedback system for treadmill gait rehabilitation, including a method for progressing the rehabilitation settings. During gait rehabilitation (GR) sessions, therapy settings need to be adjusted by physiotherapists, based on their clinical experience, to address the deficiencies of individuals while maximizing their motor functioning and maintaining their motivation. The system integrates persuasive elements, adjusted when physiotherapists progress the therapeutic settings, such as the treadmill speed, auditory feedback, and game challenges. We followed a scenario-based design method to develop the system, which comprised an observational study of conventional GR sessions and evaluations with 6 and 9 rehabilitation specialists who provided feedback about the system design. They perceived that the proposed method was valuable for physiotherapists to adapt the rehabilitation based on the children's performance. Also, they remarked that the system's visual and auditory stimulus would help engage the children in the therapy.
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Bendt M, Forslund EB, Hagman G, Hultling C, Seiger Å, Franzén E. Gait and dynamic balance in adults with spina bifida. Gait Posture 2022; 96:343-350. [PMID: 35820238 DOI: 10.1016/j.gaitpost.2022.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Spina bifida (SB) is a complex congenital malformation, often causing impaired gait performance depending on the level and extent of malformation. Research regarding gait and balance performance in adults with SB, has not been sufficiently described yet. RESEARCH QUESTION What are the characteristics of spatiotemporal gait parameters and balance performance in adults with SB? Further, do persons with muscle function (MF) level 3 differ regarding gait and balance performance from those with MF level 1-2? METHODS Cross-sectional observational study at an outpatient clinic. 41 adults with SB (18-65 years), who walked regularly. Spatiotemporal parameters of gait was assessed with the APDM system and balance performance with the Mini Balance Evaluation Systems Test (Mini-BESTest). Muscle strength in the legs was assessed with 0-5 manual muscle test, and participants were classified according to level of MF into groups MF1, MF2, and MF3. Two-sided t-test was used for parametric independent variables, and Cohen's d was used for effect sizes. The Mann-Whitney U test was used for non-parametric independent data and effect size was calculated by the z value (r = z/√n). RESULTS Mean gait speed was 0.96 (SD 0.20) m/s and mean stride length 1.08 m (SD 0.17), individuals with MF3 showed significantly slower gaitspeed and shorter stride length (p < 0.05). Lumbar rotation was 21° (SD 11), and thoracic lateral sway 15° (IQR 15) with significantley difference (p < 0.001 and p < 0.05) for individuals in MF3. Mini-BESTest showed a mean score of 11.3 (SD 6.9), and individuals with MF3 showed significantly lower scores (p ≤ 0.001). SIGNIFICANCE Gait and balance performance was reduced compared to normative data in almost all parameters, especially in persons with less muscle function. Increased knowledge from advanced gait analysis may help healthcare professionals to design rehabilitation programmes, in order to achieve and maintain a sustainable gait and balance performance.
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Affiliation(s)
- Martina Bendt
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Division of Physiotherapy, Aleris Rehab Station, Stockholm, Sweden.
| | - Emelie Butler Forslund
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Division of Clinical Geriatrics, Aleris Rehab Station, Stockholm, Sweden.
| | - Göran Hagman
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Karolinska University Hospital, Stockholm, Sweden.
| | - Claes Hultling
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Division of Clinical Geriatrics, Spinalis Foundation, Sophiahemmet University College, Stockholm, Sweden.
| | - Åke Seiger
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Division of Clinical Geriatrics, Aleris Rehab Station, Stockholm, Sweden.
| | - Erika Franzén
- Karolinska Institutet, Department of Neurobiology, Care Science and Society, Division of Physiotherapy, Allied Health Professionals, Medical Unit Occupational Therapy and Physical Therapy, Karolinska University Hospital, Stockholms Sjukhem R and D Unit, Stockholm, Sweden.
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Kim J, Seo H, Naseem MT, Lee CS. Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:4863. [PMID: 35808358 PMCID: PMC9269520 DOI: 10.3390/s22134863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification.
