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Ahmed K, Taheri S, Weygers I, Ortiz-Catalan M. Validation of IMU against optical reference and development of open-source pipeline: proof of concept case report in a participant with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant. J Neuroeng Rehabil 2024; 21:128. [PMID: 39085954 PMCID: PMC11290066 DOI: 10.1186/s12984-024-01426-6] [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: 11/29/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree. RESULTS Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in 'excellent' similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant. CONCLUSIONS We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.
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Affiliation(s)
- Kirstin Ahmed
- Chalmers University, Chalmersplatsen 4, 412 96, Gothenburg, Sweden.
| | - Shayan Taheri
- Chalmers University, Chalmersplatsen 4, 412 96, Gothenburg, Sweden
- Aalto University, Espoo, Finland
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Rukmini PG, Hegde RB, Basavarajappa BK, Bhat AK, Pujari AN, Gargiulo GD, Gunawardana U, Jan T, Naik GR. Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey. SENSORS (BASEL, SWITZERLAND) 2024; 24:4301. [PMID: 39001080 PMCID: PMC11243832 DOI: 10.3390/s24134301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/16/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.
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Affiliation(s)
- Pradyumna G. Rukmini
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Roopa B. Hegde
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Bommegowda K. Basavarajappa
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Anil Kumar Bhat
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Amit N. Pujari
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hertfordshire AL10 9AB, UK;
- School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
- The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Penrith, NSW 2751, Australia
- Translational Health Research Institute, Western Sydney University, Penrith, NSW 2751, Australia
- The Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
| | - Tony Jan
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
| | - Ganesh R. Naik
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
- College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
- Design and Creative Technology Vertical, Torrens University, Wakefield Street, Adelaide, SA 5000, Australia
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Taetz B, Lorenz M, Miezal M, Stricker D, Bleser-Taetz G. JointTracker: Real-time inertial kinematic chain tracking with joint position estimation. OPEN RESEARCH EUROPE 2024; 4:33. [PMID: 38953016 PMCID: PMC11216284 DOI: 10.12688/openreseurope.16939.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/22/2024] [Indexed: 07/03/2024]
Abstract
In-field human motion capture (HMC) is drawing increasing attention due to the multitude of application areas. Plenty of research is currently invested in camera-based (markerless) HMC, with the advantage of no infrastructure being required on the body, and additional context information being available from the surroundings. However, the inherent drawbacks of camera-based approaches are the limited field of view and occlusions. In contrast, inertial HMC (IHMC) does not suffer from occlusions, thus being a promising approach for capturing human motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to the underlying skeleton model. This work contributes to calibration-free IHMC, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot. The estimated and derived quantities, global and relative sensor orientations, joint positions, and segment lengths can be exploited for human motion analysis and anthropometric measurements, as well as in the context of hybrid markerless visual-inertial HMC.
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Affiliation(s)
- Bertram Taetz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
- IT & Engineering, International University of Applied Sciences, Erfurt, Thuringia, 99084, Germany
| | - Michael Lorenz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Markus Miezal
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Didier Stricker
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Gabriele Bleser-Taetz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
- IT & Engineering, International University of Applied Sciences, Erfurt, Thuringia, 99084, Germany
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Kvist A, Tinmark F, Bezuidenhout L, Reimeringer M, Conradsson DM, Franzén E. Validation of algorithms for calculating spatiotemporal gait parameters during continuous turning using lumbar and foot mounted inertial measurement units. J Biomech 2024; 162:111907. [PMID: 38134464 DOI: 10.1016/j.jbiomech.2023.111907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Spatiotemporal gait parameters such as step time and walking speed can be used to quantify gait performance and determine physical function. Inertial measurement units (IMUs) allow for the measurement of spatiotemporal gait parameters in unconstrained environments but must be validated against a gold standard. While many IMU systems and algorithms have been validated during treadmill walking and overground walking in a straight line, fewer studies have validated algorithms during more complex walking conditions such as continuous turning in different directions. This study explored the concurrent validity in a population of healthy adults (range 26-52 years) of three different algorithms using lumbar and foot mounted IMUs to calculate spatiotemporal gait parameters: two methods utilizing an inverted pendulum model, and one method based on strapdown integration. IMU data was compared to a Vicon twelve-camera optoelectronic system, using data collected from 9 participants performing straight walking and continuous walking trials at different speeds, resulting in 162 walking trials in total. Intraclass correlation coefficients (ICCA,1) for absolute agreement were calculated between the algorithm outputs and Vicon output. Temporal parameters were comparable in all methods and ranged from moderate to excellent, except double support time which was poor. Strapdown integration performed better for estimating spatial parameters than pendulum models during straight walking, but worse during turning. Selecting the most appropriate model should take into consideration both speed and walking condition.
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Affiliation(s)
- Alexander Kvist
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Fredrik Tinmark
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, Sweden
| | - Lucian Bezuidenhout
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Reimeringer
- Karolinska University Hospital, Motion Analysis Laboratory, Stockholm, Sweden
| | - David Moulaee Conradsson
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Erika Franzén
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden.
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Ensink C, Smulders K, Warnar J, Keijsers N. Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals. PeerJ 2023; 11:e16641. [PMID: 38111664 PMCID: PMC10726747 DOI: 10.7717/peerj.16641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/19/2023] [Indexed: 12/20/2023] Open
Abstract
Background Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS). Methods Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition. Results For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants. Conclusions Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.
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Affiliation(s)
- Carmen Ensink
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
- Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Katrijn Smulders
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Jolien Warnar
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Noel Keijsers
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
- Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Rehabilitation, Donders institute for Brain, Cognition and Behaviour, Radboud Univeristy Medical Center, Nijmegen, the Netherlands
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Baniasad M, Martin R, Crevoisier X, Pichonnaz C, Becce F, Aminian K. Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait. SENSORS (BASEL, SWITZERLAND) 2023; 23:3587. [PMID: 37050647 PMCID: PMC10098809 DOI: 10.3390/s23073587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.
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Affiliation(s)
- Mina Baniasad
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Robin Martin
- Department of Orthopaedic Surgery and Traumatology, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
| | - Xavier Crevoisier
- Department of Orthopaedic Surgery and Traumatology, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
| | - Claude Pichonnaz
- Department of Orthopaedic Surgery and Traumatology, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
- Department of Physiotherapy, School of Health Sciences HESAV, HES-SO University of Applied Sciences and Arts Western Switzerland, 1011 Lausanne, Switzerland
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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Caron-Laramée A, Walha R, Boissy P, Gaudreault N, Zelovic N, Lebel K. Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes? SENSORS (BASEL, SWITZERLAND) 2023; 23:2209. [PMID: 36850806 PMCID: PMC9965262 DOI: 10.3390/s23042209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 02/04/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Gait assessment is of interest to clinicians and researchers because it provides information about patients' functional mobility. Optoelectronic camera-based systems with gait event detection algorithms are considered the gold standard for gait assessment. Yet, the choice of the algorithm used to process data and extract the desired parameters from those detected gait events has an impact on the validity and reliability of the gait parameters computed. There are multiple techniques documented in the literature for computing gait events, including the analysis of the minimal position of the heel and toe markers, the computation of the relative distance between sacrum and foot markers, and the assessment of the smallest distance between the heel and toe markers. Validation studies conducted on these algorithms report variations in accuracy. Yet, these studies were conducted in different conditions, at varying gait velocities, and on different populations. The purpose of this study is to compare accuracy, precision, and robustness of three algorithms using motion capture data obtained from 25 healthy persons and 21 psoriatic arthritic patients walking at three distinct speeds on an instrumented treadmill. Errors in gait events recognition (heel strike-HS and toe-off-TO) and their impact on gait metrics (stance phase and stride length) are reported and compared to ground reaction force events measured with force plates. Over the 9114 collected steps across all walking speeds, more than 99% of gait events were recognized by all algorithms. On average, HS events were detected within 1.2 ms of the reference for two algorithms, while the third one detected HS late, with an average detection error of 40.7 ms. Yet, significant variations in accuracy were noted with gait speed; the performance decreased for all algorithms at slow speed. TO events were identified early by all algorithms, with an average error ranging from 16.0 to 100.0 ms. These gait events errors lead to 2-15% inaccuracies in stance phase assessment, while the impact on stride length remains below 0.3 cm. Overall, the algorithm based on the relative distance between the sacral and foot markers stood out for its accuracy, precision, and robustness at all walking speeds.
