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Wang W, Peng Y, Sun Y, Wang J, Li G. Towards Wearable and Portable Spine Motion Analysis Through Dynamic Optimization of Smartphone Videos and IMU Data. IEEE J Biomed Health Inform 2024; 28:5929-5940. [PMID: 38923475 DOI: 10.1109/jbhi.2024.3419591] [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: 06/28/2024]
Abstract
BACKGROUND Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation. METHODS This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation. RESULTS The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference. CONCLUSION Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method. SIGNIFICANCE This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.
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Bonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture 2024; 113:191-203. [PMID: 38917666 DOI: 10.1016/j.gaitpost.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). RESEARCH QUESTION How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? METHODS The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. RESULTS FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. SIGNIFICANCE We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
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Affiliation(s)
- Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Véronique Feipel
- Laboratory of Functional Anatomy, Faculty of Motor Sciences, Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
| | - Giulia Corniani
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Gamze Arin-Bal
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey; Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Alberto Leardini
- Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Ebers MR, Pitts M, Kutz JN, Steele KM. Human motion data expansion from arbitrary sparse sensors with shallow recurrent decoders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596487. [PMID: 38895371 PMCID: PMC11185509 DOI: 10.1101/2024.06.01.596487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Advances in deep learning and sparse sensing have emerged as powerful tools for monitoring human motion in natural environments. We develop a deep learning architecture, constructed from a shallow recurrent decoder network, that expands human motion data by mapping a limited (sparse) number of sensors to a comprehensive (dense) configuration, thereby inferring the motion of unmonitored body segments. Even with a single sensor, we reconstruct the comprehensive set of time series measurements, which are important for tracking and informing movement-related health and performance outcomes. Notably, this mapping leverages sensor time histories to inform the transformation from sparse to dense sensor configurations. We apply this mapping architecture to a variety of datasets, including controlled movement tasks, gait pattern exploration, and free-moving environments. Additionally, this mapping can be subject-specific (based on an individual's unique data for deployment at home and in the community) or group-based (where data from a large group are used to learn a general movement model and predict outcomes for unknown subjects). By expanding our datasets to unmeasured or unavailable quantities, this work can impact clinical trials, robotic/device control, and human performance by improving the accuracy and availability of digital biomarker estimates.
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Affiliation(s)
- Megan R Ebers
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195
| | - Mackenzie Pitts
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195
| | - J Nathan Kutz
- Department of Applied Mathematics and Electrical and Computer Engineering, University of Washington, Seattle, WA 98195
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195
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Hughes GTG, Camomilla V, Vanwanseele B, Harrison AJ, Fong DTP, Bradshaw EJ. Novel technology in sports biomechanics: some words of caution. Sports Biomech 2024; 23:393-401. [PMID: 33896368 DOI: 10.1080/14763141.2020.1869453] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Gerwyn T G Hughes
- Department of Kinesiology, University of San Francisco, San Francisco, CA, USA
| | - Valentina Camomilla
- Department of Movement, Human and Health Science, University of Rome "Foro Italico", Rome, Italy
| | - Benedicte Vanwanseele
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Andrew J Harrison
- Biomechanics Research Unit, University of Limerick, Limerick, Ireland
| | - Daniel T P Fong
- National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Elizabeth J Bradshaw
- Centre for Sport Research, School of Exercise and Nutrition Science, Deakin University, Melbourne, Australia
- Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, New Zealand
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Krishnakumar S, van Beijnum BJF, Baten CTM, Veltink PH, Buurke JH. Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2163. [PMID: 38610374 PMCID: PMC11014074 DOI: 10.3390/s24072163] [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: 01/26/2024] [Revised: 03/18/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024]
Abstract
After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.
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Affiliation(s)
- Sanchana Krishnakumar
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Bert-Jan F. van Beijnum
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Chris T. M. Baten
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
| | - Peter H. Veltink
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Jaap H. Buurke
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
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Zhang Y, Caccese JB, Kiourti A. Wearable Loop Sensor for Bilateral Knee Flexion Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:1549. [PMID: 38475086 DOI: 10.3390/s24051549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
We have previously reported wearable loop sensors that can accurately monitor knee flexion with unique merits over the state of the art. However, validation to date has been limited to single-leg configurations, discrete flexion angles, and in vitro (phantom-based) experiments. In this work, we take a major step forward to explore the bilateral monitoring of knee flexion angles, in a continuous manner, in vivo. The manuscript provides the theoretical framework of bilateral sensor operation and reports a detailed error analysis that has not been previously reported for wearable loop sensors. This includes the flatness of calibration curves that limits resolution at small angles (such as during walking) as well as the presence of motional electromotive force (EMF) noise at high angular velocities (such as during running). A novel fabrication method for flexible and mechanically robust loops is also introduced. Electromagnetic simulations and phantom-based experimental studies optimize the setup and evaluate feasibility. Proof-of-concept in vivo validation is then conducted for a human subject performing three activities (walking, brisk walking, and running), each lasting 30 s and repeated three times. The results demonstrate a promising root mean square error (RMSE) of less than 3° in most cases.
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Affiliation(s)
- Yingzhe Zhang
- ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43212, USA
| | - Jaclyn B Caccese
- School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Asimina Kiourti
- ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43212, USA
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Cimorelli A, Patel A, Karakostas T, Cotton RJ. Validation of portable in-clinic video-based gait analysis for prosthesis users. Sci Rep 2024; 14:3840. [PMID: 38360820 PMCID: PMC10869722 DOI: 10.1038/s41598-024-53217-7] [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: 01/17/2023] [Accepted: 01/30/2024] [Indexed: 02/17/2024] Open
Abstract
Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer .
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Affiliation(s)
| | - Ankit Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical & Computer Engineering, Rice University, Houston, USA
| | - Tasos Karakostas
- Shirley Ryan AbilityLab, Chicago, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, USA
| | - R James Cotton
- Shirley Ryan AbilityLab, Chicago, USA.
- Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, USA.
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8
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Willi R, Werner C, Demkó L, de Bie R, Filli L, Zörner B, Curt A, Bolliger M. Reliability of patient-specific gait profiles with inertial measurement units during the 2-min walk test in incomplete spinal cord injury. Sci Rep 2024; 14:3049. [PMID: 38321085 PMCID: PMC10847409 DOI: 10.1038/s41598-024-53301-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024] Open
Abstract
Most established clinical walking tests assess specific aspects of movement function (velocity, endurance, etc.) but are generally unable to determine specific biomechanical or neurological deficits that limit an individual's ability to walk. Recently, inertial measurement units (IMU) have been used to collect objective kinematic data for gait analysis and could be a valuable extension for clinical assessments (e.g., functional walking measures). This study assesses the reliability of an IMU-based overground gait analysis during the 2-min walk test (2mWT) in individuals with spinal cord injury (SCI). Furthermore, the study elaborates on the capability of IMUs to distinguish between different gait characteristics in individuals with SCI. Twenty-six individuals (aged 22-79) with acute or chronic SCI (AIS: C and D) completed the 2mWT with IMUs attached above each ankle on 2 test days, separated by 1 to 7 days. The IMU-based gait analysis showed good to excellent test-retest reliability (ICC: 0.77-0.99) for all gait parameters. Gait profiles remained stable between two measurements. Sensor-based gait profiling was able to reveal patient-specific gait impairments even in individuals with the same walking performance in the 2mWT. IMUs are a valuable add-on to clinical gait assessments and deliver reliable information on detailed gait pathologies in individuals with SCI.Trial registration: NCT04555759.
