1
|
Zadka A, Rabin N, Gazit E, Mirelman A, Nieuwboer A, Rochester L, Del Din S, Pelosin E, Avanzino L, Bloem BR, Della Croce U, Cereatti A, Hausdorff JM. A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders. NPJ Digit Med 2024; 7:142. [PMID: 38796519 PMCID: PMC11127966 DOI: 10.1038/s41746-024-01136-2] [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: 12/26/2023] [Accepted: 05/10/2024] [Indexed: 05/28/2024] Open
Abstract
Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson's disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.
Collapse
Affiliation(s)
- Assaf Zadka
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neta Rabin
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Anat Mirelman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medical & Health Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Tyne, NE1 7RU, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Tyne, NE1 7RU, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Laura Avanzino
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Bastiaan R Bloem
- Radboud university medical center, Donders Institute for Brain, Cognition, and Behavior; Department of Neurology, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.
- Faculty of Medical & Health Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- Department of Physical Therapy, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center and Rush University Medical Center, Chicago, Illinois, USA.
| |
Collapse
|
2
|
Kim M, Park S. Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor. Sci Rep 2024; 14:9201. [PMID: 38649763 PMCID: PMC11035581 DOI: 10.1038/s41598-024-59949-w] [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: 06/02/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
In this study, we address two technical challenges to enhance golf swing trajectory accuracy using a wrist-worn inertial sensor: orientation estimation and drift error mitigation. We extrapolated consistent sensor orientation from specific address-phase signal segments and trained the estimation with a convolutional neural network. We then mitigated drift error by applying a constraint on wrist speed at the address, backswing top, and finish, and ensuring that the wrist's finish displacement aligns with a virtual circle on the 3D swing plane. To verify the proposed methods, we gathered data from twenty male right-handed golfers, including professionals and amateurs, using a driver and a 7-iron. The orientation estimation error was about 60% of the baseline, comparable to studies requiring additional sensor information or calibration poses. The drift error was halved and the single-inertial-sensor tracking performance across all swing phases was about 17 cm, on par with multimodal approaches. This study introduces a novel signal processing method for tracking rapid, wide-ranging motions, such as a golf swing, while maintaining user convenience. Our results could impact the burgeoning field of daily motion monitoring for health care, especially with the increasing prevalence of wearable devices like smartwatches.
Collapse
Affiliation(s)
- Myeongsub Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Sukyung Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
| |
Collapse
|
3
|
Reinhardt L, Schwesig R, Schulze S, Donath L, Kurz E. Accuracy of unilateral and bilateral gait assessment using a mobile gait analysis system at different walking speeds. Gait Posture 2024; 109:291-297. [PMID: 38387196 DOI: 10.1016/j.gaitpost.2024.01.029] [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: 02/21/2023] [Revised: 12/24/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Previous research on the accuracy of mobile measurement systems has focused on parameters related to the whole gait cycle. Specifically, bilateral gait characteristics were primarily used as outcome measures. RESEARCH QUESTION How accurate are unilateral gait characteristics detected using a mobile system at various fixed walking speeds? METHODS Gait analysis during treadmill walking at velocities (VEL) of 2.5 (v1), 4.5 (v2) and 6.5 km/h (v3) was performed in a population of 47 healthy young adults, consisting of 27 females (age: 23 ± 2 years, BMI: 21.4 ± 2.2 kg/m²) and 20 males (age: 22 ± 1 years, BMI: 23.3 ± 3.4 kg/m²). Spatiotemporal gait data were simultaneously determined using an instrumented treadmill (gaitway 3D) and a mobile gait analysis system (RehaGait). Besides VEL, bilateral (stride length [SL], cadence [CAD]) and unilateral (contact duration [CON], single [SS] and double support duration [DS]) outcomes were validated. RESULTS Across the three VEL investigated, the correlations between both measurement systems were almost perfect in SL and CAD (r > 0.97). In addition, SL significantly differed (p < 0.01) with moderate to large effects, whereby the root mean squared error (RMSE) did not exceed 1.8 cm. RMSE in CAD was not higher than 0.33 spm and statistically significant differences were only present at v1 (d = 0.63). DS was the most erroneous unilateral parameter with values for %RMSE ranging from 9% at v1 to 14% at v3. In CON and SS %RMSE was in a magnitude of 2-4% across all VEL. Furthermore, VEL affected measurement accuracy in unilateral outcomes with moderate to large effects (F (2, 45) > 6.0, p < 0.01, ηp2 > 0.11) with consistently higher differences at lower velocities. SIGNIFICANCE Based on the results presented the validity of the mobile gait analysis system investigated to detect gait asymmetries must be questioned.
Collapse
Affiliation(s)
- Lars Reinhardt
- Institute for Applied Training Science, Leipzig, Germany.
| | - René Schwesig
- Department of Orthopedic and Trauma Surgery, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Stephan Schulze
- Department of Orthopedic and Trauma Surgery, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Lars Donath
- Department of Intervention Research in Exercise Training, German Sport University Cologne, Cologne, Germany
| | - Eduard Kurz
- Department of Orthopedic and Trauma Surgery, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| |
Collapse
|
4
|
Han SL, Cai ML, Pan MC. Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects. SENSORS (BASEL, SWITZERLAND) 2024; 24:1519. [PMID: 38475055 DOI: 10.3390/s24051519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.
Collapse
Affiliation(s)
- Shao-Li Han
- Department of Mechanical Engineering, National Central University, Taoyuan 32001, Taiwan
- Department of Physical Medicine and Rehabilitation, Changhua Christian Hospital, Changhua 500209, Taiwan
| | - Meng-Lin Cai
- Department of Mechanical Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Min-Chun Pan
- Department of Mechanical Engineering, National Central University, Taoyuan 32001, Taiwan
| |
Collapse
|
5
|
Yoon DH, Kim JH, Lee K, Cho JS, Jang SH, Lee SU. Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study. Gait Posture 2024; 107:212-217. [PMID: 37863672 DOI: 10.1016/j.gaitpost.2023.10.006] [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: 01/01/2023] [Revised: 09/18/2023] [Accepted: 10/04/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Gait assessment has been used in a wide range of clinical applications, and gait velocity is also a leading predictor of disease and physical functional aspects in older adults. RESEARCH QUESTION The study aim to examine the changes in IMU-based gait parameters according to age in healthy adults aged 50 and older, to analyze differences between aging patients. METHODS A total of 296 healthy adults (65.32 ± 6.74 yrs; 83.10 % female) were recruited. Gait assessment was performed using an IMU sensor-based gait analysis system, and 3D motion information of hip and knee joints was obtained using magnetic sensors. The basic characteristics of the study sample were stratified by age category, and the baseline characteristics between the groups were compared using analysis of variance (ANOVA). Pearson's correlation analysis was used to analyze the relationship between age as the dependent variable and several measures of gait parameters and joint angles as independent variables. RESULTS The results of this study found that there were significant differences in gait velocity and both terminal double support in the three groups according to age, and statistically significant differences in the three groups in hip joint angle and knee joints angle. In addition, it was found that the gait velocity and knee/hip joint angle changed with age, and the gait velocity and knee/hip joint angle were also different in the elderly and adult groups. CONCLUSIONS We found changes in gait parameters and joint angles according to age in healthy adults and older adults and confirmed the difference in gait velocity and joint angles between adults and older adults.