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Affiliation(s)
- Amélie Caron-Laramée
- Département de Génie Électrique et de Génie Informatique, Faculté de Génie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Roua Walha
- Department of Surgery, Orthopedics Division, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche sur le Vieillissement, Sherbrooke, QC J1H 4C4, Canada
| | - Patrick Boissy
- Department of Surgery, Orthopedics Division, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche sur le Vieillissement, Sherbrooke, QC J1H 4C4, Canada
| | - Nathaly Gaudreault
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Nikola Zelovic
- Département de Génie Électrique et de Génie Informatique, Faculté de Génie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Karina Lebel
- Département de Génie Électrique et de Génie Informatique, Faculté de Génie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Centre de Recherche sur le Vieillissement, Sherbrooke, QC J1H 4C4, Canada
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A Minimal Sensor Inertial Measurement Unit System Is Replicable and Capable of Estimating Bilateral Lower-Limb Kinematics in a Stationary Bodyweight Squat and a Countermovement Jump. J Appl Biomech 2023; 39:42-53. [PMID: 36652950 DOI: 10.1123/jab.2022-0168] [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: 06/28/2022] [Revised: 11/20/2022] [Accepted: 11/29/2022] [Indexed: 01/19/2023]
Abstract
This study aimed to validate a 7-sensor inertial measurement unit system against optical motion capture to estimate bilateral lower-limb kinematics. Hip, knee, and ankle sagittal plane peak angles and range of motion (ROM) were compared during bodyweight squats and countermovement jumps in 18 participants. In the bodyweight squats, left peak hip flexion (intraclass correlation coefficient [ICC] = .51), knee extension (ICC = .68) and ankle plantar flexion (ICC = .55), and hip (ICC = .63) and knee (ICC = .52) ROM had moderate agreement, and right knee ROM had good agreement (ICC = .77). Relatively higher agreement was observed in the countermovement jumps compared to the bodyweight squats, moderate to good agreement in right peak knee flexion (ICC = .73), and right (ICC = .75) and left (ICC = .83) knee ROM. Moderate agreement was observed for right ankle plantar flexion (ICC = .63) and ROM (ICC = .51). Moderate agreement (ICC > .50) was observed in all variables in the left limb except hip extension, knee flexion, and dorsiflexion. In general, there was poor agreement for peak flexion angles, and at least moderate agreement for joint ROM. Future work will aim to optimize methodologies to increase usability and confidence in data interpretation by minimizing variance in system-based differences and may also benefit from expanding planes of movement.
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Riek PM, Best AN, Wu AR. Validation of Inertial Sensors to Evaluate Gait Stability. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031547. [PMID: 36772586 PMCID: PMC9921478 DOI: 10.3390/s23031547] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/07/2023] [Accepted: 01/23/2023] [Indexed: 05/31/2023]
Abstract
The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an inertial sensor-based motion-capture suit could accurately assess gait stability. Healthy adult participants were asked to walk normally, with eyes closed, with approximately twice their normal step width, and in tandem. Their motion was simultaneously measured by inertial measurement units (IMU) and optical motion capture (Optical). Gait stability was assessed by calculating the margin of stability (MoS), short-term Lyapunov exponents, and step variability, along with basic gait parameters, using each system. We found that IMUs were able to detect the same differences among conditions as Optical for all but one of the measures. Bland-Altman and intraclass correlation (ICC) analysis demonstrated that mediolateral parameters (step width and mediolateral MoS) were measured less accurately by IMUs compared to their anterior-posterior equivalents (step length and anterior-posterior MoS). Our results demonstrate that IMUs can be used to evaluate gait stability through detecting changes in stability-related measures, but that the magnitudes of these measures might not be accurate or reliable, especially in the mediolateral direction.
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Liu M, Naseri A, Lee IC, Hu X, Lewek MD, Huang H. A simplified model for whole-body angular momentum calculation. Med Eng Phys 2023; 111:103944. [PMID: 36792238 PMCID: PMC9970829 DOI: 10.1016/j.medengphy.2022.103944] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 11/28/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (Lbody) is a convenient stability indicator for wearable motion capture systems. However, Lbody is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the Lbody could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.
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Affiliation(s)
- Ming Liu
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States.
| | - Amirreza Naseri
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States
| | - I-Chieh Lee
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States
| | - Michael D Lewek
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States
| | - He Huang
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, North Carolina, United States
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Finco MG, Patterson RM, Moudy SC. A pilot case series for concurrent validation of inertial measurement units to motion capture in individuals who use unilateral lower-limb prostheses. J Rehabil Assist Technol Eng 2023; 10:20556683231182322. [PMID: 37441370 PMCID: PMC10334000 DOI: 10.1177/20556683231182322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/31/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction Inertial measurement units (IMUs) may be viable options to collect gait data in clinics. This study compared IMU to motion capture data in individuals who use unilateral lower-limb prostheses. Methods Participants walked with lower-body IMUs and reflective markers in a motion analysis space. Sagittal plane hip, knee, and ankle waveforms were extracted for the entire gait cycle. Discrete points of peak flexion, peak extension, and range of motion were extracted from the waveforms. Stance times were also extracted to assess the IMU software's accuracy at detecting gait events. IMU and motion capture-derived data were compared using absolute differences and root mean square error (RMSE). Results Five individuals (n = 3 transtibial; n = 2 transfemoral) participated. IMU prosthetic limb data was similar to motion capture (RMSE: waveform ≤4.65°; discrete point ≤9.04°; stance ≤0.03s). However, one transfemoral participant had larger differences at the microprocessor knee joint (RMSE: waveform ≤15.64°; discrete ≤29.21°) from IMU magnetometer interference. Intact limbs tended to have minimal differences between IMU and motion capture data (RMSE: waveform ≤6.33°; discrete ≤9.87°; stance ≤0.04s). Conclusion Findings from this pilot study suggest IMUs have the potential to collect data similar to motion capture systems in sagittal plane kinematics and stance time.
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Affiliation(s)
- MG Finco
- Department of Anatomy and
Physiology, University of North Texas Health
Science Center, Fort Worth, TX, USA
| | - Rita M Patterson
- Department of Family and
Osteopathic Medicine, University of North Texas Health
Science Center, Fort Worth, TX, USA
| | - Sarah C Moudy
- Department of Anatomy and
Physiology, University of North Texas Health
Science Center, Fort Worth, TX, USA
- Department of Family and
Osteopathic Medicine, University of North Texas Health
Science Center, Fort Worth, TX, USA
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12
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Measurement of temporal and spatial parameters of ice hockey skating using a wearable system. Sci Rep 2022; 12:22280. [PMID: 36566292 PMCID: PMC9790001 DOI: 10.1038/s41598-022-26777-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
Ice hockey is a dynamic and competitive sport that requires a high level of neuromuscular and cardiovascular function. An objective assessment of skating helps coaches monitor athletes' performance during training sessions and matches. This study aimed to estimate the temporal and spatial parameters of skating by proposing an optimized configuration of wearable inertial measurement units (IMUs) and validating the system compared to in-lab reference systems. Ten participants were recruited to skate on a 14 m synthetic ice surface built in a motion-capture lab. Eight original event detection methods and three more adopted from gait analysis studies were implemented to detect blades-off and skate-strikes. These temporal events were detected with high accuracy and precision using skate-mounted IMUs. Also, four novel stride length estimation methods were developed to correct the estimated skaters' position using IMUs' readouts. The stride time, contact time, stride length, and stride velocity were obtained with relative errors of 3 ± 3%, 4 ± 3%, 2 ± 6%, and 2 ± 8%, respectively. This study showed that the wearable IMUs placed on skates and pelvis enables the estimation of temporal and spatial parameters of skating with high accuracy and precision, which could help coaches monitor skaters' performance in training.