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Affiliation(s)
- Romina Willi
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
| | - Charlotte Werner
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
| | - László Demkó
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
| | - Rob de Bie
- Department of Epidemiology, Maastricht University, Maastricht, The Netherlands
| | - Linard Filli
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
- Swiss Center for Movement Analysis (SCMA), Balgrist Campus AG, Zurich, Switzerland
| | - Björn Zörner
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
| | - Armin Curt
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland
| | - Marc Bolliger
- Spinal Cord Injury Centre Balgrist, University Hospital, Zurich, Switzerland.
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9
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Debertin D, Wargel A, Mohr M. Reliability of Xsens IMU-Based Lower Extremity Joint Angles during In-Field Running. SENSORS (BASEL, SWITZERLAND) 2024; 24:871. [PMID: 38339587 PMCID: PMC10856827 DOI: 10.3390/s24030871] [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: 12/27/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
The Xsens Link motion capture suit has become a popular tool in investigating 3D running kinematics based on wearable inertial measurement units outside of the laboratory. In this study, we investigated the reliability of Xsens-based lower extremity joint angles during unconstrained running on stable (asphalt) and unstable (woodchip) surfaces within and between five different testing days in a group of 17 recreational runners (8 female, 9 male). Specifically, we determined the within-day and between-day intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs) with respect to discrete ankle, knee, and hip joint angles. When comparing runs within the same day, the investigated Xsens-based joint angles generally showed good to excellent reliability (median ICCs > 0.9). Between-day reliability was generally lower than the within-day estimates: Initial hip, knee, and ankle angles in the sagittal plane showed good reliability (median ICCs > 0.88), while ankle and hip angles in the frontal plane showed only poor to moderate reliability (median ICCs 0.38-0.83). The results were largely unaffected by the surface. In conclusion, within-day adaptations in lower-extremity running kinematics can be captured with the Xsens Link system. Our data on between-day reliability suggest caution when trying to capture longitudinal adaptations, specifically for ankle and hip joint angles in the frontal plane.
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Affiliation(s)
- Daniel Debertin
- Department of Sport Science, University of Innsbruck, Fürstenweg 185, A-6020 Innsbruck, Austria;
| | | | - Maurice Mohr
- Department of Sport Science, University of Innsbruck, Fürstenweg 185, A-6020 Innsbruck, Austria;
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García-Luna MA, Jimenez-Olmedo JM, Pueo B, Manchado C, Cortell-Tormo JM. Concurrent Validity of the Ergotex Device for Measuring Low Back Posture. Bioengineering (Basel) 2024; 11:98. [PMID: 38275578 PMCID: PMC10812927 DOI: 10.3390/bioengineering11010098] [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: 12/19/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
Highlighting the crucial role of monitoring and quantifying lumbopelvic rhythm for spinal curvature, the Ergotex IMU, a portable, lightweight, cost-effective, and energy-efficient technology, has been specifically designed for the pelvic and lumbar area. This study investigates the concurrent validity of the Ergotex device in measuring sagittal pelvic tilt angle. We utilized an observational, repeated measures design with healthy adult males (mean age: 39.3 ± 7.6 y, body mass: 82.2 ± 13.0 kg, body height: 179 ± 8 cm), comparing Ergotex with a 3D optical tracking system. Participants performed pelvic tilt movements in anterior, neutral, and posterior conditions. Statistical analysis included paired samples t-tests, Bland-Altman plots, and regression analysis. The findings show minimal systematic error (0.08° overall) and high agreement between the Ergotex and optical tracking, with most data points falling within limits of agreement of Bland-Altman plots (around ±2°). Significant differences were observed only in the anterior condition (0.35°, p < 0.05), with trivial effect sizes (ES = 0.08), indicating that these differences may not be clinically meaningful. The high Pearson's correlation coefficients across conditions underscore a robust linear relationship between devices (r > 0.9 for all conditions). Regression analysis showed a standard error of estimate (SEE) of 1.1° with small effect (standardized SEE < 0.26 for all conditions), meaning that the expected average deviation from the true value is around 1°. These findings validate the Ergotex as an effective, portable, and cost-efficient tool for assessing sagittal pelvic tilt, with practical implications in clinical and sports settings where traditional methods might be impractical or costly.
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Affiliation(s)
- Marco A. García-Luna
- Health, Physical Activity, and Sports Technology Research Group, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain; (M.A.G.-L.); (B.P.), (J.M.C.-T.)
| | - Jose M. Jimenez-Olmedo
- Health, Physical Activity, and Sports Technology Research Group, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain; (M.A.G.-L.); (B.P.), (J.M.C.-T.)
| | - Basilio Pueo
- Health, Physical Activity, and Sports Technology Research Group, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain; (M.A.G.-L.); (B.P.), (J.M.C.-T.)
| | - Carmen Manchado
- Sports Coaching and Performance Research Group, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain;
| | - Juan M. Cortell-Tormo
- Health, Physical Activity, and Sports Technology Research Group, Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain; (M.A.G.-L.); (B.P.), (J.M.C.-T.)
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Gasparutto X, Rose-Dulcina K, Grouvel G, DiGiovanni P, Carcreff L, Hannouche D, Armand S. Sensor-to-Bone Calibration with the Fusion of IMU and Bi-Plane X-rays. SENSORS (BASEL, SWITZERLAND) 2024; 24:419. [PMID: 38257515 PMCID: PMC10819897 DOI: 10.3390/s24020419] [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: 12/08/2023] [Revised: 12/21/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
Inertial measurement units (IMUs) need sensor-to-segment calibration to measure human kinematics. Multiple methods exist, but, when assessing populations with locomotor function pathologies, multiple limitations arise, including holding postures (limited by joint pain and stiffness), performing specific tasks (limited by lack of selectivity) or hypothesis on limb alignment (limited by bone deformity and joint stiffness). We propose a sensor-to-bone calibration based on bi-plane X-rays and a specifically designed fusion box to measure IMU orientation with respect to underlying bones. Eight patients undergoing total hip arthroplasty with bi-plane X-rays in their clinical pathway participated in the study. Patients underwent bi-plane X-rays with fusion box and skin markers followed by a gait analysis with IMUs and a marker-based method. The validity of the pelvis, thigh and hip kinematics measured with a conventional sensor-to-segment calibration and with the sensor-to-bone calibration were compared. Results showed (1) the feasibility of the fusion of bi-plane X-rays and IMUs in measuring the orientation of anatomical axes, and (2) higher validity of the sensor-to-bone calibration for the pelvic tilt and similar validity for other degrees of freedom. The main strength of this novel calibration is to remove conventional hypotheses on joint and segment orientations that are frequently violated in pathological populations.
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Affiliation(s)
- Xavier Gasparutto
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (K.R.-D.); (G.G.); (L.C.); (S.A.)
| | - Kevin Rose-Dulcina
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (K.R.-D.); (G.G.); (L.C.); (S.A.)
| | - Gautier Grouvel
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (K.R.-D.); (G.G.); (L.C.); (S.A.)
| | - Peter DiGiovanni
- Division of Orthopaedic Surgery and Musculoskeletal Trauma Care, Surgery Department, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (P.D.); (D.H.)
| | - Lena Carcreff
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (K.R.-D.); (G.G.); (L.C.); (S.A.)
| | - Didier Hannouche
- Division of Orthopaedic Surgery and Musculoskeletal Trauma Care, Surgery Department, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (P.D.); (D.H.)
| | - Stéphane Armand
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland; (K.R.-D.); (G.G.); (L.C.); (S.A.)