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Affiliation(s)
- Jungi Kim
- Department of Automotive Lighting Convergence Engineering, Yeungnam University, Gyeongsan 38541, Korea;
| | - Haneol Seo
- Research Institute of Human Ecology, Yeungnam University, Gyeongsan 38541, Korea; (H.S.); (M.T.N.)
| | - Muhammad Tahir Naseem
- Research Institute of Human Ecology, Yeungnam University, Gyeongsan 38541, Korea; (H.S.); (M.T.N.)
| | - Chan-Su Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
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Butkuviene M, Tamuleviciute-Prasciene E, Beigiene A, Barasaite V, Sokas D, Kubilius R, Petrenas A. Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery. IEEE J Biomed Health Inform 2022; 26:4426-4435. [PMID: 35700246 DOI: 10.1109/jbhi.2022.3181738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.
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Warren-Smith SC, Kilpatrick AD, Wisal K, Nguyen LV. Multimode optical fiber specklegram smart bed sensor array. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:067002. [PMID: 35751142 PMCID: PMC9231555 DOI: 10.1117/1.jbo.27.6.067002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes. We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially. AIM Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed. APPROACH Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3 × 4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3 × 4 image is formed by comparing the signal at each crossing point between two fibers. RESULTS The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing. CONCLUSIONS The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.
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Affiliation(s)
- Stephen C. Warren-Smith
- University of South Australia, Future Industries Institute, Mawson Lakes, South Australia, Australia
- The University of Adelaide, Institute for Photonics and Advanced Sensing, School of Physical Sciences, Adelaide, South Australia, Australia
- The University of Adelaide, Australian Research Council Centre of Excellence for Nanoscale Biophotonics, Adelaide, South Australia, Australia
| | - Adam D. Kilpatrick
- The University of Adelaide, Adelaide Nursing School, Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Kabish Wisal
- Yale University, Department of Physics, New Haven, Connecticut, United States
| | - Linh V. Nguyen
- University of South Australia, Future Industries Institute, Mawson Lakes, South Australia, Australia
- The University of Adelaide, Institute for Photonics and Advanced Sensing, School of Physical Sciences, Adelaide, South Australia, Australia
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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts. SENSORS 2022; 22:s22103859. [PMID: 35632266 PMCID: PMC9143761 DOI: 10.3390/s22103859] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 12/17/2022]
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
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Troisi Lopez E, Minino R, Sorrentino P, Manzo V, Tafuri D, Sorrentino G, Liparoti M. Sensitivity to gait improvement after levodopa intake in Parkinson's disease: A comparison study among synthetic kinematic indices. PLoS One 2022; 17:e0268392. [PMID: 35551300 PMCID: PMC9098031 DOI: 10.1371/journal.pone.0268392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 04/28/2022] [Indexed: 02/07/2023] Open
Abstract
The synthetic indices are widely used to describe balance and stability during gait. Some of these are employed to describe the gait features in Parkinson's disease (PD). However, the results are sometimes inconsistent, and the same indices are rarely used to compare the individuals affected by PD before and after levodopa intake (OFF and ON condition, respectively). Our aim was to investigate which synthetic measure among Harmonic Ratio, Jerk Ratio, Golden Ratio and Trunk Displacement Index is representative of gait stability and harmony, and which of these are more sensitive to the variations between OFF and ON condition. We found that all indices, except the Jerk Ratio, significantly improve after levodopa. Only the improvement of the Trunk Displacement Index showed a direct correlation with the motor improvement measured through the clinical scale UPDRS-III (Unified Parkinson's Disease Rating Scale-part III). In conclusion, we suggest that the synthetic indices can be useful to detect motor changes induced by, but not all of them clearly correlate with the clinical changes achieved with the levodopa administration. In our analysis, only the Trunk Displacement Index was able to show a clear relationship with the PD clinical motor improvement.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Pierpaolo Sorrentino
- Institut de Neuroscience des Systemès, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
| | - Valentino Manzo
- Alzheimer Unit and Movement Disorders Clinic, Department of Neurology, Cardarelli Hospital, Naples, Italy
| | - Domenico Tafuri
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
- Institute for Diagnosis and Care, Hermitage Capodimonte, Naples, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
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Sethi D, Bharti S, Prakash C. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work. Artif Intell Med 2022; 129:102314. [DOI: 10.1016/j.artmed.2022.102314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/15/2022]
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Ngo T, Nguyen DC, Pathirana PN, Corben LA, Delatycki MB, Horne M, Szmulewicz DJ, Roberts M. Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-crafted Feature Extractor. IEEE Trans Neural Syst Rehabil Eng 2022; 30:803-811. [PMID: 35316188 DOI: 10.1109/tnsre.2022.3161272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.