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13
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Hulleck AA, Menoth Mohan D, Abdallah N, El Rich M, Khalaf K. Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:901331. [PMID: 36590154 PMCID: PMC9800936 DOI: 10.3389/fmedt.2022.901331] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. Research objective This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. Methods A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Results and significance Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.
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Affiliation(s)
- Abdul Aziz Hulleck
- Mechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Dhanya Menoth Mohan
- School of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, Australia
| | - Nada Abdallah
- Weill Cornell Medicine, New York City, NY, United States
| | - Marwan El Rich
- Mechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kinda Khalaf
- Biomedical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates,Health Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates,Correspondence: Kinda Khalaf
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14
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Kamstra H, Wilmes E, van der Helm FCT. Quantification of Error Sources with Inertial Measurement Units in Sports. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249765. [PMID: 36560134 PMCID: PMC9782389 DOI: 10.3390/s22249765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/06/2022] [Accepted: 12/10/2022] [Indexed: 06/01/2023]
Abstract
BACKGROUND Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filter. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to present a method to quantify each of the sources of error of an IMU separately. METHODS An optoelectronic system was used as a gold standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different football-specific movements, varying both in the type of movement, and in movement intensity. RESULTS The error of the definition of the body frames (11.3-18.7 deg RMSD), the STA (3.8-9.1 deg RMSD) and the error of the orientation filter (3.0-12.7 deg RMSD) were all quantified separately for each body segment. CONCLUSIONS The error sources of IMU-based motion analysis were quantified separately. This allows future studies to quantify and optimize the effects of error reduction techniques.
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Affiliation(s)
- Haye Kamstra
- Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CN Delft, The Netherlands
| | - Erik Wilmes
- Amsterdam Movement Sciences, Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- FIFA Medical Centre of Excellence, Royal Netherlands Football Association, 3707 HX Zeist, The Netherlands
| | - Frans C. T. van der Helm
- Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CN Delft, The Netherlands
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15
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OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data. INFORMATICS 2022. [DOI: 10.3390/informatics9040097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.
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16
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Jabri S, Carender W, Wiens J, Sienko KH. Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection. J Neuroeng Rehabil 2022; 19:132. [PMID: 36456966 PMCID: PMC9713134 DOI: 10.1186/s12984-022-01099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Vestibular deficits can impair an individual's ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers. METHODS Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task. RESULTS Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed. CONCLUSIONS These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.
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Affiliation(s)
- Safa Jabri
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
| | - Wendy Carender
- grid.412590.b0000 0000 9081 2336Department of Otolaryngology, Michigan Medicine, Ann Arbor, MI 48109 USA
| | - Jenna Wiens
- grid.214458.e0000000086837370Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - Kathleen H. Sienko
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
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17
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Hamilton RI, Williams J, Holt C. Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:1005000. [PMID: 36451804 PMCID: PMC9701737 DOI: 10.3389/fresc.2022.1005000] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/05/2022] [Indexed: 01/14/2024]
Abstract
The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.
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Affiliation(s)
- Rebecca I. Hamilton
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Jenny Williams
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | | | - Cathy Holt
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
- Osteoarthritis Technology NetworkPlus (OATech+), EPSRC UK-Wide Research Network+, United Kingdom
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18
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. SENSORS (BASEL, SWITZERLAND) 2022; 22:8398. [PMID: 36366096 PMCID: PMC9654083 DOI: 10.3390/s22218398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.
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Affiliation(s)
- Michael V. Potter
- Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USA
| | - Stephen M. Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Reed D. Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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19
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Ippisch R, Jelusic A, Bertram J, Schniepp R, Wuehr M. mVEGAS - mobile smartphone-based spatiotemporal gait analysis in healthy and ataxic gait disorders. Gait Posture 2022; 97:80-85. [PMID: 35914387 DOI: 10.1016/j.gaitpost.2022.07.256] [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: 03/17/2022] [Revised: 07/18/2022] [Accepted: 07/25/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Quantitative gait assessment is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric gait disorders. Its broad application, however, demands for low-cost and mobile solutions that facilitate high-quality assessment outside laboratory settings. The aim of this study was to present and evaluate the concurrent validity of a mobile and low-cost gait assessment tool (mVEGAS) that combines body-fixed inertial sensors and a smartphone-based video capture for spatiotemporal identification of gait sequences. METHODS Initially, we examined potential interferences of wearing mVEGAS with walking performance in a cohort of 20 young healthy individuals (31.1 ± 10.1 years; 8 females). Subsequently, we assessed the concurrent validity of mVEGAS as compared to a pressure-sensitive gait carpet (GAITRite) in a cohort of 26 healthy individuals (55.8 ± 14.3 years; 10 females) and 26 patients (55.7 ± 14.0; 14 females) with moderate to severe degrees of cerebellar gait ataxia. All participants were instructed to walk at preferred, slow, and fast walking speed and standard average and variability gait measures including velocity, stride length, stride time, base of support, swing and double support phase were examined for agreement between the two systems by absolute error and intraclass correlation coefficients (ICC). RESULTS Wearing mVEGAS did only marginally interfere with normal walking behavior. mVEGAS-derived average and variability gait measures exhibited good to excellent concurrent validity in healthy individuals (ICCs ranging between 0.645 and 1.000) and patients with gait ataxia (ICCs ranging between 0.788 and 1.000) SIGNIFICANCE: mVEGAS may facilitate high-quality and long-term gait monitoring in different non-specialized environments such as medical practices, nursing homes or community centers.
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Affiliation(s)
- R Ippisch
- Outpatient Center for Neurology, Psychiatry and Psychotherapy, Germering, Germany
| | - A Jelusic
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany
| | - J Bertram
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany
| | - R Schniepp
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany; Department of Neurology, Ludwig-Maximilians-University, Munich, Germany
| | - M Wuehr
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.
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20
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Homan K, Yamamoto K, Kadoya K, Ishida N, Iwasaki N. Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis. BMC Sports Sci Med Rehabil 2022; 14:71. [PMID: 35430808 PMCID: PMC9013462 DOI: 10.1186/s13102-022-00461-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). METHODS Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. RESULTS Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. CONCLUSION The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.
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Affiliation(s)
- Kentaro Homan
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Keizo Yamamoto
- School of Lifelong Sport, Hokusho University, 23 Bunkyodai, Ebetsu, 069-8511, Japan
| | - Ken Kadoya
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Naoki Ishida
- Department of Orthopedic Surgery, Hokuto Medical Corporation Hokuto Hospital, Kisen 7-5 Inada-cho, Obihiro, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
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21
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Das R, Paul S, Mourya GK, Kumar N, Hussain M. Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait. Front Neurosci 2022; 16:859298. [PMID: 35495059 PMCID: PMC9051393 DOI: 10.3389/fnins.2022.859298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/01/2022] [Indexed: 12/06/2022] Open
Abstract
The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.