Collapse
Affiliation(s)
- Dong Hyun Yoon
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, South Korea; Institute on Aging, Seoul National University, Seoul, South Korea
| | - Jeong-Hyun Kim
- Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea
| | - Kyuwon Lee
- Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea
| | - Jae-Sung Cho
- Korea Orthopedics & Rehabilitation Engineering Center, Incheon, South Korea
| | - Seong-Ho Jang
- Department of Rehabilitation Medicine, Hanyang University College of Medicine, Gyeonggi-do, South Korea
| | - Shi-Uk Lee
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, South Korea; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
| |
Collapse
|
6
|
Prigent G, Aminian K, Cereatti A, Salis F, Bonci T, Scott K, Mazzà C, Alcock L, Del Din S, Gazit E, Hansen C, Paraschiv-Ionescu A. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings. Med Biol Eng Comput 2023; 61:2341-2352. [PMID: 37069465 PMCID: PMC10412496 DOI: 10.1007/s11517-023-02826-x] [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: 09/26/2022] [Accepted: 03/08/2023] [Indexed: 04/19/2023]
Abstract
Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.
Collapse
Affiliation(s)
- Gaëlle Prigent
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - for the Mobilise-D consortium
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| |
Collapse
|
7
|
Vezočnik M, Juric MB. Adaptive Inertial Sensor-Based Step Length Estimation Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:9452. [PMID: 36502153 PMCID: PMC9739942 DOI: 10.3390/s22239452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.
Collapse
|
8
|
Yang Z, Tran LC, Safaei F, Le AT, Taparugssanagorn A. Real-Time Step Length Estimation in Indoor and Outdoor Scenarios. SENSORS (BASEL, SWITZERLAND) 2022; 22:8472. [PMID: 36366171 PMCID: PMC9656841 DOI: 10.3390/s22218472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is "sliding" forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.
Collapse
Affiliation(s)
- Zanru Yang
- School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Le Chung Tran
- School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Farzad Safaei
- School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Anh Tuyen Le
- School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
| | | |
Collapse
|
9
|
Lee HJ, Park JS, Bae JB, Han JW, Kim KW. Development of a gait speed estimation model for healthy older adults using a single inertial measurement unit. PLoS One 2022; 17:e0275612. [PMID: 36201497 PMCID: PMC9536628 DOI: 10.1371/journal.pone.0275612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Although gait speed changes are associated with various geriatric conditions, standard gait analysis systems, such as laboratory-based motion capture systems or instrumented walkways, are too expensive, spatially limited, and difficult to access. A wearable inertia sensor is cheap and easy to access; however, its accuracy in estimating gait speed is limited. In this study, we developed a model for accurately estimating the gait speed of healthy older adults using the data captured by an inertia sensor placed at their center of body mass (CoM). We enrolled 759 healthy older adults from two population-based cohort studies and asked them to walk on a 14 m long walkway thrice at comfortable paces with an inertia sensor attached to their CoM. In the middle of the walkway, we placed GAITRite™ to obtain the gold standard of gait speed. We then divided the participants into three subgroups using the normalized step length and developed a linear regression model for estimating the gold standard gait speed using age, foot length, and the features obtained from an inertia sensor, including cadence, vertical height displacement, yaw angle, and role angle of CoM. Our model exhibited excellent accuracy in estimating the gold standard gait speed (mean absolute error = 3.74%; root mean square error = 5.30 cm/s; intraclass correlation coefficient = 0.954). Our model may contribute to the early detection and monitoring of gait disorders and other geriatric conditions by making gait assessment easier, cheaper, and more ambulatory while remaining as accurate as other standard gait analysis systems.
Collapse
Affiliation(s)
- Hyang Jun Lee
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji Sun Park
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Korea
| | - Jong Bin Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Korea
- Department of Health Science and Technology, Seoul National University Graduate School of Convergence Science and Technology, Suwon, Korea
- * E-mail:
| |
Collapse
|
10
|
Lee YJ, Wei MY, Chen YJ. Multiple inertial measurement unit combination and location for recognizing general, fatigue, and simulated-fatigue gait. Gait Posture 2022; 96:330-337. [PMID: 35785657 DOI: 10.1016/j.gaitpost.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/23/2022] [Accepted: 06/26/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Muscle fatigue of the lower limbs results in dynamic imbalance and gait instability, increasing the risk of falling. However, people might slow walk without physical muscle fatigue due to mental fatigue. Wearable inertial measurement units (IMU) and machine learning approaches have been well employed for recognizing human activities. RESEARCH QUESTION The study aims to use a machine learning technique to recognize the data collected from IMUs for physically fatigued or slow-walking gaits. Second, the study aims to reveal the location or the number of IMUs can have the best performance. METHODS Sixteen healthy adults with six IMUs attached to their heels, toes, sacrum, and head participated in the experiment. On the first day, the participants were instructed to walk along a hallway before and after the fatigue protocol as the Pre- and Post-fatigue gait. On the second day, the participants were instructed to walk along a hallway following the beat of their fatigue gait cadence measured on the first day as the simulated cadence (SC) gait. Gait cycles of each condition were segmented as the inputs of the Long Short-Term Memory (LSTM) model for recognization. RESULTS The result revealed that the LSTM model could recognize the gait of simulated cadence with the highest accuracy among these three gaits. For the signal body part, the highest accuracy was 93.20 % observed at the IMUs of toes. For the best combination, the IMUs of toes and sacrum achieved the highest accuracy of 95.71 %. SIGNIFICANCE The machine learning technique of LSTM with one or more IMUs can recognize the gait under normal, physical fatigue, or simulated cadence without muscle fatigue. Our model and approach would be expected to provide conditional warning in multiple fields, such as industrial safety for potential applications.
Collapse
Affiliation(s)
- Yun-Ju Lee
- Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Hsinchu, Taiwan.
| | - Ming-Yi Wei
- Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Hsinchu, Taiwan
| | - Yu-Jung Chen
- Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Hsinchu, Taiwan
| |
Collapse
|
11
|
Yang Z, Tran LC, Safaei F. Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios. SENSORS 2022; 22:s22041640. [PMID: 35214542 PMCID: PMC8878979 DOI: 10.3390/s22041640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/14/2022] [Accepted: 02/16/2022] [Indexed: 02/04/2023]
Abstract
In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.