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Thompson R, Rico Bini R, Paton C, Hébert-Losier K. Validation of LEOMO inertial measurement unit sensors with marker-based three-dimensional motion capture during maximum sprinting in track cyclists. J Sports Sci 2024; 42:179-188. [PMID: 38440835 DOI: 10.1080/02640414.2024.2324604] [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: 03/25/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024]
Abstract
LEOMO™ is a commercial inertial measurement unit system that provides cycling-specific motion performance indicators (MPIs) and offers a mobile solution for monitoring cyclists. We aimed to validate the LEOMO sensors during sprint cycling using gold-standard marker-based three-dimensional (3D) motion technology (Qualisys, AB). Our secondary aim was to explore the relationship between peak power during sprints and MPIs. Seventeen elite track cyclists performed 3 × 15s seated start maximum efforts on a cycle ergometer. Based on intraclass correlation coefficient (ICC3,1), the MPIs derived from 3D and LEOMO showed moderate agreement (0.50 < 0.75) for the right foot angular range (FAR); left foot angular range first quadrant (FARQ1); right leg angular range (LAR); and mean angle of the pelvis in the sagittal plane. Agreement was poor (ICC < 0.50) between MPIs derived from 3D and LEOMO for the left FAR, right FARQ1, left LAR, and mean range of motion of the pelvis in the frontal and transverse planes. Only one LEOMO-derived (pelvic rotation) and two 3D-derived (right FARQ1 and FAR) MPIs showed large positive significant correlations with peak power. Caution is advised regarding use of the LEOMO for short maximal cycling efforts and derived MPIs to inform peak sprint cycling power production.
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Affiliation(s)
- Roné Thompson
- Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of Health, University of Waikato, Adams Centre for High Performance, Tauranga, New Zealand
- Department of Performance Health, High Performance Sport New Zealand, Grassroots Trust Velodrome, Cambridge, New Zealand
| | | | - Carl Paton
- School of Health and Sport Science, Te Pukenga at Eastern Institute of Technology, Napier, New Zealand
| | - Kim Hébert-Losier
- Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of Health, University of Waikato, Adams Centre for High Performance, Tauranga, New Zealand
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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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14
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Wang F, Liang W, Afzal HMR, Fan A, Li W, Dai X, Liu S, Hu Y, Li Z, Yang P. Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:9039. [PMID: 38005427 PMCID: PMC10674933 DOI: 10.3390/s23229039] [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: 09/22/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.
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Affiliation(s)
- Fanjie Wang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Wenqi Liang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Hafiz Muhammad Rehan Afzal
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Ao Fan
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Wenjiong Li
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Xiaoqian Dai
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Shujuan Liu
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Yiwei Hu
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Zhili Li
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Pengfei Yang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
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15
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Shin S, Li Z, Halilaj E. Markerless Motion Tracking With Noisy Video and IMU Data. IEEE Trans Biomed Eng 2023; 70:3082-3092. [PMID: 37171931 DOI: 10.1109/tbme.2023.3275775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
OBJECTIVE Marker-based motion capture, considered the gold standard in human motion analysis, is expensive and requires trained personnel. Advances in inertial sensing and computer vision offer new opportunities to obtain research-grade assessments in clinics and natural environments. A challenge that discourages clinical adoption, however, is the need for careful sensor-to-body alignment, which slows the data collection process in clinics and is prone to errors when patients take the sensors home. METHODS We propose deep learning models to estimate human movement with noisy data from videos (VideoNet), inertial sensors (IMUNet), and a combination of the two (FusionNet), obviating the need for careful calibration. The video and inertial sensing data used to train the models were generated synthetically from a marker-based motion capture dataset of a broad range of activities and augmented to account for sensor-misplacement and camera-occlusion errors. The models were tested using real data that included walking, jogging, squatting, sit-to-stand, and other activities. RESULTS On calibrated data, IMUNet was as accurate as state-of-the-art models, while VideoNet and FusionNet reduced mean ± std root-mean-squared errors by 7.6 ± 5.4 ° and 5.9 ± 3.3 °, respectively. Importantly, all the newly proposed models were less sensitive to noise than existing approaches, reducing errors by up to 14.0 ± 5.3 ° for sensor-misplacement errors of up to 30.0 ± 13.7 ° and by up to 7.4 ± 5.5 ° for joint-center-estimation errors of up to 101.1 ± 11.2 mm, across joints. CONCLUSION These tools offer clinicians and patients the opportunity to estimate movement with research-grade accuracy, without the need for time-consuming calibration steps or the high costs associated with commercial products such as Theia3D or Xsens, helping democratize the diagnosis, prognosis, and treatment of neuromusculoskeletal conditions.
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Ekdahl M, Loewen A, Erdman A, Sahin S, Ulman S. Inertial Measurement Unit Sensor-to-Segment Calibration Comparison for Sport-Specific Motion Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:7987. [PMID: 37766040 PMCID: PMC10534374 DOI: 10.3390/s23187987] [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: 07/27/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
Wearable inertial measurement units (IMUs) can be utilized as an alternative to optical motion capture as a method of measuring joint angles. These sensors require functional calibration prior to data collection, known as sensor-to-segment calibration. This study aims to evaluate previously described sensor-to-segment calibration methods to measure joint angle range of motion (ROM) during highly dynamic sports-related movements. Seven calibration methods were selected to compare lower extremity ROM measured using IMUs to an optical motion capture system. The accuracy of ROM measurements for each calibration method varied across joints and sport-specific tasks, with absolute mean differences between IMU measurement and motion capture measurement ranging from <0.1° to 24.1°. Fewer significant differences were observed at the pelvis than at the hip, knee, or ankle across all tasks. For each task, one or more calibration movements demonstrated non-significant differences in ROM for at least nine out of the twelve ROM variables. These results suggest that IMUs may be a viable alternative to optical motion capture for sport-specific lower-extremity ROM measurement, although the sensor-to-segment calibration methods used should be selected based on the specific tasks and variables of interest for a given application.
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Affiliation(s)
- Mitchell Ekdahl
- Scottish Rite for Children, Frisco, TX 75034, USA; (A.L.); (A.E.); (S.S.); (S.U.)
| | - Alex Loewen
- Scottish Rite for Children, Frisco, TX 75034, USA; (A.L.); (A.E.); (S.S.); (S.U.)
| | - Ashley Erdman
- Scottish Rite for Children, Frisco, TX 75034, USA; (A.L.); (A.E.); (S.S.); (S.U.)
| | - Sarp Sahin
- Scottish Rite for Children, Frisco, TX 75034, USA; (A.L.); (A.E.); (S.S.); (S.U.)
| | - Sophia Ulman
- Scottish Rite for Children, Frisco, TX 75034, USA; (A.L.); (A.E.); (S.S.); (S.U.)
- Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Siddiqui HUR, Saleem AA, Raza MA, Villar SG, Lopez LAD, Diez IDLT, Rustam F, Dudley S. Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics (Basel) 2023; 13:2881. [PMID: 37761248 PMCID: PMC10530167 DOI: 10.3390/diagnostics13182881] [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: 07/15/2023] [Revised: 08/25/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.
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Affiliation(s)
- Hafeez Ur Rehman Siddiqui
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan; (H.U.R.S.); (A.A.S.); (M.A.R.)
| | - Adil Ali Saleem
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan; (H.U.R.S.); (A.A.S.); (M.A.R.)
| | - Muhammad Amjad Raza
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan; (H.U.R.S.); (A.A.S.); (M.A.R.)
| | - Santos Gracia Villar
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (S.G.V.); (L.A.D.L.)