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Affiliation(s)
- Ratan Das
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Gajendra Kumar Mourya
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Neelesh Kumar
- Biomedical Applications Unit, Central Scientific Instruments Organisation, Chandigarh, India
| | - Masaraf Hussain
- Department of Neurology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
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22
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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23
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Felius RAW, Geerars M, Bruijn SM, van Dieën JH, Wouda NC, Punt M. Reliability of IMU-Based Gait Assessment in Clinical Stroke Rehabilitation. SENSORS 2022; 22:s22030908. [PMID: 35161654 PMCID: PMC8839370 DOI: 10.3390/s22030908] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Background: Gait is often impaired in people after stroke, restricting personal independence and affecting quality of life. During stroke rehabilitation, walking capacity is conventionally assessed by measuring walking distance and speed. Gait features, such as asymmetry and variability, are not routinely determined, but may provide more specific insights into the patient’s walking capacity. Inertial measurement units offer a feasible and promising tool to determine these gait features. Objective: We examined the test–retest reliability of inertial measurement units-based gait features measured in a two-minute walking assessment in people after stroke and while in clinical rehabilitation. Method: Thirty-one people after stroke performed two assessments with a test–retest interval of 24 h. Each assessment consisted of a two-minute walking test on a 14-m walking path. Participants were equipped with three inertial measurement units, placed at both feet and at the low back. In total, 166 gait features were calculated for each assessment, consisting of spatio-temporal (56), frequency (26), complexity (63), and asymmetry (14) features. The reliability was determined using the intraclass correlation coefficient. Additionally, the minimal detectable change and the relative minimal detectable change were computed. Results: Overall, 107 gait features had good–excellent reliability, consisting of 50 spatio-temporal, 8 frequency, 36 complexity, and 13 symmetry features. The relative minimal detectable change of these features ranged between 0.5 and 1.5 standard deviations. Conclusion: Gait can reliably be assessed in people after stroke in clinical stroke rehabilitation using three inertial measurement units.
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Affiliation(s)
- Richard A. W. Felius
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
- Correspondence:
| | - Marieke Geerars
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Physiotherapy Department Neurology, Rehabilitation Center de Parkgraaf, 3526 KJ Utrecht, The Netherlands
| | - Sjoerd M. Bruijn
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
| | - Jaap H. van Dieën
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
| | - Natasja C. Wouda
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Physiotherapy Department Neurology, De Hoogstraat Revalidatie, 3583 TM Utrecht, The Netherlands
| | - Michiel Punt
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
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24
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Pappas P, Paradisis GP, Girard O. Influence of lower limb dominance on mechanical asymmetries during high-speed treadmill running. Sports Biomech 2021:1-12. [PMID: 34939524 DOI: 10.1080/14763141.2021.2016926] [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: 03/18/2021] [Accepted: 12/06/2021] [Indexed: 10/19/2022]
Abstract
We determine whether mechanical asymmetries differ between dominant and non-dominant legs at fast treadmill speed. Stride temporal variables, derived from high-speed camera recordings, allowed to estimate leg and vertical stiffness through the sine-wave method in 31 uninjured males during treadmill running at 6.67 m.s-1. Lower limb dominance was determined by the triple-jump test. The asymmetry was expressed as dominant-non-dominant and indexed by the absolute asymmetry index (ASI). The lowest and highest mean ASI values were detected for contact time (1.69%) and flight time (5.66%), respectively; ASI values for spring-mass characteristics (2.6% ≤ leg and vertical stiffness, peak vertical force, change in vertical leg length and centre of mass vertical displacement ≤ 4.7%) were within this range. Inter-subject variability in ASI varied substantially among the seven analysed variables with larger and smaller range of variability in ASI found for flight time (0-16.56%) and contact time (0-3.47%), respectively. Because the magnitude of group mean ASI appears inconsistent among stride temporal and spring-mass characteristics, different biomechanical variables should not be used interchangeably to assess laterality effects at fast treadmill speed. The widespread ASI range also indicates that using a 'fixed cut-off' threshold is an arbitrary approach.
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Affiliation(s)
- Panagiotis Pappas
- Sports Performance Laboratory, School of Physical Education & Sport Science, National & Kapodistrian University of Athens, Athens, Greece
| | - Giorgos P Paradisis
- Sports Performance Laboratory, School of Physical Education & Sport Science, National & Kapodistrian University of Athens, Athens, Greece
| | - Olivier Girard
- School of Human Sciences (Exercise and Sport Science), University of Western Australia, Crawley, Perth, Australia
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25
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Pollet J, Buraschi R, Villafañe JH, Piovanelli B, Negrini S. Gait parameters assessed with inertial measurement unit during 6-minute walk test in people after stroke. Int J Rehabil Res 2021; 44:358-363. [PMID: 34570043 DOI: 10.1097/mrr.0000000000000498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Gait impairments are among the main issues for stroke survivors as they are often linked with a lack of endurance capacity, balance impairments and functional limitations. These conditions can be carefully assessed by combining an endurance capacity test, the 6-minute walk test (6MWT), with the analysis of gait performed by an inertial measurement unit (IMU). We investigated the evolution of gait spatiotemporal and kinematic parameters during the 6MWT and compared it with age-matched healthy subjects. Moreover, gait parameters and 6MWT distance were associated with clinical outcome scales. In a postacute rehabilitation general hospital, we performed an observational study. Subjects with a single cortical stroke were recruited into the stroke group (SG). An age-matched healthy group (HG) was also recruited. All participants performed a 6MWT while wearing an IMU. The outcomes considered were 6MWT distance, gait spatiotemporal and kinematic parameters, and symmetry. Before the test at each subject, in the SG was administered Berg balance scale, Canadian neurological stroke scale and motricity index. 32 subjects were recruited into the SG, and 12 into the HG. Between the paretic and nonparetic limbs of the SG, there were differences in the stance phase and single support phase (P < 0.05). SG gait speed and stride length strongly correlated with balance, strength and disability scales. The SG walked fewer meters than the HG (Δ = -260.90 m; P < 0.001). Adopting an IMU during a 6mwt resulted valuable and effective in providing meaningful information regarding both the disability and functional capabilities of SG subjects.
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Affiliation(s)
| | | | | | | | - Stefano Negrini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan "La Statale"
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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26
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Laidig D, Jocham AJ, Guggenberger B, Adamer K, Fischer M, Seel T. Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors. Front Digit Health 2021; 3:736418. [PMID: 34806077 PMCID: PMC8599134 DOI: 10.3389/fdgth.2021.736418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 09/24/2021] [Indexed: 02/01/2023] Open
Abstract
Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.
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Affiliation(s)
- Daniel Laidig
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andreas J. Jocham
- Institute of Physiotherapy, FH JOANNEUM University of Applied Sciences, Graz, Austria
| | - Bernhard Guggenberger
- Institute of Physiotherapy, FH JOANNEUM University of Applied Sciences, Graz, Austria
| | - Klemens Adamer
- Vamed Rehabilitation Center Kitzbuehel, Kitzbuehel, Austria
| | - Michael Fischer
- Vamed Rehabilitation Center Kitzbuehel, Kitzbuehel, Austria
- Ludwig Boltzmann Institute for Rehabilitation Research, Vienna, Austria
- Hannover Medical School MHH, Clinic for Rehabilitation Medicine, Hannover, Germany
| | - Thomas Seel
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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27
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Teufl W, Taetz B, Miezal M, Dindorf C, Fröhlich M, Trinler U, Hogan A, Bleser G. Automated detection and explainability of pathological gait patterns using a one-class support vector machine trained on inertial measurement unit based gait data. Clin Biomech (Bristol, Avon) 2021; 89:105452. [PMID: 34481198 DOI: 10.1016/j.clinbiomech.2021.105452] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Machine learning approaches for the classification of pathological gait based on kinematic data, e.g. derived from inertial sensors, are commonly used in terms of a multi-class classification problem. However, there is a lack of research regarding one-class classifiers that are independent of certain pathologies. Therefore, it was the aim of this work to design a one-class classifier based on healthy norm-data that provides not only a prediction probability but rather an explanation of the classification decision, increasing the acceptance of this machine learning approach. METHODS The inertial sensor based gait kinematics of 25 healthy subjects was employed to train a one-class support vector machine. 25 healthy subjects, 20 patients after total hip arthroplasty and one transfemoral amputee served to validate the classifier. Prediction probabilities and feature importance scores were estimated for each subject. FINDINGS The support vector machine predicted 100% of the patients as outliers from the healthy group. Three healthy subjects were predicted as outliers. The feature importance calculation revealed the hip in the sagittal plane as most relevant feature concerning the group after total hip arthroplasty. For the misclassified healthy subject with the lowest probability score the knee flexion and the pelvis obliquity were identified. INTERPRETATION The support vector machine seems a useful tool to identify outliers from a healthy norm-group. The feature importance examination proved to provide valuable information on the musculoskeletal status of the subjects. In this combination, the present approach could be employed in various disciplines to identify abnormal gait and suggest subsequent training.