Collapse
|
12
|
Hiser S, Urbanek J, Young DL, McLaughlin KH, Colantuoni E, Brotman DJ, Needham DM, Hoyer E. Improving patient selection for use of consumer grade physical activity monitors in the hospital. Braz J Phys Ther 2022; 26:100447. [PMID: 36183578 PMCID: PMC9530475 DOI: 10.1016/j.bjpt.2022.100447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Bedrest is toxic for inpatients and consumer grade physical activity monitors offer an economical solution to monitor patient ambulation. But these devices may not be accurate in debilitated hospitalized patients who frequently ambulate very slowly. OBJECTIVE To determine whether measures of physical capacity can help identify inpatients for whom wearable physical activity monitors may accurately measure step count. METHODS Prospective observational study of 54 adult inpatients with acute neurological diagnoses. Patients were assessed using 2 physical capacity assessments (Activity Measure for Post-Acute Care Inpatient Mobility Short Form [AM-PAC IMSF] and Katz Activities of Daily Living [ADL] scale). They also completed a 2-minute walk test (2MWT) wearing a consumer grade physical activity monitor. RESULTS The wearable activity monitor recorded steps (initiated) in 33 (61%) of the inpatients, and for 94% of inpatients with gait speeds >0.43 m/s. Physical capacity assessments correlated well with gait speed, AM-PAC IMSF r = 0.7, and Katz ADL r = 0.6, p < 0.05. When the physical activity monitor initiated, the mean absolute percent error (SD) comparing device calculated steps to observed steps, was 10% (13). AM-PAC IMSF (T-score >45) and Katz ADL (>5) cutoff scores identified inpatients for whom physical activity monitors initiated with a sensitivity of 94 and 91%, respectively. CONCLUSIONS Physical capacity assessments, such as AM-PAC, and Katz ADL, may be a useful and feasible screening strategy to help identify inpatients where wearable physical activity monitors can measure their mobility.
Collapse
Affiliation(s)
- Stephanie Hiser
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacek Urbanek
- Department of Medicine, Division of Geriatric Medicine and Gerontology, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel L. Young
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Department of Physical Therapy, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Kevin H. McLaughlin
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth Colantuoni
- Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel J Brotman
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Dale M. Needham
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Erik Hoyer
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
13
|
Mobbs RJ, Perring J, Raj SM, Maharaj M, Yoong NKM, Sy LW, Fonseka RD, Natarajan P, Choy WJ. Gait metrics analysis utilizing single-point inertial measurement units: a systematic review. Mhealth 2022; 8:9. [PMID: 35178440 PMCID: PMC8800203 DOI: 10.21037/mhealth-21-17] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/27/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors. RESULTS A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls. DISCUSSION This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.
Collapse
Affiliation(s)
- Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia
| | - Jordan Perring
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | | | - Monish Maharaj
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Nicole Kah Mun Yoong
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Luke Wicent Sy
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| | - Rannulu Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| |
Collapse
|
14
|
Scott K, Bonci T, Alcock L, Buckley E, Hansen C, Gazit E, Schwickert L, Cereatti A, Mazzà C. A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems. SENSORS 2021; 21:s21248223. [PMID: 34960317 PMCID: PMC8703700 DOI: 10.3390/s21248223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/16/2022]
Abstract
Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.
Collapse
Affiliation(s)
- Kirsty Scott
- Department of Mechanical Engineering & INSIGNEO Institute of In Silico Medicine, The University of Sheffield, Sheffield S1 3JD, UK; (T.B.); (E.B.); (C.M.)
- Correspondence:
| | - Tecla Bonci
- Department of Mechanical Engineering & INSIGNEO Institute of In Silico Medicine, The University of Sheffield, Sheffield S1 3JD, UK; (T.B.); (E.B.); (C.M.)
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK;
| | - Ellen Buckley
- Department of Mechanical Engineering & INSIGNEO Institute of In Silico Medicine, The University of Sheffield, Sheffield S1 3JD, UK; (T.B.); (E.B.); (C.M.)
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, 24105 Kiel, Germany;
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Centre, Tel Aviv 6492416, Israel;
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, 70376 Stuttgart, Germany;
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Claudia Mazzà
- Department of Mechanical Engineering & INSIGNEO Institute of In Silico Medicine, The University of Sheffield, Sheffield S1 3JD, UK; (T.B.); (E.B.); (C.M.)
| | | |
Collapse
|
15
|
Mazzà C, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Brozgol M, Buckley E, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, Chynkiamis N, Ciravegna F, Del Din S, Eskofier B, Evers J, Garcia Aymerich J, Gazit E, Hansen C, Hausdorff JM, Helbostad JL, Hiden H, Hume E, Paraschiv-Ionescu A, Ireson N, Keogh A, Kirk C, Kluge F, Koch S, Küderle A, Lanfranchi V, Maetzler W, Micó-Amigo ME, Mueller A, Neatrour I, Niessen M, Palmerini L, Pluimgraaff L, Reggi L, Salis F, Schwickert L, Scott K, Sharrack B, Sillen H, Singleton D, Soltani A, Taraldsen K, Ullrich M, Van Gelder L, Vereijken B, Vogiatzis I, Warmerdam E, Yarnall A, Rochester L. Technical validation of real-world monitoring of gait: a multicentric observational study. BMJ Open 2021; 11:e050785. [PMID: 34857567 PMCID: PMC8640671 DOI: 10.1136/bmjopen-2021-050785] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. METHODS AND ANALYSIS This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. ETHICS AND DISSEMINATION The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. TRIAL REGISTRATION NUMBER ISRCTN (12246987).
Collapse
Affiliation(s)
- Claudia Mazzà
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Tecla Bonci
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Marina Brozgol
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ellen Buckley
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Anne-Elie Carsin
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Marco Caruso
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
- PolitoBIOMed Lab - Biomedical Engineering Lab, Politecnico di Torino, Torino, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Fabio Ciravegna
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jordi Evers
- McRoberts BV, Den Haag, Zuid-Holland, Netherlands
| | - Judith Garcia Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jorunn L Helbostad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Neil Ireson
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Vitaveska Lanfranchi
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | | | - Luca Reggi
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Henrik Sillen
- Digital Health R&D, AstraZeneca Sweden, Sodertalje, Sweden
| | - David Singleton
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazi Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Van Gelder
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Elke Warmerdam
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| |
Collapse
|
16
|
Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults. SENSORS 2021; 21:s21206831. [PMID: 34696041 PMCID: PMC8540088 DOI: 10.3390/s21206831] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 11/17/2022]
Abstract
The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants' TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults' fall risks remotely through the evaluation of the TUG score during their daily walking.
Collapse
|
17
|
Soltani A, Aminian K, Mazza C, Cereatti A, Palmerini L, Bonci T, Paraschiv-Ionescu A. Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1955-1964. [PMID: 34506286 DOI: 10.1109/tnsre.2021.3111681] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively.