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Department of Extension, Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Luis Alonso Dzul Lopez
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (S.G.V.); (L.A.D.L.)
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Department of Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Isabel de la Torre Diez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Furqan Rustam
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Sandra Dudley
- Bioengineering Research Centre, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK;
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18
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Peper KK, Jensen ER, Haddadin S. Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941178 DOI: 10.1109/icorr58425.2023.10304782] [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: 11/10/2023]
Abstract
The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation (R=0.88±0.09) between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements (R = 0.62±0.27) are promising and a first application to a patient data set shows potential for future clinical application.
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19
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De Miguel-Fernández J, Salazar-Del Rio M, Rey-Prieto M, Bayón C, Guirao-Cano L, Font-Llagunes JM, Lobo-Prat J. Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study. Front Bioeng Biotechnol 2023; 11:1208561. [PMID: 37744246 PMCID: PMC10513467 DOI: 10.3389/fbioe.2023.1208561] [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: 04/19/2023] [Accepted: 08/11/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user's performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70-0.80; MAE = 0.48-0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00-0.19; MAE = 1.15-1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke.
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Affiliation(s)
- Jesús De Miguel-Fernández
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Miguel Salazar-Del Rio
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Marta Rey-Prieto
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Cristina Bayón
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | | | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
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20
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Lee R, Akhundov R, James C, Edwards S, Snodgrass SJ. Variations in Concurrent Validity of Two Independent Inertial Measurement Units Compared to Gold Standard for Upper Body Posture during Computerised Device Use. SENSORS (BASEL, SWITZERLAND) 2023; 23:6761. [PMID: 37571544 PMCID: PMC10422555 DOI: 10.3390/s23156761] [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: 05/21/2023] [Revised: 07/11/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023]
Abstract
Inertial measurement units (IMUs) may provide an objective method for measuring posture during computer use, but research is needed to validate IMUs' accuracy. We examine the concurrent validity of two different IMU systems in measuring three-dimensional (3D) upper body posture relative to a motion capture system (Mocap) as a potential device to assess postures outside a laboratory environment. We used 3D Mocap and two IMU systems (Wi-Fi and Bluetooth) to capture the upper body posture of twenty-six individuals during three physical computer working conditions (monitor correct, monitor raised, and laptop). Coefficient of determination (R2) and root-mean-square error (RMSE) compared IMUs to Mocap. Head/neck segment [HN], upper trunk segment [UTS], and joint angle [HN-UTS] were the primary variables. Wi-Fi IMUs demonstrated high validity for HN and UTS (sagittal plane) and HN-UTS (frontal plane) for all conditions, and for HN rotation movements (both for the monitor correct and monitor raised conditions), others moderate to poor. Bluetooth IMUs for HN, and UTS (sagittal plane) for the monitor correct, laptop, and monitor raised conditions were moderate. Frontal plane movements except UTS (monitor correct and laptop) and all rotation had poor validity. Both IMU systems were affected by gyroscopic drift with sporadic data loss in Bluetooth IMUs. Wi-Fi IMUs had more acceptable accuracy when measuring upper body posture during computer use compared to Mocap, except for trunk rotations. Variation in IMU systems' performance suggests validation in the task-specific movement(s) is essential.
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Affiliation(s)
- Roger Lee
- School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2308, Australia
- Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
| | - Riad Akhundov
- School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2308, Australia
- Griffith Centre for Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD 4222, Australia
| | - Carole James
- Sydney School of Health Sciences, Discipline of Occupational Therapy, Faculty of Medicine and Health, University of Sydney, Newcastle, NSW 2308, Australia
| | - Suzi Edwards
- Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
- School of Health Sciences, Discipline of Exercise & Sport Science, Faculty of Medicine & Health, Sydney University, Sydney, NSW 2006, Australia
| | - Suzanne J. Snodgrass
- School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2308, Australia
- Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
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21
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Kusunose M, Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I, Furukawa T, Kato T, Tanaka S, Kuroda R. Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:6445. [PMID: 37514738 PMCID: PMC10416158 DOI: 10.3390/s23146445] [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: 05/25/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries-MediaPipe and LightGBM-were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, -15°, or -30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R2 and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion.
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Affiliation(s)
| | - Atsuyuki Inui
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan; (M.K.); (H.N.); (Y.M.); (T.Y.); (I.S.); (T.F.); (T.K.); (S.T.); (R.K.)
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22
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Pearl O, Shin S, Godura A, Bergbreiter S, Halilaj E. Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model. J Biomech 2023; 155:111617. [PMID: 37220709 DOI: 10.1016/j.jbiomech.2023.111617] [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: 11/16/2022] [Revised: 04/26/2023] [Accepted: 05/02/2023] [Indexed: 05/25/2023]
Abstract
Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.
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Affiliation(s)
- Owen Pearl
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Soyong Shin
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ashwin Godura
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sarah Bergbreiter
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Eni Halilaj
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
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23
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Di Raimondo G, Willems M, Killen BA, Havashinezhadian S, Turcot K, Vanwanseele B, Jonkers I. Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094484. [PMID: 37177688 PMCID: PMC10181595 DOI: 10.3390/s23094484] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.
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Affiliation(s)
- Giacomo Di Raimondo
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Miel Willems
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Bryce Adrian Killen
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | | | - Katia Turcot
- Department of Kinesiology, Université Laval, Québec, QC G1V 0A6, Canada
| | - Benedicte Vanwanseele
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Ilse Jonkers
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
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Sonchan P, Ratchatanantakit N, O-larnnithipong N, Adjouadi M, Barreto A. Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion. SENSORS (BASEL, SWITZERLAND) 2023; 23:3786. [PMID: 37112128 PMCID: PMC10144482 DOI: 10.3390/s23083786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 04/05/2023] [Indexed: 06/19/2023]
Abstract
In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.
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Siviy C, Baker LM, Quinlivan BT, Porciuncula F, Swaminathan K, Awad LN, Walsh CJ. Opportunities and challenges in the development of exoskeletons for locomotor assistance. Nat Biomed Eng 2023; 7:456-472. [PMID: 36550303 DOI: 10.1038/s41551-022-00984-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 11/08/2022] [Indexed: 12/24/2022]
Abstract
Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.
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Affiliation(s)
- Christopher Siviy
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Lauren M Baker
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Brendan T Quinlivan
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Franchino Porciuncula
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Physical Therapy, College of Health and Rehabilitation Sciences: Sargent, Boston University, Boston, MA, USA
| | - Krithika Swaminathan
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Louis N Awad
- Department of Physical Therapy, College of Health and Rehabilitation Sciences: Sargent, Boston University, Boston, MA, USA
| | - Conor J Walsh
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
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Huang M, Mo S, Pak-Kwan Chan P, Chan ZYS, Zhang-Lea JH, Cheung RTH. The influence of running shoes on familiarization time for treadmill running biomechanics evaluation. Sports Biomech 2023; 22:459-472. [PMID: 35232315 DOI: 10.1080/14763141.2022.2046144] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
This study investigated treadmill familiarisation time in different shoe conditions by comparing lower limb consecutive kinematics waveforms using a trend symmetry method to calculate trend symmetry index, range amplitude ratio and range offset. Eighteen young adults (26.6 ± 3.3 years, 7 females) completed three 10-minute running trials at their preferred running speed (2.30 ± 0.17 m/s) on a treadmill with three shoe conditions (i.e., usual, minimalist and maximalist shoes) in a random order. Sagittal lower limb kinematic data were recorded using inertial measurement units. The results showed that sagittal-plane kinematic waveforms in the hip, knee and ankle remained consistent (trend symmetry > 0.95) without extreme excursions (range amplitude ratio ≈ 1) over 10 minutes within each testing shoe condition. Significant time × shoe interaction effect was observed in range offset (i.e., absolute differences in the average degree of kinematic waveforms between consecutive minutes) at ankle (p = 0.029, ŋp2 = 0.096) and knee (p = 0.002, ŋp2 = 0.126). Post-hoc analysis suggested that running with novel shoes required a shorter time to achieve stable lower limb kinematics (2 to 3 minutes) compared with usual shoes (7 minutes). In conclusion, young healthy adults need up to 3 and 7 minutes to familiarise to the treadmill when running at their preferred speed with their novel and usual running shoes.