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Affiliation(s)
- Wolfgang Teufl
- University of Salzburg, Department of Sport Science, Schlossallee 49, 5400 Hallein, Austria.
| | - Bertram Taetz
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
| | - Markus Miezal
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
| | - Carlo Dindorf
- Technische Universität Kaiserslautern, Department of Sport Science, Erwin-Schrödinger-Straße 57, 67663 Kaiserslautern, Germany.
| | - Michael Fröhlich
- Technische Universität Kaiserslautern, Department of Sport Science, Erwin-Schrödinger-Straße 57, 67663 Kaiserslautern, Germany.
| | - Ursula Trinler
- BG Klinik Ludwigshafen, Ludwig-Guttmann-Straße 13, 67071 Ludwigshafen, Germany.
| | - Aidan Hogan
- BG Klinik Ludwigshafen, Ludwig-Guttmann-Straße 13, 67071 Ludwigshafen, Germany.
| | - Gabriele Bleser
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
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28
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Mehdizadeh S, Nabavi H, Sabo A, Arora T, Iaboni A, Taati B. Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions. J Neuroeng Rehabil 2021; 18:139. [PMID: 34526074 PMCID: PMC8443117 DOI: 10.1186/s12984-021-00933-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/01/2021] [Indexed: 12/02/2022] Open
Abstract
Background Many of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population. Methods We compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant’s averaged gait variables. Results Our findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement. Conclusions There are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.
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Affiliation(s)
- Sina Mehdizadeh
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Hoda Nabavi
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Andrea Sabo
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Twinkle Arora
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Andrea Iaboni
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Babak Taati
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, Canada. .,KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada. .,Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
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29
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Verrelli CM, Iosa M, Roselli P, Pisani A, Giannini F, Saggio G. Generalized Finite-Length Fibonacci Sequences in Healthy and Pathological Human Walking: Comprehensively Assessing Recursivity, Asymmetry, Consistency, Self-Similarity, and Variability of Gaits. Front Hum Neurosci 2021; 15:649533. [PMID: 34434095 PMCID: PMC8381873 DOI: 10.3389/fnhum.2021.649533] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 07/05/2021] [Indexed: 11/20/2022] Open
Abstract
Healthy and pathological human walking are here interpreted, from a temporal point of view, by means of dynamics-on-graph concepts and generalized finite-length Fibonacci sequences. Such sequences, in their most general definition, concern two sets of eight specific time intervals for the newly defined composite gait cycle, which involves two specific couples of overlapping (left and right) gait cycles. The role of the golden ratio, whose occurrence has been experimentally found in the recent literature, is accordingly characterized, without resorting to complex tools from linear algebra. Gait recursivity, self-similarity, and asymmetry (including double support sub-phase consistency) are comprehensively captured. A new gait index, named Φ-bonacci gait number, and a new related experimental conjecture—concerning the position of the foot relative to the tibia—are concurrently proposed. Experimental results on healthy or pathological gaits support the theoretical derivations.
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Affiliation(s)
| | - Marco Iosa
- Department of Psychology, Sapienza University of Rome, Rome, Italy.,Laboratory for the Study of Mind and Action in Rehabilitation Technologies, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia Foundation, Rome, Italy
| | - Paolo Roselli
- Department of Mathematics of University of Rome Tor Vergata, Rome, Italy.,Institut de Recherche en Mathématique et Physique, Universite' Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Antonio Pisani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Istituto di Ricovero e Cura a Carattere Scientific Mondino Foundation, Pavia, Italy
| | - Franco Giannini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
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30
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Osaka H, Fujita D, Kobara K, Suehiro T. Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking. Rehabil Res Pract 2021; 2021:8833221. [PMID: 34306759 PMCID: PMC8285203 DOI: 10.1155/2021/8833221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 06/17/2021] [Accepted: 06/30/2021] [Indexed: 11/26/2022] Open
Abstract
Gait parameters calculated from trunk acceleration reflect the features of gait; however, they cannot evaluate the gait pattern corresponding to the gait cycle. This study is aimed at investigating the differences in gait parameters calculated from trunk acceleration during gait corresponding to the gait cycle in healthy subjects with restricted knee extension. Participants included eight healthy volunteers who walked normally (NW) and with knee orthosis that restricted knee extension (ER). The ground reaction force (GRF), joint angles, and trunk acceleration during walking were measured using four force plates, a three-dimensional motion analysis system, and an inertial measurement unit. The peak GRF of the vertical components, joint ranges of motion, and moments of force were analyzed. The root mean square (RMS) and amplitude peak ratio (AR) of autocorrelation function were calculated from the trunk acceleration waveform. The first peak GRF and peak ankle dorsiflexion angles significantly increased during ER. The peak hip extension, knee flexion, knee extension angles, and the peak moment of knee extension significantly decreased during ER compared to that during NW. The acceleration AR significantly decreased during ER compared to that during NW. There was no significant difference in the RMS between the two conditions. The acceleration AR may show the temporal postural structure with restricted knee extension from the terminal stance phase for the ipsilateral limb to the initial stance phase for the contralateral limb. These results suggest that novel metrics for accelerometry gait analysis can reveal gait abnormalities, with restricted knee extension corresponding to the gait cycle.
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Affiliation(s)
- Hiroshi Osaka
- Department of Physical Therapy, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama, Japan
| | - Daisuke Fujita
- Department of Physical Therapy, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama, Japan
| | - Kenichi Kobara
- Department of Physical Therapy, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama, Japan
| | - Tadanobu Suehiro
- Department of Physical Therapy, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama, Japan
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31
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Scataglini S, Verwulgen S, Roosens E, Haelterman R, Van Tiggelen D. Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System. SENSORS (BASEL, SWITZERLAND) 2021; 21:4441. [PMID: 34209518 PMCID: PMC8271716 DOI: 10.3390/s21134441] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 12/22/2022]
Abstract
This study aims to measure and compare spatiotemporal gait parameters in nineteen subjects using a full wearable inertial mocap system Xsens (MVN Awinda, Netherlands) and a photoelectronic system one-meter OptoGaitTM (Microgait, Italy) on a treadmill imposing a walking speed of 5 km/h. A total of eleven steps were considered for each subject constituting a dataset of 209 samples from which spatiotemporal parameters (SPT) were calculated. The step length measurement was determined using two methods. The first one considers the calculation of step length based on the inverted pendulum model, while the second considers an anthropometric approach that correlates the stature with an anthropometric coefficient. Although the absolute agreement and consistency were found for the calculation of the stance phase, cadence and gait cycle, from our study, differences in SPT were found between the two systems. Mean square error (MSE) calculation of their speed (m/s) with respect to the imposed speed on a treadmill reveals a smaller error (MSE = 0.0008) using the OptoGaitTM. Overall, our results indicate that the accurate detection of heel strike and toe-off have an influence on phases and sub-phases for the entire acquisition. Future study in this domain should investigate how to design and integrate better products and algorithms aiming to solve the problematic issues already identified in this study without limiting the user's need and performance in a different environment.