Collapse
|
18
|
Vidyarani K, Talasila V, Megharjun N, Supriya M, Ravi Prasad K, Prashanth G. An inertial sensing mechanism for measuring gait parameters and gait energy expenditure. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
|
19
|
Mallat R, Bonnet V, Dumas R, Adjel M, Venture G, Khalil M, Mohammed S. Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1300-1311. [PMID: 34138711 DOI: 10.1109/tnsre.2021.3089873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study investigates the possibility of estimating lower-limb joint kinematics and meaningful performance indexes for physiotherapists, during gait on a treadmill based on data collected from a sparse placement of new Visual Inertial Measurement Units (VIMU) and the use of an Extended Kalman Filter (EKF). The proposed EKF takes advantage of the biomechanics of the human body and of the investigated task to reduce sensor inaccuracies. Two state-vector formulations, one based on the use of constant acceleration model and one based on Fourier series, and the tuning of their corresponding parameters were analyzed. The constant acceleration model, due to its inherent inconsistency for human motion, required a cumbersome optimisation process and needed the a-priori knowledge of reference joint trajectories for EKF parameters tuning. On the other hand, the Fourier series formulation could be used without a specific parameters tuning process. In both cases, the average root mean square difference and correlation coefficient between the estimated joint angles and those reconstructed with a reference stereophotogrammetric system was 3.5deg and 0.70, respectively. Moreover, the stride lengths were estimated with a normalized root mean square difference inferior to 2% when using the forward kinematics model receiving as input the estimated joint angles. The popular gait deviation index was also estimated and showed similar results very close to 100, using both the proposed method and the reference stereophotogrammetric system. Such consistency was obtained using only three wireless and affordable VIMU located at the pelvis and both heels and tracked using two affordable RGB cameras. Being further easy-to-use and suitable for applications taking place outside of the laboratory, the proposed method thus represents a good compromise between accurate reference stereophotogrammetric systems and markerless ones for which accuracy is still under debate.
Collapse
|
20
|
Vezočnik M, Kamnik R, Juric MB. Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis. SENSORS 2021; 21:s21103527. [PMID: 34069414 PMCID: PMC8159098 DOI: 10.3390/s21103527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/07/2021] [Accepted: 05/14/2021] [Indexed: 11/16/2022]
Abstract
Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.
Collapse
Affiliation(s)
- Melanija Vezočnik
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000 Ljubljana, Slovenia;
- Correspondence:
| | - Roman Kamnik
- Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000 Ljubljana, Slovenia;
| | - Matjaz B. Juric
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000 Ljubljana, Slovenia;
| |
Collapse
|
21
|
Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke. ACTA ACUST UNITED AC 2021; 2. [PMID: 34396094 PMCID: PMC8360352 DOI: 10.1017/wtc.2020.11] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, and task-specific gait training is instrumental for effective gait rehabilitation, characteristics that our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision and frequency to detect subtle changes in intervention efficacy during both conventional and exosuit-assisted gait training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors to reconstruct three clinically meaningful gait metrics related to circumduction, foot clearance, and stride length. Our method corrects sensor drift using instantaneous information from both sides of the body. This approach makes our method robust to irregular walking conditions poststroke as well as usable in real-time applications, such as real-time movement monitoring, exosuit assistance control, and biofeedback. We validate our algorithm in eight people poststroke in comparison to lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) for circumduction, −0.6 cm (−3.5%) for foot clearance, and 3.8 cm (3.6%) for stride length. A single-participant case study shows our technique’s promise in daily-living environments by detecting exosuit-induced changes in gait while walking in a busy outdoor plaza.
Collapse
|
22
|
Powell D, Celik Y, Trojaniello D, Young F, Moore J, Stuart S, Godfrey A. Instrumenting traditional approaches to physical assessment. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00005-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
23
|
An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment. SENSORS 2020; 20:s20226509. [PMID: 33202608 PMCID: PMC7696193 DOI: 10.3390/s20226509] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 01/11/2023]
Abstract
Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.
Collapse
|
24
|
Celik Y, Stuart S, Woo WL, Godfrey A. Gait analysis in neurological populations: Progression in the use of wearables. Med Eng Phys 2020; 87:9-29. [PMID: 33461679 DOI: 10.1016/j.medengphy.2020.11.005] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/02/2020] [Accepted: 11/11/2020] [Indexed: 12/19/2022]
Abstract
Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.
Collapse
Affiliation(s)
- Y Celik
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - S Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - W L Woo
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
| |
Collapse
|
25
|
Digo E, Pierro G, Pastorelli S, Gastaldi L. Evaluation of spinal posture during gait with inertial measurement units. Proc Inst Mech Eng H 2020; 234:1094-1105. [PMID: 32633209 DOI: 10.1177/0954411920940830] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt-twist method was adopted to evaluate flexion-extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.
Collapse
Affiliation(s)
- Elisa Digo
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Giuseppina Pierro
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Stefano Pastorelli
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Laura Gastaldi
- Department of Mathematical Sciences "G.L. Lagrange," Politecnico di Torino, Torino, Italy
| |
Collapse
|
26
|
State of the Field of waist-mounted sensor algorithm for gait events detection: A scoping review. Gait Posture 2020; 79:152-161. [PMID: 32408039 DOI: 10.1016/j.gaitpost.2020.03.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 03/28/2020] [Accepted: 03/31/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait. RESEARCH QUESTION To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults. METHODS A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner. RESULTS In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation. SIGNIFICANCE There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice.
Collapse
|
27
|
Kobsar D, Charlton JM, Tse CTF, Esculier JF, Graffos A, Krowchuk NM, Thatcher D, Hunt MA. Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis. J Neuroeng Rehabil 2020; 17:62. [PMID: 32393301 PMCID: PMC7216606 DOI: 10.1186/s12984-020-00685-3] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 04/07/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults. METHODS Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed. RESULTS A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research. CONCLUSIONS The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2-95) being the primary limitation.
Collapse
Affiliation(s)
- Dylan Kobsar
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada.,Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada
| | - Jesse M Charlton
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada.,Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Calvin T F Tse
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada.,Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Francois Esculier
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada.,Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.,The Running Clinic, Lac Beauport, QC, Canada
| | - Angelo Graffos
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada.,Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Natasha M Krowchuk
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Thatcher
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada
| | - Michael A Hunt
- Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada. .,Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
28
|
Byun S, Lee HJ, Han JW, Kim JS, Choi E, Kim KW. Walking-speed estimation using a single inertial measurement unit for the older adults. PLoS One 2019; 14:e0227075. [PMID: 31877181 PMCID: PMC6932800 DOI: 10.1371/journal.pone.0227075] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 12/10/2019] [Indexed: 11/19/2022] Open
Abstract
Background Although walking speed is associated with important clinical outcomes and designated as the sixth vital sign of the elderly, few walking-speed estimation algorithms using an inertial measurement unit (IMU) have been derived and tested in the older adults, especially in the elderly with slow speed. We aimed to develop a walking-speed estimation algorithm for older adults based on an IMU. Methods We used data from 659 of 785 elderly enrolled from the cohort study. We measured gait using an IMU attached on the lower back while participants walked around a 28 m long round walkway thrice at comfortable paces. Best-fit linear regression models were developed using selected demographic, anthropometric, and IMU features to estimate the walking speed. The accuracy of the algorithm was verified using mean absolute error (MAE) and root mean square error (RMSE) in an independent validation set. Additionally, we verified concurrent validity with GAITRite using intraclass correlation coefficients (ICCs). Results The proposed algorithm incorporates the age, sex, foot length, vertical displacement, cadence, and step-time variability obtained from an IMU sensor. It exhibited high estimation accuracy for the walking speed of the elderly and remarkable concurrent validity compared to the GAITRite (MAE = 4.70%, RMSE = 6.81 𝑐𝑚/𝑠, concurrent validity (ICC (3,1)) = 0.937). Moreover, it achieved high estimation accuracy even for slow walking by applying a slow-speed-specific regression model sequentially after estimation by a general regression model. The accuracy was higher than those obtained with models based on the human gait model with or without calibration to fit the population. Conclusions The developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.