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Affiliation(s)
- Meizhen Huang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Shiwei Mo
- Research Center of Health & Exercise Sciences, Division of Sports Science and Physical Education, Shenzhen University, Shenzhen, Guangdong, China
| | - Peter Pak-Kwan Chan
- Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Zoe Y S Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong.,Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
| | - Janet H Zhang-Lea
- Department of Integrative Physiology, University of Colorado, Boulder, CO, USA
| | - Roy T H Cheung
- School of Health Sciences, Western Sydney University, NSW, Australia
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Perpiñá-Martínez S, Arguisuelas-Martínez MD, Pérez-Domínguez B, Nacher-Moltó I, Martínez-Gramage J. Differences between Sexes and Speed Levels in Pelvic 3D Kinematic Patterns during Running Using an Inertial Measurement Unit (IMU). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3631. [PMID: 36834324 PMCID: PMC9961938 DOI: 10.3390/ijerph20043631] [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: 12/21/2022] [Revised: 01/27/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
This study aimed to assess the 3D kinematic pattern of the pelvis during running and establish differences between sexes using the IMU sensor for spatiotemporal outcomes, vertical acceleration symmetry index, and ranges of motion of the pelvis in the sagittal, coronal, and transverse planes of movement. The kinematic range in males was 5.92°-6.50°, according to tilt. The range of obliquity was between 7.84° and 9.27° and between 9.69° and 13.60°, according to pelvic rotation. In females, the results were 6.26°-7.36°, 7.81°-9.64°, and 13.2°-16.13°, respectively. Stride length increased proportionally to speed in males and females. The reliability of the inertial sensor according to tilt and gait symmetry showed good results, and the reliability levels were excellent for cadence parameters, stride length, stride time, obliquity, and pelvic rotation. The amplitude of pelvic tilt did not change at different speed levels between sexes. The range of pelvic obliquity increased in females at a medium speed level, and the pelvic rotation range increased during running, according to speed and sex. The inertial sensor has been proven to be a reliable tool for kinematic analysis during running.
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Affiliation(s)
- Sara Perpiñá-Martínez
- Department of Nursing and Physiotherapy Salus Infirmorum, Universidad Pontificia de Salamanca, 37002 Madrid, Spain
| | | | | | - Ivan Nacher-Moltó
- Department of Nursing and Physiotherapy, Universidad Cardenal Herrera CEU, CEU Universities, 46115 Valencia, Spain
| | - Javier Martínez-Gramage
- Head of Human Motion & Biomechanics in DAWAKO Medtech, Faculty of Medicine and Health Sciences, Catholic University of Valencia, 46001 Valencia, Spain
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Reneaud N, Zory R, Guérin O, Thomas L, Colson SS, Gerus P, Chorin F. Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace. SENSORS (BASEL, SWITZERLAND) 2023; 23:1812. [PMID: 36850411 PMCID: PMC9968020 DOI: 10.3390/s23041812] [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: 10/31/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
To test a novel instrumented knee brace intended for use as a rehabilitation system, based on inertial measurement units (IMU) to monitor home-based exercises, the device was compared to the gold standard of motion analysis. The purpose was to validate a new calibration method through functional tasks and assessed the value of adding magnetometers for motion analysis. Thirteen healthy young adults performed a 60-second gait test at a comfortable walking speed on a treadmill. Knee kinematics were captured simultaneously, using the instrumented knee brace and an optoelectronic camera system (OCS). The intraclass correlation coefficient (ICC) showed excellent reliability for the three axes of rotation with and without magnetometers, with values ranging between 0.900 and 0.972. Pearson's r coefficient showed good to excellent correlation for the three axes, with the root mean square error (RMSE) under 3° with the IMUs and slightly higher with the magnetometers. The instrumented knee brace obtained certain clinical parameters, as did the OCS. The instrumented knee brace seems to be a valid tool to assess ambulatory knee kinematics, with an RMSE of <3°, which is sufficient for clinical interpretations. Indeed, this portable system can obtain certain clinical parameters just as well as the gold standard of motion analysis. However, the addition of magnetometers showed no significant advantage in terms of enhancing accuracy.
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Affiliation(s)
- Nicolas Reneaud
- Université Côte d’Azur, LAMHESS, 06205 Nice, France
- Ted Orthopedics, 37 Rue Guibal, 13003 Marseille, France
- Université Côte d’Azur, CHU, 06000 Nice, France
| | - Raphaël Zory
- Université Côte d’Azur, LAMHESS, 06205 Nice, France
- Institut Universitaire de France, 75231 Paris, France
| | - Olivier Guérin
- Université Côte d’Azur, CNRS, INSERM, IRCAN, 06107 Nice, France
| | - Luc Thomas
- Ted Orthopedics, 37 Rue Guibal, 13003 Marseille, France
| | | | | | - Frédéric Chorin
- Université Côte d’Azur, LAMHESS, 06205 Nice, France
- Université Côte d’Azur, CHU, 06000 Nice, France
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Brasiliano P, Mascia G, Di Feo P, Di Stanislao E, Alvini M, Vannozzi G, Camomilla V. Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking. MICROMACHINES 2023; 14:277. [PMID: 36837977 PMCID: PMC9962364 DOI: 10.3390/mi14020277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.
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Affiliation(s)
- Paolo Brasiliano
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 6, 00135 Rome, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
| | - Guido Mascia
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 6, 00135 Rome, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
| | - Paolo Di Feo
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 6, 00135 Rome, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
| | - Eugenio Di Stanislao
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
- “ITOP SpA Officine Ortopediche”, Via Prenestina Nuova 307/A, 00036 Palestrina, Italy
| | - Martina Alvini
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
- “ITOP SpA Officine Ortopediche”, Via Prenestina Nuova 307/A, 00036 Palestrina, Italy
| | - Giuseppe Vannozzi
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 6, 00135 Rome, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
| | - Valentina Camomilla
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 6, 00135 Rome, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, 00135 Rome, Italy
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Young C, Hamilton-Wright A, Oliver ML, Gordon KD. Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:942. [PMID: 36679747 PMCID: PMC9865234 DOI: 10.3390/s23020942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/08/2023] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
Current methods for ergonomic assessment often use video-analysis to estimate wrist postures during occupational tasks. Wearable sensing and machine learning have the potential to automate this tedious task, and in doing so greatly extend the amount of data available to clinicians and researchers. A method of predicting wrist posture from inertial measurement units placed on the wrist and hand via a deep convolutional neural network has been developed. This study has quantified the accuracy and reliability of the postures predicted by this system relative to the gold standard of optoelectronic motion capture. Ten participants performed 3 different simulated occupational tasks on 2 occasions while wearing inertial measurement units on the hand and wrist. Data from the occupational task recordings were used to train a convolutional neural network classifier to estimate wrist posture in flexion/extension, and radial/ulnar deviation. The model was trained and tested in a leave-one-out cross validation format. Agreement between the proposed system and optoelectronic motion capture was 65% with κ = 0.41 in flexion/extension and 60% with κ = 0.48 in radial/ulnar deviation. The proposed system can predict wrist posture in flexion/extension and radial/ulnar deviation with accuracy and reliability congruent with published values for human estimators. This system can estimate wrist posture during occupational tasks in a small fraction of the time it takes a human to perform the same task. This offers opportunity to expand the capabilities of practitioners by eliminating the tedium of manual postural assessment.