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Affiliation(s)
- Sofia Scataglini
- Center for Physical Medicine and Rehabilitation, Military Hospital Queen Astrid, Rue Bruyn 200, 1120 Bruxelles, Belgium; (E.R.); (D.V.T.)
- Department of Mathematics, Royal Military Academy, Rue Hobbema 8, 1000 Bruxelles, Belgium;
- Department of Product Development, Faculty of Design Science, University of Antwerp, 2000 Antwerp, Belgium;
| | - Stijn Verwulgen
- Department of Product Development, Faculty of Design Science, University of Antwerp, 2000 Antwerp, Belgium;
| | - Eddy Roosens
- Center for Physical Medicine and Rehabilitation, Military Hospital Queen Astrid, Rue Bruyn 200, 1120 Bruxelles, Belgium; (E.R.); (D.V.T.)
| | - Robby Haelterman
- Department of Mathematics, Royal Military Academy, Rue Hobbema 8, 1000 Bruxelles, Belgium;
| | - Damien Van Tiggelen
- Center for Physical Medicine and Rehabilitation, Military Hospital Queen Astrid, Rue Bruyn 200, 1120 Bruxelles, Belgium; (E.R.); (D.V.T.)
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32
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Tabor P, Iwańska D, Grabowska O, Karczewska-Lindinger M, Popieluch A, Mastalerz A. Evaluation of selected indices of gait asymmetry for the assessment of running asymmetry. Gait Posture 2021; 86:1-6. [PMID: 33662807 DOI: 10.1016/j.gaitpost.2021.02.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/02/2021] [Accepted: 02/17/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND The methods of running asymmetry evaluation are not as highly developed as the methods of gait evaluation. RESEARCH QUESTION Which asymmetry indices used in the gait analysis best characterize the asymmetry of the running movement? METHODS The kinematics of the sprint in a straight run over a distance of 50 m was evaluated using the X-sens system. Three indices (Ia, IS, SA) were based on discrete values from the first point of contact of the foot with the ground (1% of the running cycle phase) and were called discrete coefficients. Furthermore, two indices (SI, RAI) were used to evaluate asymmetry over the entire running cycle and were termed continuous coefficients. The study examined 21 elite and non-professional middle-distance runners of both sexes. The evaluation of the usefulness of individual indices for the evaluation of gait asymmetry was performed by means of the analysis of ROC curves and evaluation of data scatter on Bland-Altman charts. RESULTS The values of discrete and continuous asymmetry coefficients were different to each other. In Bland-Altman plots there was a meaningful variety of discrete coefficients and a small variety of continuous coefficients. The analysis of ROC curves proves this assumption. Including the real curve course of angular placement in particular joints it is observed that continuous coefficients describe asymmetry of movement more precisely. SIGNIFICANCE It was found that the so-called continuous indices SI and RAI ensure the best identification of the phenomenon of movement asymmetry.
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Affiliation(s)
- Piotr Tabor
- Józef Pisudski Universytyof Physical Education in Warsaw, Poland, Department of Physical Education, Biomedical Sciences, Poland.
| | - Dagmara Iwańska
- Józef Pisudski Universytyof Physical Education in Warsaw, Poland, Department of Physical Education, Biomedical Sciences, Poland
| | - Olga Grabowska
- Józef Pisudski Universytyof Physical Education in Warsaw, Poland, Department of Physical Education, Biomedical Sciences, Poland
| | - Magdalena Karczewska-Lindinger
- University of Gothenburg, Institute of Neuroscience and Physiology, Department of Physiology and University of Gothenburg, Sweden, Center of Health and Performance at the Department of Food and Nutrition and Sport Science, Poland
| | - Aneta Popieluch
- Józef Pisudski Universytyof Physical Education in Warsaw, Poland, Department of Physical Education, Biomedical Sciences, Poland
| | - Andrzej Mastalerz
- Józef Pisudski Universytyof Physical Education in Warsaw, Poland, Department of Physical Education, Biomedical Sciences, Poland
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33
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model. PLoS One 2021; 16:e0249577. [PMID: 33878142 PMCID: PMC8057618 DOI: 10.1371/journal.pone.0249577] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/20/2021] [Indexed: 11/19/2022] Open
Abstract
Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.
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Affiliation(s)
- Michael V. Potter
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- * E-mail:
| | - Stephen M. Cain
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Reed D. Gurchiek
- M-Sense Research Group, University of Vermont, Burlington, VT, United States of America
| | - Ryan S. McGinnis
- M-Sense Research Group, University of Vermont, Burlington, VT, United States of America
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
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An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities. SENSORS 2021; 21:s21082869. [PMID: 33921846 PMCID: PMC8074136 DOI: 10.3390/s21082869] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/11/2021] [Accepted: 04/15/2021] [Indexed: 12/26/2022]
Abstract
The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.
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Prasanth H, Caban M, Keller U, Courtine G, Ijspeert A, Vallery H, von Zitzewitz J. Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:2727. [PMID: 33924403 PMCID: PMC8069962 DOI: 10.3390/s21082727] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/26/2021] [Accepted: 04/08/2021] [Indexed: 11/16/2022]
Abstract
Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.
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Affiliation(s)
- Hari Prasanth
- ONWARD, Building 32, Hightech Campus, 5656 AE Eindhoven, The Netherlands;
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
| | - Miroslav Caban
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (M.C.); (A.I.)
- ONWARD, EPFL Innovation Park Building C, 1015 Lausanne, Switzerland; (U.K.); (J.v.Z.)
| | - Urs Keller
- ONWARD, EPFL Innovation Park Building C, 1015 Lausanne, Switzerland; (U.K.); (J.v.Z.)
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland;
- Department of Neurosurgery, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), 1011 Lausanne, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), 1011 Lausanne, Switzerland
- Defitech Center for Interventional Neurotherapies (.NeuroRestore), CHUV/UNIL/EPFL, 1011 Lausanne, Switzerland
| | - Auke Ijspeert
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (M.C.); (A.I.)
| | - Heike Vallery
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
- Department of Rehabilitation Medicine, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Joachim von Zitzewitz
- ONWARD, EPFL Innovation Park Building C, 1015 Lausanne, Switzerland; (U.K.); (J.v.Z.)
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Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and Kinematic Measures. Appl Bionics Biomech 2021; 2021:6628320. [PMID: 33859720 PMCID: PMC8024877 DOI: 10.1155/2021/6628320] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/04/2021] [Accepted: 03/11/2021] [Indexed: 11/18/2022] Open
Abstract
Inertial-based motion capture (IMC) has been suggested to overcome many of the limitations of traditional motion capture systems. The validity of IMC is, however, suggested to be dependent on the methodologies used to process the raw data collected by the inertial device. The aim of this technical summary is to provide researchers and developers with a starting point from which to further develop the current IMC data processing methodologies used to estimate human spatiotemporal and kinematic measures. The main workflow pertaining to the estimation of spatiotemporal and kinematic measures was presented, and a general overview of previous methodologies used for each stage of data processing was provided. For the estimation of spatiotemporal measures, which includes stride length, stride rate, and stance/swing duration, measurement thresholding and zero-velocity update approaches were discussed as the most common methodologies used to estimate such measures. The methodologies used for the estimation of joint kinematics were found to be broad, with the combination of Kalman filtering or complimentary filtering and various sensor to segment alignment techniques including anatomical alignment, static calibration, and functional calibration methods identified as being most common. The effect of soft tissue artefacts, device placement, biomechanical modelling methods, and ferromagnetic interference within the environment, on the accuracy and validity of IMC, was also discussed. Where a range of methods have previously been used to estimate human spatiotemporal and kinematic measures, further development is required to reduce estimation errors, improve the validity of spatiotemporal and kinematic estimations, and standardize data processing practices. It is anticipated that this technical summary will reduce the time researchers and developers require to establish the fundamental methodological components of IMC prior to commencing further development of IMC methodologies, thus increasing the rate of development and utilisation of IMC.