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Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards? Eur Geriatr Med 2022; 13:817-824. [PMID: 35243600 PMCID: PMC9378320 DOI: 10.1007/s41999-022-00629-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/16/2022] [Indexed: 11/06/2022]
Abstract
Aim To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Findings Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system. Message Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts. Purpose We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Methods Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale. Results The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge. Conclusion Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.
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Shin JH, Yu R, Ong JN, Lee CY, Jeon SH, Park H, Kim HJ, Lee J, Jeon B. Quantitative Gait Analysis Using a Pose-Estimation Algorithm with a Single 2D-Video of Parkinson's Disease Patients. JOURNAL OF PARKINSON'S DISEASE 2022; 11:1271-1283. [PMID: 33935106 DOI: 10.3233/jpd-212544] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Clinician-based rating scales or questionnaires for gait in Parkinson's disease (PD) are subjective and sensor-based analysis is limited in accessibility. OBJECTIVE To develop an easily accessible and objective tool to evaluate gait in PD patients, we analyzed gait from a single 2-dimensional (2D) video. METHODS We prospectively recorded 2D videos of PD patients (n = 16) and healthy controls (n = 15) performing the timed up and go test (TUG). The gait was simultaneously evaluated with a pressure-sensor (GAITRite). We estimated the 3D position of toes and heels with a deep-learning based pose-estimation algorithm and calculated gait parameters including step length, step length variability, gait velocity and step cadence which was validated with the result from the GAITRite. We further calculated the time and steps required for turning. Then, we applied the algorithm to previously recorded and archived videos of PD patients (n = 32) performing the TUG. RESULTS From the validation experiment, gait parameters derived from video tracking were in excellent agreement with the parameters obtained with the GAITRite. (Intraclass correlation coefficient > 0.9). From the analysis with the archived videos, step length, gait velocity, number of steps, and the time required for turning were significantly correlated (Absolute R > 0.4, p < 0.005) with the Freezing of gait questionnaire, Unified PD Rating scale part III total score, HY stage and postural instability. Furthermore, the video-based tracking objectively measured significant improvement of step length, gait velocity, steps and the time required for turning with antiparkinsonian medication. CONCLUSION 2D video-based tracking could objectively evaluate gait in PD patients.
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Affiliation(s)
- Jung Hwan Shin
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Ri Yu
- Department of Computer Science and Engineering, Seoul National University
| | - Jed Noel Ong
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Chan Young Lee
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Seung Ho Jeon
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Hwanpil Park
- Department of Computer Science and Engineering, Seoul National University
| | - Han-Joon Kim
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Jehee Lee
- Department of Computer Science and Engineering, Seoul National University
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
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Nguyen NT, Streepey JW. Reliability of Smartphone Accelerometers for Measuring Gait During Data Collection Over Zoom. TELEMEDICINE REPORTS 2022; 3:125-129. [PMID: 35860305 PMCID: PMC9282780 DOI: 10.1089/tmr.2022.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/14/2022] [Indexed: 05/17/2023]
Abstract
This study examined whether gait data could be reliably collected by homebound participants using iPhones under online supervision. Eighteen healthy young adults met with investigators through Zoom and installed an app to record acceleration from their iPhones' accelerometers. Half of the subjects walked normally; the other half walked while spelling words backward. During the gait tasks subjects recorded their anterior-posterior (AP), medial-lateral (ML), and vertical (V) accelerations. Data collection was repeated the following week. Seven maximum and minimum peak accelerations in the AP, ML, and vertical directions associated with events in gait were determined. Significant main effects of week and direction were observed for the first and second vertical acceleration measures. Cronbach alpha values were >0.60 for all acceleration measures, but the maximum and minimum AP accelerations that showed fair to good levels of consistency. The findings suggest gait data collected inside the home setting may be of clinical use.