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Affiliation(s)
- Calvin Young
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
| | - Andrew Hamilton-Wright
- School of Computer Science, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
| | - Michele L. Oliver
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
| | - Karen D. Gordon
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
- School of Computer Science, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
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Jeong S, Kim SH, Park KN. Core stability status classification based on mediolateral head motion during rhythmic movements and functional movement tests. Digit Health 2023; 9:20552076231186217. [PMID: 37434735 PMCID: PMC10331090 DOI: 10.1177/20552076231186217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 06/08/2023] [Indexed: 07/13/2023] Open
Abstract
Objective Core stability assessment is paramount for the prevention of low back pain, with core stability being considered as the most critical factor in such pain. The objective of this study was to develop a simple model for the automated assessment of core stability status. Methods To assess core stability-defined as the ability to control trunk position relative to the pelvic position - we used an inertial measurement unit sensor embedded within a wireless earbud to estimate the mediolateral head angle during rhythmic movements (RMs) such as cycling, walking, and running. The activities of muscles around the trunk were analyzed by an experienced, highly trained individual. Functional movement tests (FMTs) were performed, including single-leg squat, lunge, and side lunge. Data was collected from 77 participants, who were then classified into good and poor core stability groups based on their Sahrmann core stability test scores. Results From the head angle data, we extrapolated the symmetry index (SI) and amplitude of mediolateral head motion (Amp). Support vector machine and neural network models were trained and validated using these features. In both models, the accuracy was similar across three feature sets for RMs, FMTs, and full, and support vector machine accuracy (∼87%) is greater than neural network (∼75%). Conclusion The use of this model, trained with head motion-related features obtained during RMs or FMTs, can help to accurately classify core stability status during activities.
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Affiliation(s)
- Siwoo Jeong
- Department of Physical Therapy, Jeonju University, Jeonju, Korea
| | - Si-Hyun Kim
- Department of Physical Therapy, Sangji University, Wonju, Korea
| | - Kyue-Nam Park
- Department of Physical Therapy, Jeonju University, Jeonju, Korea
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Laidig D, Weygers I, Seel T. Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints. SENSORS (BASEL, SWITZERLAND) 2022; 22:9850. [PMID: 36560219 PMCID: PMC9785932 DOI: 10.3390/s22249850] [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: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.
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Affiliation(s)
- Daniel Laidig
- Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany
| | - Ive Weygers
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Thomas Seel
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
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Linnhoff D, Ploigt R, Mattes K. Sofigait-A Wireless Inertial Sensor-Based Gait Sonification System. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228782. [PMID: 36433376 PMCID: PMC9698922 DOI: 10.3390/s22228782] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 05/14/2023]
Abstract
In this study, a prototype of an inertial sensor-based gait sonification system was tested for the purpose of providing real-time gait feedback on the knee angle. The study consisted of two parts: (1) a comparison of the knee angle measurement to a marker-based 3D optical capturing system (Vicon, Oxford, UK) with N = 24 participants and (2) an evaluation four different sonification feedback versions in an accentuation × pitch (2 × 2) design on a sample of N = 28 participants. For the measurement system comparison, the RMSE was 7.6° ± 2.6° for the left and 6.9° ± 3.1° for the right side. Measurement agreement with bias up to −7.5° ± 6.2° (for maximum knee flexion) was indicated by the Bland−Altmann Method. The SPM revealed significant differences between both measurement systems for the area 45−90% (p < 0.001) (left) and the area between 45% and 80% (p = 0.007) (right). For the sonification perception, the variation of pitch had a significant effect on the perception of pleasantness of the sound. No effect was found for the accentuation of the swing or stance phase.
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Affiliation(s)
- Dagmar Linnhoff
- Institute of Human Movement Science, University of Hamburg, 20148 Hamburg, Germany
- Correspondence:
| | | | - Klaus Mattes
- Institute of Human Movement Science, University of Hamburg, 20148 Hamburg, Germany
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. SENSORS (BASEL, SWITZERLAND) 2022; 22:8398. [PMID: 36366096 PMCID: PMC9654083 DOI: 10.3390/s22218398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.
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Affiliation(s)
- Michael V. Potter
- Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USA
| | - Stephen M. Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Reed D. Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Palermo M, Lopes JM, André J, Matias AC, Cerqueira J, Santos CP. A multi-camera and multimodal dataset for posture and gait analysis. Sci Data 2022; 9:603. [PMID: 36202855 PMCID: PMC9537285 DOI: 10.1038/s41597-022-01722-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.
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Affiliation(s)
- Manuel Palermo
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - João M Lopes
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - João André
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana C Matias
- Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal
| | - João Cerqueira
- Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal.,Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Cristina P Santos
- CMEMS-UMinho, University of Minho, Guimarães, Portugal. .,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal. .,Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal.
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Predicting Coordination Variability of Selected Lower Extremity Couplings during a Cutting Movement: An Investigation of Deep Neural Networks with the LSTM Structure. Bioengineering (Basel) 2022; 9:bioengineering9090411. [PMID: 36134957 PMCID: PMC9495438 DOI: 10.3390/bioengineering9090411] [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: 07/06/2022] [Revised: 08/15/2022] [Accepted: 08/19/2022] [Indexed: 11/17/2022] Open
Abstract
There are still few portable methods for monitoring lower limb joint coordination during the cutting movements (CM). This study aims to obtain the relevant motion biomechanical parameters of the lower limb joints at 90°, 135°, and 180° CM by collecting IMU data of the human lower limbs, and utilizing the Long Short-Term Memory (LSTM) deep neural-network framework to predict the coordination variability of selected lower extremity couplings at the three CM directions. There was a significant (p < 0.001) difference between the three couplings during the swing, especially at 90° vs the other directions. At 135° and 180°, t13-he coordination variability of couplings was significantly greater than at 90° (p < 0.001). It is important to note that the coordination variability of Hip rotation/Knee flexion-extension was significantly higher at 90° than at 180° (p < 0.001). By the LSTM, the CM coordination variability for 90° (CMC = 0.99063, RMSE = 0.02358), 135° (CMC = 0.99018, RMSE = 0.02465) and 180° (CMC = 0.99485, RMSE = 0.01771) were accurately predicted. The predictive model could be used as a reliable tool for predicting the coordination variability of different CM directions in patients or athletes and real-world open scenarios using inertial sensors.
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Ghaffari A, Rahbek O, Lauritsen REK, Kappel A, Kold S, Rasmussen J. Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis. SENSORS (BASEL, SWITZERLAND) 2022; 22:5289. [PMID: 35890969 PMCID: PMC9322915 DOI: 10.3390/s22145289] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/08/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test-retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93-0.96]. The concordance correlation coefficients for the two systems' agreement were [0.81-0.91] in the time domain and [0.43-0.99] in the frequency domain. The absolute biases estimated by the Bland-Altman method were [0.0005-0.008] in the time domain and [0-0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.