Collapse
Affiliation(s)
- Seonjeong Byun
- Department of Psychiatry, Seoul National University, College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, National Medical Center, Seoul, Korea
| | - Hyang Jun Lee
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jun Sung Kim
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Korea
| | - Euna Choi
- Korean National Institute of Dementia, Seongnam, Korea
| | - Ki Woong Kim
- Department of Psychiatry, Seoul National University, College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Korea
- Korean National Institute of Dementia, Seongnam, Korea
- * E-mail:
| |
Collapse
|
29
|
Simonetti E, Villa C, Bascou J, Vannozzi G, Bergamini E, Pillet H. Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study. Med Biol Eng Comput 2019; 58:461-470. [PMID: 31873834 DOI: 10.1007/s11517-019-02098-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/10/2019] [Indexed: 11/27/2022]
Abstract
In recent years, inertial measurement units (IMUs) have been proposed as an alternative to force platforms and pressure sensors for gait events (i.e., initial and final contacts) detection. While multiple algorithms have been developed, the impact of gait event timing errors on temporal parameters and asymmetry has never been investigated in people with transfemoral amputation walking freely on level ground. In this study, five algorithms were comparatively assessed on gait data of seven people with transfemoral amputation, equipped with three IMUs mounted at the pelvis and both shanks, using pressure insoles for reference. Algorithms' performance was first quantified in terms of gait event detection rate (sensitivity, positive predictive value). Only two algorithms, based on shank mounted IMUs, achieved an acceptable detection rate (positive predictive value > 99%). For these two, accuracy of gait events timings, temporal parameters, and absolute symmetry index of stance-phase duration (SPD-ASI) were assessed. Whereas both algorithms achieved high accuracy for stride duration estimates (median errors: 0%, interquartile ranges < 1.75%), lower accuracy was found for other temporal parameters due to relatively high errors in the detection of final contact events. Furthermore, SPD-ASI derived from IMU-based algorithms proved to be significantly different to that obtained from insoles data. Graphical abstract Gait event detection with IMU in people with transfemoral amputation: initial contact (IC) and final contact (FC) events at the sound (s) and prosthetic (p) side are identified. Five algorithms were implemented using either shank-mounted or pelvis-mounted IMUs. Gait events were used to estimate temporal parameters (stride duration, stance phase duration [SPD], and double support time) and SPD asymmetry.
Collapse
Affiliation(s)
- Emeline Simonetti
- Institution nationale des Invalides (INI)/CERAH, 47 rue de l'Echat, 94000, Créteil, France.
- Arts et Métiers, Institut de Biomécanique Humaine Georges Charpak, 151 boulevard de l'Hôpital, 75013, Paris, France.
- Department of Movement, Human and Health Sciences, Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome "Foro Italico", Piazza Lauro de Bosis, 6, 00135, Rome, Italy.
| | - Coralie Villa
- Institution nationale des Invalides (INI)/CERAH, 47 rue de l'Echat, 94000, Créteil, France
- Arts et Métiers, Institut de Biomécanique Humaine Georges Charpak, 151 boulevard de l'Hôpital, 75013, Paris, France
| | - Joseph Bascou
- Institution nationale des Invalides (INI)/CERAH, 47 rue de l'Echat, 94000, Créteil, France
- Arts et Métiers, Institut de Biomécanique Humaine Georges Charpak, 151 boulevard de l'Hôpital, 75013, Paris, France
| | - Giuseppe Vannozzi
- Department of Movement, Human and Health Sciences, Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome "Foro Italico", Piazza Lauro de Bosis, 6, 00135, Rome, Italy
| | - Elena Bergamini
- Department of Movement, Human and Health Sciences, Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome "Foro Italico", Piazza Lauro de Bosis, 6, 00135, Rome, Italy
| | - Hélène Pillet
- Arts et Métiers, Institut de Biomécanique Humaine Georges Charpak, 151 boulevard de l'Hôpital, 75013, Paris, France
| |
Collapse
|
30
|
Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features. SENSORS 2019; 19:s19225006. [PMID: 31744141 PMCID: PMC6891461 DOI: 10.3390/s19225006] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/14/2019] [Accepted: 11/14/2019] [Indexed: 11/17/2022]
Abstract
Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.
Collapse
|
31
|
Flood MW, O'Callaghan BPF, Lowery MM. Gait Event Detection From Accelerometry Using the Teager-Kaiser Energy Operator. IEEE Trans Biomed Eng 2019; 67:658-666. [PMID: 31150328 DOI: 10.1109/tbme.2019.2919394] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments. METHODS A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision, and F1-score values. RESULTS The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasiuniform random distributions and lower accuracy. CONCLUSION Compared with the established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection. SIGNIFICANCE The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.
Collapse
|
32
|
Wang W, Adamczyk PG. Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1925. [PMID: 31022889 PMCID: PMC6515355 DOI: 10.3390/s19081925] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 04/17/2019] [Accepted: 04/22/2019] [Indexed: 11/22/2022]
Abstract
Assessing interventions for mobility disorders using real-life movement remains an unsolved problem. We propose a new method combining the strengths of traditional laboratory studies where environment is strictly controlled, and field-based studies where subjects behave naturally. We use a foot-mounted inertial sensor, a GPS receiver and a barometric altitude sensor to reconstruct a subject's path and detailed foot movement, both indoors and outdoors, during days-long measurement using strapdown navigation and sensor fusion algorithms. We cluster repeated movement paths based on location, and propose that on these paths, most environmental and behavioral factors (e.g., terrain and motivation) are as repeatable as in a laboratory. During each bout of movement along a frequently repeated path, any synchronized measurement can be isolated for study, enabling focused statistical comparison of different interventions. We conducted a 10-day test on one subject wearing athletic shoes and sandals each for five days. The algorithm detected four frequently-repeated straight walking paths with at least 300 total steps and repetitions on at least three days for each condition. Results on these frequently-repeated paths indicated significantly lower foot clearance and shorter stride length and a trend toward decreased stride width when wearing athletic shoes vs. sandals. Comparisons based on all straight walking were similar, showing greater statistical power, but higher variability in the data. The proposed method offers a new way to evaluate how mobility interventions affect everyday movement behavior.