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Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements. SENSORS 2021; 21:s21072259. [PMID: 33804840 PMCID: PMC8037013 DOI: 10.3390/s21072259] [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: 02/02/2021] [Revised: 03/12/2021] [Accepted: 03/20/2021] [Indexed: 11/29/2022]
Abstract
Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.
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Valarezo Añazco E, Han SJ, Kim K, Lopez PR, Kim TS, Lee S. Hand Gesture Recognition Using Single Patchable Six-Axis Inertial Measurement Unit via Recurrent Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:1404. [PMID: 33671364 PMCID: PMC7922880 DOI: 10.3390/s21041404] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/09/2021] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs). In this paper, a hand gesture recognition system using a single patchable six-axis IMU attached at the wrist via recurrent neural networks (RNN) is presented. The IMU comprises IC-based electronic components on a stretchable, adhesive substrate with serpentine-structured interconnections. The proposed patchable IMU with soft form-factors can be worn in close contact with the human body, comfortably adapting to skin deformations. Thus, signal distortion (i.e., motion artifacts) produced for vibration during the motion is minimized. Also, our patchable IMU has a wireless communication (i.e., Bluetooth) module to continuously send the sensed signals to any processing device. Our hand gesture recognition system was evaluated, attaching the proposed patchable six-axis IMU on the right wrist of five people to recognize three hand gestures using two models based on recurrent neural nets. The RNN-based models are trained and validated using a public database. The preliminary results show that our proposed patchable IMU have potential to continuously monitor people's motions in remote settings for applications in mobile health, human-computer interaction, and control gestures recognition.
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Affiliation(s)
- Edwin Valarezo Añazco
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
- Faculty of Engineering in Electricity and Computation, FIEC, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil EC090112, Ecuador
| | - Seung Ju Han
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
| | - Kangil Kim
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
| | - Patricio Rivera Lopez
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
| | - Tae-Seong Kim
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
| | - Sangmin Lee
- Department of Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea; (E.V.A.); (S.J.H.); (K.K.); (P.R.L.); (T.-S.K.)
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Jung WC, Lee JK. Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis. SENSORS 2021; 21:s21030786. [PMID: 33503973 PMCID: PMC7866024 DOI: 10.3390/s21030786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/06/2021] [Accepted: 01/20/2021] [Indexed: 12/02/2022]
Abstract
A treadmill was used to perform continuous walking tests in a limited space that can be covered by marker-based optical motion capture systems. Most treadmill-based gait data are analyzed based on gait cycle percentage. However, achieving continuous walking motion trajectories over time without time normalization is often required, even if tests are performed under treadmill walking conditions. This study presents a treadmill-to-overground mapping method of optical marker trajectories for treadmill-based continuous gait analysis, by adopting a simple concept of virtual origin. The position vector from the backward moving virtual origin to a targeted marker within a limited walking volume is the same as the position vector from the fixed origin to the forward moving marker over the ground. With the proposed method, it is possible (i) to observe the change in physical quantity visually during the treadmill walking, and (ii) to obtain overground-mapped gait data for evaluating the accuracy of the inertial-measurement-unit-based trajectory estimation. The accuracy of the proposed method was verified from various treadmill walking tests, which showed that the total travel displacement error rate was 0.32% on average.
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Cuadrado J, Michaud F, Lugrís U, Pérez Soto M. Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis. SENSORS 2021; 21:s21020427. [PMID: 33435369 PMCID: PMC7827523 DOI: 10.3390/s21020427] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/31/2020] [Accepted: 01/04/2021] [Indexed: 02/05/2023]
Abstract
Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.
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Auepanwiriyakul C, Waibel S, Songa J, Bentley P, Faisal AA. Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches. SENSORS 2020; 20:s20247313. [PMID: 33352717 PMCID: PMC7766923 DOI: 10.3390/s20247313] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 11/16/2022]
Abstract
Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted a two-stage study: First, we compared the inertial accuracy of wrist-worn IMUs, both research-grade (Xsens MTw Awinda, and Axivity AX3) and consumer-grade (Apple Watch Series 3 and 5), and optical motion tracking (OptiTrack). Given the moderate to strong performance of the consumer-grade sensors, we then evaluated this sensor and surveyed the experiences and attitudes of hospital patients (N = 44) and staff (N = 15) following a clinical test in which patients wore smartwatches for 1.5–24 h in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple Series 5 and 3 smartwatches and Axivity AX3 (RMSE 1.66 ± 0.12 m·s−2; R2 0.78 ± 0.02; RMSE 2.29 ± 0.09 m·s−2; R2 0.56 ± 0.01; RMSE 2.14 ± 0.09 m·s−2; R2 0.49 ± 0.02; RMSE 4.12 ± 0.18 m·s−2; R2 0.34 ± 0.01 respectively). For angular velocity, Series 5 and 3 smartwatches achieved similar performances against Xsens with RMSE 0.22 ± 0.02 rad·s−1; R2 0.99 ± 0.00; and RMSE 0.18 ± 0.01 rad·s−1; R2 1.00± SE 0.00, respectively. Surveys indicated that in-patients and healthcare professionals strongly agreed that wearable motion sensors are easy to use, comfortable, unobtrusive, suitable for long-term use, and do not cause anxiety or limit daily activities. Our results suggest that consumer smartwatches achieved moderate to strong levels of accuracy compared to laboratory gold-standard and are acceptable for pervasive monitoring of motion/behaviour within hospital settings.
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Affiliation(s)
- Chaiyawan Auepanwiriyakul
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK; (C.A.); (S.W.)
- Behaviour Analytics Lab, Data Science Institute, London SW7 2AZ, UK
| | - Sigourney Waibel
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK; (C.A.); (S.W.)
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK;
| | - Joanna Songa
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK;
| | - Paul Bentley
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK;
- Correspondence: (P.B.); (A.A.F.)
| | - A. Aldo Faisal
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK; (C.A.); (S.W.)
- Behaviour Analytics Lab, Data Science Institute, London SW7 2AZ, UK
- UKRI CDT in AI for Healthcare, Imperial College London, London SW7 2AZ, UK
- MRC London Institute of Medical Sciences, London W12 0NN, UK
- Correspondence: (P.B.); (A.A.F.)
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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.
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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.
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Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units. SENSORS 2020; 20:s20216331. [PMID: 33171972 PMCID: PMC7664179 DOI: 10.3390/s20216331] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 12/17/2022]
Abstract
Backward walking (BW) is being increasingly used in neurologic and orthopedic rehabilitation as well as in sports to promote balance control as it provides a unique challenge to the sensorimotor control system. The identification of initial foot contact (IC) and terminal foot contact (TC) events is crucial for gait analysis. Data of optical motion capture (OMC) kinematics and inertial motion units (IMUs) are commonly used to detect gait events during forward walking (FW). However, the agreement between such methods during BW has not been investigated. In this study, the OMC kinematics and inertial data of 10 healthy young adults were recorded during BW and FW on a treadmill at different speeds. Gait events were measured using both kinematics and inertial data and then evaluated for agreement. Excellent reliability (Interclass Correlation > 0.9) was achieved for the identification of both IC and TC. The absolute differences between methods during BW were 18.5 ± 18.3 and 20.4 ± 15.2 ms for IC and TC, respectively, compared to 9.1 ± 9.6 and 10.0 ± 14.9 for IC and TC, respectively, during FW. The high levels of agreement between methods indicate that both may be used for some applications of BW gait analysis.