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Affiliation(s)
- Arash Ghaffari
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark; (O.R.); (R.E.K.L.); (A.K.); (S.K.)
| | - Ole Rahbek
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark; (O.R.); (R.E.K.L.); (A.K.); (S.K.)
| | | | - Andreas Kappel
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark; (O.R.); (R.E.K.L.); (A.K.); (S.K.)
| | - Søren Kold
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark; (O.R.); (R.E.K.L.); (A.K.); (S.K.)
| | - John Rasmussen
- Department of Materials and Production, Aalborg University, 9220 Aalborg East, Denmark;
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Mascia G, Brasiliano P, Di Feo P, Cereatti A, Camomilla V. A functional calibration protocol for ankle plantar-dorsiflexion estimate using magnetic and inertial measurement units: Repeatability and reliability assessment. J Biomech 2022; 141:111202. [PMID: 35751925 DOI: 10.1016/j.jbiomech.2022.111202] [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: 11/02/2021] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022]
Abstract
The ankle joint complex presents a tangled functional anatomy, which understanding is fundamental to effectively estimate its kinematics on the sagittal plane. Protocols based on the use of magnetic and inertial measurement units (MIMUs) currently do not take in due account this factor. To this aim, a joint coordinate system for the ankle joint complex is proposed, along with a protocol to perform its anatomical calibration using MIMUs, consisting in a combination of anatomical functional calibrations of the tibiotalar axis and static acquisitions. Protocol repeatability and reliability were tested according to the metrics proposed in Schwartz et al. (2004) involving three different operators performing the protocol three times on ten participants, undergoing instrumented gait analysis through both stereophotogrammetry and MIMUs. Instrumental reliability was evaluated comparing the MIMU-derived kinematic traces with the stereophotogrammetric ones, obtained with the same protocol, through the linear fit method. A total of 270 gait cycles were considered. Results showed that the protocol was repeatable and reliable for what concerned the operators (0.4 ± 0.4 deg and 0.8 ± 0.5 deg, respectively). Instrumental reliability analysis showed a mean RMSD of 3.0 ± 1.3 deg, a mean offset of 9.4 ± 8.4 deg and a mean linear relationship strength of R2 = 0.88 ± 0.08. With due caution, the protocol can be considered both repeatable and reliable. Further studies should pay attention to the other ankle degrees of freedom as well as on the angular convention to compute them.
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Affiliation(s)
- Guido Mascia
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Paolo Brasiliano
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Paolo Di Feo
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Andrea Cereatti
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Electronics and Telecommunications, Polytechnic of Turin, Torino, Italy
| | - Valentina Camomilla
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy.
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Xiang L, Wang A, Gu Y, Zhao L, Shim V, Fernandez J. Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review. Front Neurorobot 2022; 16:913052. [PMID: 35721274 PMCID: PMC9201717 DOI: 10.3389/fnbot.2022.913052] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/04/2022] [Indexed: 01/17/2023] Open
Abstract
With the emergence of wearable technology and machine learning approaches, gait monitoring in real-time is attracting interest from the sports biomechanics community. This study presents a systematic review of machine learning approaches in running biomechanics using wearable sensors. Electronic databases were retrieved in PubMed, Web of Science, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect. A total of 4,068 articles were identified via electronic databases. Twenty-four articles that met the eligibility criteria after article screening were included in this systematic review. The range of quality scores of the included studies is from 0.78 to 1.00, with 40% of articles recruiting participant numbers between 20 and 50. The number of inertial measurement unit (IMU) placed on the lower limbs varied from 1 to 5, mainly in the pelvis, thigh, distal tibia, and foot. Deep learning algorithms occupied 57% of total machine learning approaches. Convolutional neural networks (CNN) were the most frequently used deep learning algorithm. However, the validation process for machine learning models was lacking in some studies and should be given more attention in future research. The deep learning model combining multiple CNN and recurrent neural networks (RNN) was observed to extract different running features from the wearable sensors and presents a growing trend in running biomechanics.
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Affiliation(s)
- Liangliang Xiang
- Faculty of Sports Science, Ningbo University, Ningbo, China
- Research Academy of Grand Health, Ningbo University, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo, China
- Research Academy of Grand Health, Ningbo University, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Liang Zhao
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Research Academy of Grand Health, Ningbo University, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Engineering Science, Faculty of Engineering, The University of Auckland, Auckland, New Zealand
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P 052 - Detection of lower extremity asymmetries in slow and dynamic bilateral tasks. Inertial Sensor system vs Optical Motion Capture System. Gait Posture 2022; 95:219-220. [PMID: 35644619 DOI: 10.1016/j.gaitpost.2018.06.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Patel G, Mullerpatan R, Agarwal B, Shetty T, Ojha R, Shaikh-Mohammed J, Sujatha S. Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane. Proc Inst Mech Eng H 2022; 236:686-696. [DOI: 10.1177/09544119211072971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.
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Affiliation(s)
- Gunjan Patel
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
- Biodesign Medical Technology, Synersense Private Limited, Ahmedabad, India
| | - Rajani Mullerpatan
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Bela Agarwal
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Triveni Shetty
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Rajdeep Ojha
- Movement Analysis and Rehab Research Laboratories, Department of Physical Medicine and Rehabilitation, Christian Medical College, Vellore, India
| | - Javeed Shaikh-Mohammed
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
| | - S Sujatha
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
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Di Raimondo G, Vanwanseele B, van der Have A, Emmerzaal J, Willems M, Killen BA, Jonkers I. Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim. SENSORS 2022; 22:s22093259. [PMID: 35590949 PMCID: PMC9104520 DOI: 10.3390/s22093259] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 01/08/2023]
Abstract
Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R2 between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.
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Homan K, Yamamoto K, Kadoya K, Ishida N, Iwasaki N. Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis. BMC Sports Sci Med Rehabil 2022; 14:71. [PMID: 35430808 PMCID: PMC9013462 DOI: 10.1186/s13102-022-00461-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). METHODS Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. RESULTS Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. CONCLUSION The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.
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Affiliation(s)
- Kentaro Homan
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Keizo Yamamoto
- School of Lifelong Sport, Hokusho University, 23 Bunkyodai, Ebetsu, 069-8511, Japan
| | - Ken Kadoya
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Naoki Ishida
- Department of Orthopedic Surgery, Hokuto Medical Corporation Hokuto Hospital, Kisen 7-5 Inada-cho, Obihiro, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
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Das R, Paul S, Mourya GK, Kumar N, Hussain M. Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait. Front Neurosci 2022; 16:859298. [PMID: 35495059 PMCID: PMC9051393 DOI: 10.3389/fnins.2022.859298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/01/2022] [Indexed: 12/06/2022] Open
Abstract
The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.
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Affiliation(s)
- Ratan Das
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Gajendra Kumar Mourya
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Neelesh Kumar
- Biomedical Applications Unit, Central Scientific Instruments Organisation, Chandigarh, India
| | - Masaraf Hussain
- Department of Neurology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
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Sy LW. An engineer's perspective on the mechanisms and applications of wearable inertial sensors. JOURNAL OF SPINE SURGERY (HONG KONG) 2022; 8:185-189. [PMID: 35441112 PMCID: PMC8990391 DOI: 10.21037/jss-21-108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
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Al Borno M, O’Day J, Ibarra V, Dunne J, Seth A, Habib A, Ong C, Hicks J, Uhlrich S, Delp S. OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations. J Neuroeng Rehabil 2022; 19:22. [PMID: 35184727 PMCID: PMC8859896 DOI: 10.1186/s12984-022-01001-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 02/02/2022] [Indexed: 11/29/2022] Open
Abstract
Background The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. Methods We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time. Results IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60–0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (− 0.14–0.17 deg/min). Conclusions Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-01001-x.