Collapse
Affiliation(s)
- Weixin Wang
- Department of Mechanical Engineering, University of Wisconsin⁻Madison, Madison, WI 53706, USA.
| | - Peter Gabriel Adamczyk
- Department of Mechanical Engineering, University of Wisconsin⁻Madison, Madison, WI 53706, USA.
| |
Collapse
|
33
|
Jheng YC, Yu CH, Chen PY, Cheng YY, Lin TC, Huang SE, Liu DH, Wang CC, Wei SH, Kao CL. Establishment of vestibular function multimodality platform. J Chin Med Assoc 2019; 82:328-334. [PMID: 30946211 DOI: 10.1097/jcma.0000000000000065] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The technology of using inertial measurement units (IMUs) to detect motions in different body segments has drawn enormous attention to research and industry. In our previous research, we have applied IMUs in evaluating and treating patients with vestibular hypofunction. Furthermore, according to the research, when a person's head rotates over 60° on either side in the horizontal plane, and desires to focus vision on any targets, then the function of gaze shift comes in to operation. Herein, we aimed to use IMUs to build up a system to evaluate vestibular ocular reflex (VOR) during gaze shifting maneuver. METHODS In this study, we developed a platform, which combines the features of gaze shift and computerized dynamic visual acuity (cDVA), called the gaze shift DVA (gsDVA) platform. The gsDVA platform measures the orientations of the subject's head by IMU, and executed the evaluation according to the algorithm that was developed by us. Finally, we used the VICON system to validate the performance of gsDVA platform. RESULTS The performance of the accuracy was 2.41° ± 1.08°, the maximal sensor error was within 4.25°, and highly correlated between our platform and VICON (p < 0.05, R = 0.99). The intraclass correlation coefficient (ICC) of between-day and within-day was 0.984 and 0.999, respectively. Furthermore, the platform not only executed the evaluation automatically but also recorded other information besides the head orientation, such as rotation speed, rotation time, reaction time, and visual acuity. CONCLUSION In this study, we demonstrated the utility of vestibular evaluation, and this platform can help to clarify the relationship between gaze shift and VOR. This methodology is useful and can be applied efficiently to different disease groups for interactive evaluation and rehabilitation programs.
Collapse
Affiliation(s)
- Ying-Chun Jheng
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Chung-Huang Yu
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Po-Yin Chen
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Yuan-Yang Cheng
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Tai-Chi Lin
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Shih-En Huang
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
- Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Ding-Hao Liu
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Chien-Chih Wang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
- Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Shun-Hwa Wei
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Chung-Lan Kao
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
- Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| |
Collapse
|
34
|
Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters. SENSORS 2018; 19:s19010038. [PMID: 30583508 PMCID: PMC6339047 DOI: 10.3390/s19010038] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 12/14/2018] [Accepted: 12/19/2018] [Indexed: 11/18/2022]
Abstract
The aim of this study was to assess the validity and test-retest reliability of an inertial measurement unit (IMU) system for gait analysis. Twenty-four healthy subjects conducted a 6-min walking test and were instrumented with seven IMUs and retroreflective markers. A kinematic approach was used to estimate the initial and terminal contact events in real-time. Based on these events twelve spatio-temporal parameters (STP) were calculated. A marker based optical motion capture (OMC) system provided the reference. Event-detection rate was about 99%. Detection offset was below 0.017 s. Relative root mean square error (RMSE) ranged from 0.90% to 4.40% for most parameters. However, the parameters that require spatial information of both feet showed higher errors. Step length showed a relative RMSE of 6.69%. Step width and swing width revealed the highest relative RMSE (34.34% and 35.20%). Test-retest results ranged from 0.67 to 0.92, except for the step width (0.25). Summarizing, it appears that the parameters describing the lateral distance between the feet need further improvement. However, the results of the validity and reliability of the IMU system encourage its validation in clinical settings as well as further research.
Collapse
|
35
|
Accelerometry-Based Distance Estimation for Ambulatory Human Motion Analysis. SENSORS 2018; 18:s18124441. [PMID: 30558312 PMCID: PMC6308986 DOI: 10.3390/s18124441] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/12/2018] [Accepted: 12/13/2018] [Indexed: 11/16/2022]
Abstract
In human motion science, accelerometers are used as linear distance sensors by attaching them to moving body parts, with their measurement axes its measurement axis aligned in the direction of motion. When double integrating the raw sensor data, multiple error sources are also integrated integrated as well, producing inaccuracies in the final position estimation which increases fast with the integration time. In this paper, we make a systematic and experimental comparison of different methods for position estimation, with different sensors and in different motion conditions. The objective is to correlate practical factors that appear in real applications, such as motion mean velocity, path length, calibration method, or accelerometer noise level, with the quality of the estimation. The results confirm that it is possible to use accelerometers to estimate short linear displacements of the body with a typical error of around 4.5% in the general conditions tested in this study. However, they also show that the motion kinematic conditions can be a key factor in the performance of this estimation, as the dynamic response of the accelerometer can affect the final results. The study lays out the basis for a better design of distance estimations, which are useful in a wide range of ambulatory human motion monitoring applications.
Collapse
|
36
|
Template-Based Step Detection with Inertial Measurement Units. SENSORS 2018; 18:s18114033. [PMID: 30463240 PMCID: PMC6263402 DOI: 10.3390/s18114033] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 11/07/2018] [Accepted: 11/16/2018] [Indexed: 11/16/2022]
Abstract
This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.
Collapse
|
37
|
Avvenuti M, Carbonaro N, Cimino MGCA, Cola G, Tognetti A, Vaglini G. Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases. SENSORS 2018; 18:s18113811. [PMID: 30405020 PMCID: PMC6263943 DOI: 10.3390/s18113811] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/29/2018] [Accepted: 11/03/2018] [Indexed: 02/07/2023]
Abstract
Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.
Collapse
Affiliation(s)
- Marco Avvenuti
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
| | - Nicola Carbonaro
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
- Research Center "E. Piaggio", University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy.
| | - Mario G C A Cimino
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
| | - Guglielmo Cola
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
| | - Alessandro Tognetti
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
- Research Center "E. Piaggio", University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy.
| | - Gigliola Vaglini
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lazzarino 1, 56122 Pisa, Italy.
| |
Collapse
|
38
|
Petraglia F, Scarcella L, Pedrazzi G, Brancato L, Puers R, Costantino C. Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. Eur J Phys Rehabil Med 2018; 55:265-280. [PMID: 30311493 DOI: 10.23736/s1973-9087.18.05306-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
INTRODUCTION The increasing popularity of inertial sensors in clinical practice is not supported by precise information on their reliability or guidelines for their use in rehabilitation. The authors investigated the state of the literature concerning the use of inertial sensors for gait analysis in both healthy and pathological adults comparing traditional systems. Furthermore, trying to define directions for clinicians. EVIDENCE ACQUISITION In accordance with the PRISMA statement, authors searched in PubMed, Web of Science and Scopus all paper published from January 1st, 2005 until December 31st, 2017. They included both healthy and pathological adults' subjects as population, wearable or inertial sensors used for gait analysis and compared with classical gait analysis performed in a Motion Lab as intervention and comparison, gait parameters as outcomes. Considering the methodological quality, authors focused on: sample; description of the study; type of gait analysis used for comparison; type of sensor; sensor placement on the body; gait task requested. EVIDENCE SYNTHESIS From a total of 888 articles, 16 manuscripts were selected and 7 of them were considered for meta-analysis for different gait parameters. Demographic data, tested devices, reference systems, test procedures and outcomes were analyzed. CONCLUSIONS Our results show a good agreement between inertial sensors and classical gait analysis for some gait parameters, supporting their use as a solution for capturing kinematic information over an extended space and time and even outside a laboratory in real-life conditions. Authors can support the use of portable inertial sensors for a practical gait analysis in clinical setting with good reliability. It will then be the experience of the clinician to direct the decision-making process.