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Affiliation(s)
- Nancy T. Nguyen
- Department of Kinesiology, School of Health and Human Sciences, Indiana University—Purdue University Indianapolis, Indianapolis, Indiana, USA
| | - Jefferson W. Streepey
- Department of Kinesiology, School of Health and Human Sciences, Indiana University—Purdue University Indianapolis, Indianapolis, Indiana, USA
- *Address correspondence to: Jefferson W. Streepey, PhD, Department of Kinesiology, School of Health and Human Sciences, Indiana University—Purdue University Indianapolis, Indianapolis, IN 46202, USA.
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Mobbs RJ, Perring J, Raj SM, Maharaj M, Yoong NKM, Sy LW, Fonseka RD, Natarajan P, Choy WJ. Gait metrics analysis utilizing single-point inertial measurement units: a systematic review. Mhealth 2022; 8:9. [PMID: 35178440 PMCID: PMC8800203 DOI: 10.21037/mhealth-21-17] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/27/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors. RESULTS A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls. DISCUSSION This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.
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Affiliation(s)
- Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia
| | - Jordan Perring
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | | | - Monish Maharaj
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Nicole Kah Mun Yoong
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Luke Wicent Sy
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| | - Rannulu Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
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Stride segmentation of inertial sensor data using statistical methods for different walking activities. ROBOTICA 2021. [DOI: 10.1017/s026357472100179x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter (
$t_{p}$
) which is based on minimum standard deviation (
$\sigma$
). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.
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Garner AJ, Saatchi R, Ward O, Hawley DP. Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies. Healthcare (Basel) 2021; 9:1683. [PMID: 34946409 PMCID: PMC8700900 DOI: 10.3390/healthcare9121683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/29/2022] Open
Abstract
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing-remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians' assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility.
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Affiliation(s)
- Amelia J. Garner
- The Medical School, University of Sheffield, Sheffield S10 2TN, UK
| | - Reza Saatchi
- Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Oliver Ward
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
| | - Daniel P. Hawley
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
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Standardized Biomechanical Investigation of Posture and Gait in Pisa Syndrome Disease. Symmetry (Basel) 2021. [DOI: 10.3390/sym13122237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Pisa syndrome is one of the possible postural deformities associated with Parkinson’s disease and it is clinically defined as a sustained lateral bending of the trunk. Some previous studies proposed clinical and biomechanical investigation to understand the pathophysiological mechanisms that occur, mainly focusing on EMG patterns and clinics. The current research deals with the assessment of a standardized biomechanical analysis to investigate the Pisa syndrome postural effects. Eight patients participated in the experimental test. Both static posture and gait trials were performed. An optoelectronic system and two force plates were used for data acquisition, while a custom multi-segments kinematic model of the human spine was used to evaluate the 3D angles. All subjects showed an important flexion of the trunk superior segment with respect to the inferior one, with a strong variability among patients (range values between 4.3° and 41.0°). Kinematics, ground reaction forces and spatio-temporal parameters are influenced by the asymmetrical trunk posture. Moreover, different proprioception, compensation and abilities of correction were depicted among subjects. Considering the forces exchanged by the feet with the floor during standing, results highlighted a significant asymmetry (p-value = 0.02) between the omo and contralateral side in a normal static posture, with greater load distribution on the same side of lateral deviation. When asked to self-correct the posture, all patients demonstrated a reduction of asymmetry, but without stressing any statistical significance. All these aspects might be crucial for the definition of a PS patients’ classification and for the assessment of the efficacy of treatments and rehabilitation.
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