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An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based 'Gold Standard'. SENSORS 2020; 20:s20185272. [PMID: 32942645 PMCID: PMC7571134 DOI: 10.3390/s20185272] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/05/2020] [Accepted: 09/12/2020] [Indexed: 12/15/2022]
Abstract
Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to ‘gold standard’ force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: −9.54 ± 0.66 ms, SK: −33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.
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Jeon H, Kim SL, Kim S, Lee D. Fast Wearable Sensor-Based Foot-Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping. SENSORS 2020; 20:s20174996. [PMID: 32899247 PMCID: PMC7506746 DOI: 10.3390/s20174996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022]
Abstract
Classification of foot–ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot–ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.
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Validation of Spatiotemporal and Kinematic Measures in Functional Exercises Using a Minimal Modeling Inertial Sensor Methodology. SENSORS 2020; 20:s20164586. [PMID: 32824216 PMCID: PMC7472244 DOI: 10.3390/s20164586] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/12/2020] [Accepted: 08/13/2020] [Indexed: 11/17/2022]
Abstract
This study proposes a minimal modeling magnetic, angular rate and gravity (MARG) methodology for assessing spatiotemporal and kinematic measures of functional fitness exercises. Thirteen healthy persons performed repetitions of the squat, box squat, sandbag pickup, shuffle-walk, and bear crawl. Sagittal plane hip, knee, and ankle range of motion (ROM) and stride length, stride time, and stance time measures were compared for the MARG method and an optical motion capture (OMC) system. The root mean square error (RMSE), mean absolute percentage error (MAPE), and Bland–Altman plots and limits of agreement were used to assess agreement between methods. Hip and knee ROM showed good to excellent agreement with the OMC system during the squat, box squat, and sandbag pickup (RMSE: 4.4–9.8°), while ankle ROM agreement ranged from good to unacceptable (RMSE: 2.7–7.2°). Unacceptable hip and knee ROM agreement was observed for the shuffle-walk and bear crawl (RMSE: 3.3–8.6°). The stride length, stride time, and stance time showed good to excellent agreement between methods (MAPE: (3.2 ± 2.8)%–(8.2 ± 7.9)%). Although the proposed MARG-based method is a valid means of assessing spatiotemporal and kinematic measures during various exercises, further development is required to assess the joint kinematics of small ROM, high velocity movements.
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Dindorf C, Teufl W, Taetz B, Bleser G, Fröhlich M. Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4385. [PMID: 32781583 PMCID: PMC7471970 DOI: 10.3390/s20164385] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/04/2020] [Accepted: 08/04/2020] [Indexed: 01/31/2023]
Abstract
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model's accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, Germany;
| | - Wolfgang Teufl
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Bertram Taetz
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Gabriele Bleser
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Michael Fröhlich
- Department of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, Germany;
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Berner K, Cockcroft J, Louw Q. Kinematics and temporospatial parameters during gait from inertial motion capture in adults with and without HIV: a validity and reliability study. Biomed Eng Online 2020; 19:57. [PMID: 32709239 PMCID: PMC7379351 DOI: 10.1186/s12938-020-00802-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 07/15/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Inertial measurement unit (IMU)-based motion capture systems are gaining popularity for gait analysis outside laboratories. It is important to determine the performance of such systems in specific patient populations. We aimed to validate and determine within-day reliability of an IMU system for measuring lower limb gait kinematics and temporal-spatial parameters (TSP) in people with and without HIV. METHODS Gait was recorded in eight adults with HIV (PLHIV) and eight HIV-seronegative participants (SNP), using IMUs and optical motion capture (OMC) simultaneously. Participants performed six gait trials. Fifteen TSP and 28 kinematic angles were extracted. Intraclass correlations (ICC), root-mean-square error (RMSE), mean absolute percentage error and Bland-Altman analyses were used to assess concurrent validity of the IMU system (relative to OMC) separately in PLHIV and SNP. IMU reliability was assessed during within-session retest of trials. ICCs were used to assess relative reliability. Standard error of measurement (SEM) and percentage SEM were used to assess absolute reliability. RESULTS Between-system TSP differences demonstrated acceptable-to-excellent ICCs (0.71-0.99), except for double support time and temporophasic parameters (< 0.60). All TSP demonstrated good mean absolute percentage errors (≤7.40%). For kinematics, ICCs were acceptable to excellent (0.75-1.00) for all but three range of motion (ROM) and four discrete angles. RMSE and bias were 0.0°-4.7° for all but two ROM and 10 discrete angles. In both groups, TSP reliability was acceptable to excellent for relative (ICC 0.75-0.99) (except for one temporal and two temporophasic parameters) and absolute (%SEM 1.58-15.23) values. Reliability trends of IMU-measured kinematics were similar between groups and demonstrated acceptable-to-excellent relative reliability (ICC 0.76-0.99) and clinically acceptable absolute reliability (SEM 0.7°-4.4°) for all but two and three discrete angles, respectively. Both systems demonstrated similar magnitude and directional trends for differences when comparing the gait of PLHIV with that of SNP. CONCLUSIONS IMU-based gait analysis is valid and reliable when applied in PLHIV; demonstrating a sufficiently low precision error to be used for clinical interpretation (< 5° for most kinematics; < 20% for TSP). IMU-based gait analysis is sensitive to subtle gait deviations that may occur in PLHIV.
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Affiliation(s)
- Karina Berner
- Division of Physiotherapy, Faculty of Medicine and Health Sciences, Stellenbosch University, PO Box 241, Cape Town, 8000, South Africa.
| | - John Cockcroft
- Central Analytical Facilities, Neuromechanics Unit, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
| | - Quinette Louw
- Division of Physiotherapy, Faculty of Medicine and Health Sciences, Stellenbosch University, PO Box 241, Cape Town, 8000, South Africa
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Zhou L, Fischer E, Tunca C, Brahms CM, Ersoy C, Granacher U, Arnrich B. How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications. SENSORS 2020; 20:s20154090. [PMID: 32707987 PMCID: PMC7435687 DOI: 10.3390/s20154090] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/12/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
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Affiliation(s)
- Lin Zhou
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
- Correspondence: (L.Z.); (B.A.)
| | - Eric Fischer
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
| | - Can Tunca
- NETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey; (C.T.); (C.E.)
| | - Clemens Markus Brahms
- Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany; (C.M.B.); (U.G.)
| | - Cem Ersoy
- NETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey; (C.T.); (C.E.)
| | - Urs Granacher
- Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany; (C.M.B.); (U.G.)
| | - Bert Arnrich
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
- Correspondence: (L.Z.); (B.A.)
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Vu HTT, Dong D, Cao HL, Verstraten T, Lefeber D, Vanderborght B, Geeroms J. A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3972. [PMID: 32708924 PMCID: PMC7411778 DOI: 10.3390/s20143972] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/08/2020] [Accepted: 07/15/2020] [Indexed: 01/01/2023]
Abstract
Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.
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Affiliation(s)
- Huong Thi Thu Vu
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- Faculty of Electronics Engineering Technology, Hanoi University of Industry, Hanoi 100000, Vietnam
| | - Dianbiao Dong
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Hoang-Long Cao
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- College of Engineering Technology, Can Tho University, Can Tho 90000, Vietnam
| | - Tom Verstraten
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Dirk Lefeber
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Bram Vanderborght
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Joost Geeroms
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
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