Collapse
Affiliation(s)
| | - Luca Scarcella
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Giuseppe Pedrazzi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | | | - Cosimo Costantino
- Department of Medicine and Surgery, University of Parma, Parma, Italy -
| |
Collapse
|
39
|
Bertoli M, Cereatti A, Trojaniello D, Avanzino L, Pelosin E, Del Din S, Rochester L, Ginis P, Bekkers EMJ, Mirelman A, Hausdorff JM, Della Croce U. Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. Biomed Eng Online 2018; 17:58. [PMID: 29739456 PMCID: PMC5941594 DOI: 10.1186/s12938-018-0488-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 04/23/2018] [Indexed: 11/11/2022] Open
Abstract
Background The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat. Results Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were − 27 to 27 ms for Stride Time, − 68 to 44 ms for Stance Time, − 31 to 31 ms for Step Time and − 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (≈ 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds. Conclusions The TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the “SIAMOC Best Methodological Paper 2017”.
Collapse
Affiliation(s)
- Matilde Bertoli
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | | | - Laura Avanzino
- Department of Experimental Medicine, Section of Human Physiology and Centro Polifunzionale di Scienze Motorie, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genoa, Genoa, Italy
| | - Silvia Del Din
- Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle, UK
| | - Lynn Rochester
- Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle, UK.,Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK
| | - Pieter Ginis
- Department of Rehabilitation Sciences, Neuromotor Rehabilitation Research Group, KU Leuven, Louvain, Belgium
| | - Esther M J Bekkers
- Department of Rehabilitation Sciences, Neuromotor Rehabilitation Research Group, KU Leuven, Louvain, Belgium.,Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Parkinson Centre Nijmegen, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Anat Mirelman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Tel Aviv, Israel
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy. .,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy.
| |
Collapse
|
40
|
Jarchi D, Pope J, Lee TKM, Tamjidi L, Mirzaei A, Sanei S. A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications. IEEE Rev Biomed Eng 2018; 11:177-194. [PMID: 29994786 DOI: 10.1109/rbme.2018.2807182] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.
Collapse
|
41
|
A practical step length algorithm using lower limb angular velocities. J Biomech 2017; 66:137-144. [PMID: 29198369 DOI: 10.1016/j.jbiomech.2017.11.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 09/14/2017] [Accepted: 11/09/2017] [Indexed: 11/24/2022]
Abstract
The use of Inertial Measurement Units (IMUs) for spatial gait analysis has opened the door to unconstrained measurements within the home and community. Bandwidth, cost limitations, and ease of use has historically restricted the number and location of sensors worn on the body. In this paper, we describe a four-sensor configuration of IMUs placed on the shanks and thighs that is sufficient to provide an accurate measure of temporal gait parameters, spatial gait parameters, and joint angle dynamics during ambulation. Estimating spatial gait parameters solely from gyroscope data is preferred because gyroscopes are less susceptible to sensor noise and a system comprised of only gyroscopes uses decreased bandwidth compared to a typical 9 degree-of-freedom IMU. The purpose of this study was to determine the validity of a novel method of step length estimation using gyroscopes attached to the shanks and thighs. An Inverted Pendulum Model algorithm (IPM) was proposed to calculate step length, stride length, and gait speed. The algorithm incorporates heel-strike events and average forward velocity per step to make these assessments. IMU algorithm accuracy was determined via concurrent validity with an instrumented walkway and results explained via the collision model of gait. The IPM produced accurate estimates of step length, stride length, and gait speed with a mean difference of 3 cm and an RMSE of 6.6 cm for step length, thus establishing a new approach for spatial gait parameter calculation. The lack of numerical integration in IPM makes it well suited for use in continuous monitoring applications where sensor sampling rates are restricted.
Collapse
|
42
|
Pepa L, Verdini F, Spalazzi L. Gait parameter and event estimation using smartphones. Gait Posture 2017; 57:217-223. [PMID: 28667903 DOI: 10.1016/j.gaitpost.2017.06.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 06/13/2017] [Accepted: 06/15/2017] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well. METHODS This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects. RESULTS High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count. CONCLUSION This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
Collapse
Affiliation(s)
- Lucia Pepa
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
| | - Federica Verdini
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
| | - Luca Spalazzi
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
| |
Collapse
|
43
|
Song M, Kim J. An Ambulatory Gait Monitoring System with Activity Classification and Gait Parameter Calculation Based on a Single Foot Inertial Sensor. IEEE Trans Biomed Eng 2017; 65:885-893. [PMID: 28708542 DOI: 10.1109/tbme.2017.2724543] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
GOAL For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. METHOD The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. RESULTS The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. CONCLUSION The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.
Collapse
|
44
|
Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements. SENSORS 2016; 16:s16122206. [PMID: 28009854 PMCID: PMC5191184 DOI: 10.3390/s16122206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 12/14/2016] [Accepted: 12/19/2016] [Indexed: 11/17/2022]
Abstract
A novel approach for estimating the instantaneous velocity of the pelvis during walking was developed based on Inertial Measurement Units (IMUs). The instantaneous velocity was modeled by the sum of a cyclical component, decomposed in the Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP) directions, and the Average Progression Velocity (APV) over each gait cycle. The proposed method required the availability of two IMUs, attached to the pelvis and one shank. Gait cycles were identified from the shank angular velocity; for each cycle, the Fourier series coefficients of the pelvis and shank acceleration signals were computed. The cyclical component was estimated by Fourier-based time-integration of the pelvis acceleration. A Bayesian Linear Regression (BLR) with Automatic Relevance Determination (ARD) predicted the APV from the stride time, the stance duration, and the Fourier series coefficients of the shank acceleration. Healthy subjects performed tasks of Treadmill Walking (TW) and Overground Walking (OW), and an optical motion capture system (OMCS) was used as reference for algorithm performance assessment. The widths of the limits of agreements (±1.96 standard deviation) were computed between the proposed method and the reference OMCS, yielding, for the cyclical component in the different directions: ML: ±0.07 m/s (±0.10 m/s); VT: ±0.03 m/s (±0.05 m/s); AP: ±0.06 m/s (±0.10 m/s), in TW (OW) conditions. The ARD-BLR achieved an APV root mean square error of 0.06 m/s (0.07 m/s) in the same conditions.