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Measurement of uni-planar and sport specific trunk motion using magneto-inertial measurement units: The concurrent validity of Noraxon and Xsens systems relative to a retro-reflective system. Gait Posture 2022; 92:129-134. [PMID: 34844151 DOI: 10.1016/j.gaitpost.2021.11.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/27/2021] [Accepted: 11/08/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND There is a range of magneto-inertial measurement unit (MIMU) systems commercially available, however sensor specifications and fusion methods vary considerably between manufacturers. Such variability can influence the concurrent validity of MIMUs relative to reference standard measurement devices. Different MIMUs have been compared during static or low-velocity conditions, with higher-velocity movements assessed in robotic-based studies. However, there is a need for the concurrent validity of higher-velocity movements to be established in human-based studies. RESEARCH QUESTION This study aimed to assess the concurrent validity of two commercial MIMU systems (Noraxon and Xsens), relative to a 'gold-standard' retro-reflective motion capture system, when measuring trunk angles during uni-planar range of motion (ROM) and cricket bowling, which involves high-speed, multi-planar movements. METHODS For this criterion-based validity study, both MIMU systems incorporated comparable sensor specifications and employed Kalman filter sensor fusion algorithms. The MIMU based angles were compared with angles derived from concurrently captured three-dimensional retro-reflective data for 10 fast-medium bowlers. Statistical parametric mapping and root mean squared differences (RMSD) were computed for both MIMU systems. RESULTS One-dimensional statistical parametric mapping showed no significant differences for angles from both MIMU systems when compared with retro-reflective based angle outputs. The MIMU systems produced ROM RMSDs between 1.4 ± 1.0° and 2.6 ± 1.5°. One system displayed RMSDs between 4.6 ± 1.4° and 7.4 ± 1.9° during bowling, indicating functionally relevant differences to retro-reflective derived angles. There were some small but statistically significant differences in RMSDs between the MIMU systems. SIGNIFICANCE MIMU-based angle accuracy is poorer during high-speed, multi-planar movement than uni-planar tasks. Comparable MIMU systems can produce varying measurements during ROM and bowling tasks. It is likely that varying sample rates and sensor fusion algorithm parameters contributed to the differences.
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Reliability and Validity of an Inertial Measurement System to Quantify Lower Extremity Joint Angle in Functional Movements. SENSORS 2022; 22:s22030863. [PMID: 35161609 PMCID: PMC8838175 DOI: 10.3390/s22030863] [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: 11/09/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 02/01/2023]
Abstract
The purpose of this research was to determine if the commercially available Perception Neuron motion capture system was valid and reliable in clinically relevant lower limb functional tasks. Twenty healthy participants performed two sessions on different days: gait, squat, single-leg squat, side lunge, forward lunge, and counter-movement jump. Seven IMUs and an OptiTrack system were used to record the three-dimensional joint kinematics of the lower extremity. To evaluate the performance, the multiple correlation coefficient (CMC) and the root mean square error (RMSE) of the waveforms as well as the difference and intraclass correlation coefficient (ICC) of discrete parameters were calculated. In all tasks, the CMC revealed fair to excellent waveform similarity (0.47–0.99) and the RMSE was between 3.57° and 13.14°. The difference between discrete parameters was lower than 14.54°. The repeatability analysis of waveforms showed that the CMC was between 0.54 and 0.95 and the RMSE was less than 5° in the frontal and transverse planes. The ICC of all joint angles in the IMU was general to excellent (0.57–1). Our findings showed that the IMU system might be utilized to evaluate lower extremity 3D joint kinematics in functional motions.
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Bailey CA, Uchida TK, Nantel J, Graham RB. Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait. SENSORS (BASEL, SWITZERLAND) 2021; 21:7690. [PMID: 34833766 PMCID: PMC8626040 DOI: 10.3390/s21227690] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/02/2021] [Accepted: 11/16/2021] [Indexed: 02/01/2023]
Abstract
Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Trunk, pelvis, and lower-limb joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λmax), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (RMSD: root mean square difference), consistency (ICC2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of speed and swing). RMSDs of joint angles were 1.7-9.2° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good to excellent in the primary plane of motion for ROM and in all planes for meanSD and λmax, but were poor to moderate for DFAα and SaEn. Modelled speed and swing responses for ROM, meanSD, and λmax were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angle time series, ROM in the primary plane of motion, magnitude of variability, and local dynamic stability.
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Affiliation(s)
- Christopher A. Bailey
- School of Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (C.A.B.); (J.N.)
| | - Thomas K. Uchida
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (C.A.B.); (J.N.)
| | - Ryan B. Graham
- School of Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (C.A.B.); (J.N.)
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Hong F, Huang C, Wu L, Wang M, Chen Y, She Y. Highly sensitive magnetic relaxation sensing method for aflatoxin B1 detection based on Au NP-assisted triple self-assembly cascade signal amplification. Biosens Bioelectron 2021; 192:113489. [PMID: 34293688 DOI: 10.1016/j.bios.2021.113489] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 07/01/2021] [Accepted: 07/06/2021] [Indexed: 12/11/2022]
Abstract
Highly sensitive detection of aflatoxin B1 (AFB1) is of great significance because of its high toxicity and carcinogenesis. We propose a magnetic relaxation sensing method based on gold nanoparticles (Au NPs)-assisted triple self-assembly cascade signal amplification for highly sensitive detection of AFB1. Both AFB1 antibody and initiator DNA (iDNA) are labeled on Au NPs to form Ab-Au-iDNA probe. iDNA is enriched by Au NPs to achieve first signal amplification. Different amounts of Ab-Au-iDNA were bound with AFB1 antigen by indirect competitive immunoassay, and then hybridization chain reaction event was initiated by iDNA to produce long hybridization chain reaction products to enrich more horseradish peroxidase-streptavidin for the second signal amplification. Dopamine could be rapidly converted to polydopamine by HRP catalysis, which is used as the third signal amplification. The Fe3+ solution, providing paramagnetic ions with a strong magnetic signal, could be adsorbed by the polydopamine due to the formation of coordination bonds of phenolic hydroxyl groups with Fe3+. This effective interaction between polydopamine and Fe3+ significantly changes the transverse relaxation time signal of Fe3+ supernatant solution, which can be used as a magnetic probe for highly sensitive detection of AFB1. The sensor exhibited high specificity and sensitivity with a detection limit of 0.453 pg/mL owing to the Au NP-assisted triple self-assembly cascade signal amplification strategy. It has been successfully employed for AFB1 detection in animal feed samples with consistent results of enzyme linked immune sorbent assay and high-performance liquid chromatography.
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Affiliation(s)
- Feng Hong
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China; Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Ministry of Education, Wuhan, China
| | - Chenxi Huang
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China; Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Ministry of Education, Wuhan, China
| | - Long Wu
- College of Food Science and Engineering, Hainan University, Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Haikou, Hainan, 570228, PR China
| | - Miao Wang
- Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Science/Key Laboratory of Agro-Products Quality and Safety of MOA, Beijing, 100081, PR China
| | - Yiping Chen
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China; Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Ministry of Education, Wuhan, China.
| | - Yongxin She
- Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Science/Key Laboratory of Agro-Products Quality and Safety of MOA, Beijing, 100081, PR China.
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