Collapse
|
45
|
Hickey A, Del Din S, Rochester L, Godfrey A. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiol Meas 2016; 38:N1-N15. [PMID: 27941238 DOI: 10.1088/1361-6579/38/1/n1] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Research suggests wearables and not instrumented walkways are better suited to quantify gait outcomes in clinic and free-living environments, providing a more comprehensive overview of walking due to continuous monitoring. Numerous validation studies in controlled settings exist, but few have examined the validity of wearables and associated algorithms for identifying and quantifying step counts and walking bouts in uncontrolled (free-living) environments. Studies which have examined free-living step and bout count validity found limited agreement due to variations in walking speed, changing terrain or task. Here we present a gait segmentation algorithm to define free-living step count and walking bouts from an open-source, high-resolution, accelerometer-based wearable (AX3, Axivity). Ten healthy participants (20-33 years) wore two portable gait measurement systems; a wearable accelerometer on the lower-back and a wearable body-mounted camera (GoPro HERO) on the chest, for 1 h on two separate occasions (24 h apart) during free-living activities. Step count and walking bouts were derived for both measurement systems and compared. For all participants during a total of almost 20 h of uncontrolled and unscripted free-living activity data, excellent relative (rho ⩾ 0.941) and absolute (ICC(2,1) ⩾ 0.975) agreement with no presence of bias were identified for step count compared to the camera (gold standard reference). Walking bout identification showed excellent relative (rho ⩾ 0.909) and absolute agreement (ICC(2,1) ⩾ 0.941) but demonstrated significant bias. The algorithm employed for identifying and quantifying steps and bouts from a single wearable accelerometer worn on the lower-back has been demonstrated to be valid and could be used for pragmatic gait analysis in prolonged uncontrolled free-living environments.
Collapse
Affiliation(s)
- Aodhán Hickey
- Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| | | | | | | |
Collapse
|
46
|
Del Din S, Hickey A, Hurwitz N, Mathers JC, Rochester L, Godfrey A. Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age. Physiol Meas 2016; 37:1785-1797. [DOI: 10.1088/0967-3334/37/10/1785] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
47
|
Iosa M, Picerno P, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis. Expert Rev Med Devices 2016; 13:641-59. [PMID: 27309490 DOI: 10.1080/17434440.2016.1198694] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis. AREAS COVERED Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.
Collapse
Affiliation(s)
- Marco Iosa
- a Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Roma , Italy
| | - Pietro Picerno
- b Faculty of Psychology, School of Sport and Exercise Sciences , 'eCampus' University , Novedrate , CO , Italy
| | - Stefano Paolucci
- a Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Roma , Italy
| | - Giovanni Morone
- a Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Roma , Italy
| |
Collapse
|
48
|
Micó-Amigo ME, Kingma I, Ainsworth E, Walgaard S, Niessen M, van Lummel RC, van Dieën JH. A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly. J Neuroeng Rehabil 2016; 13:38. [PMID: 27093956 PMCID: PMC4837611 DOI: 10.1186/s12984-016-0145-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 04/03/2016] [Indexed: 11/17/2022] Open
Abstract
Background The assessment of short episodes of gait is clinically relevant and easily implemented, especially given limited space and time requirements. BFS (body-fixed-sensors) are small, lightweight and easy to wear sensors, which allow the assessment of gait at relative low cost and with low interference. Thus, the assessment with BFS of short episodes of gait, extracted from dailylife physical activity or measured in a standardised and supervised setting, may add value in the study of gait quality of the elderly. The aim of this study was to evaluate the accuracy of a novel algorithm based on acceleration signals recorded at different human locations (lower back and heels) for the detection of step durations over short episodes of gait in healthy elderly subjects. Methods Twenty healthy elderly subjects (73.7 ± 7.9 years old) walked twice a distance of 5 m, wearing a BFS on the lower back, and on the outside of each heel. Moreover, an optoelectronic three-dimensional (3D) motion tracking system was used to detect step durations. A novel algorithm is presented for the detection of step durations from low-back and heel acceleration signals separately. The accuracy of the algorithm was assessed by comparing absolute differences in step duration between the three methods: step detection from the optoelectronic 3D motion tracking system, step detection from the application of the novel algorithm to low-back accelerations, and step detection from the application of the novel algorithm to heel accelerations. Results The proposed algorithm successfully detected all the steps, without false positives and without false negatives. Absolute average differences in step duration within trials and across subjects were calculated for each comparison, between low-back accelerations and the optoelectronic system were on average 22.4 ± 7.6 ms (4.0 ± 1.3 % of average step duration), between heel accelerations and the optoelectronic system were on average 20.7 ± 11.8 ms (3.7 ± 1.9 %), and between low-back accelerations and heel accelerations were on average 27.8 ± 15.1 ms (4.9 ± 2.5 % of average step duration). Conclusions This study showed that the presented novel algorithm detects step durations over short episodes of gait in healthy elderly subjects with acceptable accuracy from low-back and heel accelerations, which provides opportunities to extract a range of gait parameters from short episodes of gait.
Collapse
Affiliation(s)
- M Encarna Micó-Amigo
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,McRoberts B. V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - Idsart Kingma
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Erik Ainsworth
- McRoberts B. V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - Stefan Walgaard
- McRoberts B. V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - Martijn Niessen
- McRoberts B. V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - Rob C van Lummel
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,McRoberts B. V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - Jaap H van Dieën
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
49
|
Trojaniello D, Cereatti A, Della Croce U. Foot clearance estimation during overground walking and vertical obstacle passing using shank-mounted MIMUs in healthy and pathological subjects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5505-8. [PMID: 26737538 DOI: 10.1109/embc.2015.7319638] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A method for assessing maximum foot clearance (maxFCl) during overground walking and obstacle passing using magnetic and inertial measurement units (MIMUs) placed above the malleoli is proposed and validated. The method precision and accuracy were evaluated using a stereo-photogrammetric system as a gold standard. The proposed method was applied to the data obtained from the gait of both healthy subjects and patients with various abnormal gaits. First, an optimally filtered direct and reverse integration (OFDRI) was used for each gait cycle to determine the gait velocity. Then, the effect of an additional OFDRI or a simple DRI approach for obtaining vertical foot displacement was explored. The results showed that the mean absolute errors associated to the maxFCl estimates were about 10% of its range of variation for the healthy and pathological subjects during overground walking. An accurate estimate of the maxFCl during obstacle passing was reached (mean absolute errors less than 5%). Additional testing on gait at various gait speed and on a greater number of subjects should be carried out to fully validate the MIMU-based maxFCl estimates.
Collapse
|
50
|
Sejdić E, Lowry KA, Bellanca J, Perera S, Redfern MS, Brach JS. Extraction of stride events from gait accelerometry during treadmill walking. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2015; 4. [PMID: 27088063 PMCID: PMC4826761 DOI: 10.1109/jtehm.2015.2504961] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson’s disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings.
Collapse
Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Kristin A Lowry
- Department of Physical Therapy, Des Moines University, Des Moines, IA 50312, USA.
| | - Jennica Bellanca
- Department of Biomedical Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Subashan Perera
- Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Mark S Redfern
- Department of Biomedical Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Jennifer S Brach
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| |
Collapse
|