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Cereatti A, Gurchiek R, Mündermann A, Fantozzi S, Horak F, Delp S, Aminian K. ISB recommendations on the definition, estimation, and reporting of joint kinematics in human motion analysis applications using wearable inertial measurement technology. J Biomech 2024; 173:112225. [PMID: 39032224 DOI: 10.1016/j.jbiomech.2024.112225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/07/2024] [Accepted: 07/08/2024] [Indexed: 07/23/2024]
Abstract
There is widespread and growing use of inertial measurement technology for human motion analysis in biomechanics and clinical research. Due to advancements in sensor miniaturization, inertial measurement units can be used to obtain a description of human body and joint kinematics both inside and outside the laboratory. While algorithms for data processing continue to improve, a lack of standard reporting guidelines compromises the interpretation and reproducibility of results, which hinders advances in research and development of measurement and intervention tools. To address this need, the International Society of Biomechanics approved our proposal to develop recommendations on the use of inertial measurement units for joint kinematics analysis. A collaborative effort that incorporated feedback from the biomechanics community has produced recommendations in five categories: sensor characteristics and calibration, experimental protocol, definition of a kinematic model and subject-specific calibration, analysis of joint kinematics, and quality assessment. We have avoided an overly prescriptive set of recommendations for algorithms and protocols, and instead offer reporting guidelines to facilitate reproducibility and comparability across studies. In addition to a conceptual framework and reporting guidelines, we provide a checklist to guide the design and review of research using inertial measurement units for joint kinematics.
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Affiliation(s)
- Andrea Cereatti
- Department of Electronics and Telecommunications, Polytechnic University of Torino, Torino, Italy.
| | - Reed Gurchiek
- Department of Bioengineering, Clemson University, Clemson, SC, USA
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland
| | - Silvia Fantozzi
- Department of Electric, Electronic and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, Italy
| | - Fay Horak
- APDM Precision Motion of Clario, Portland, Oregon, USA; Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Scott Delp
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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2
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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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Shiao Y, Chen GY, Hoang T. Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor. SENSORS (BASEL, SWITZERLAND) 2024; 24:4306. [PMID: 39001085 PMCID: PMC11244061 DOI: 10.3390/s24134306] [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: 04/28/2024] [Revised: 06/13/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024]
Abstract
Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.
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Affiliation(s)
- Yaojung Shiao
- Department of Vehicle Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
- Railway Vehicle Research Center, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Guan-Yu Chen
- Department of Vehicle Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Thang Hoang
- Faculty of Transportation Mechanical Engineering, The University of Danang-University of Science and Technology, Danang 550000, Vietnam
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Ebers MR, Pitts M, Kutz JN, Steele KM. Human motion data expansion from arbitrary sparse sensors with shallow recurrent decoders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596487. [PMID: 38895371 PMCID: PMC11185509 DOI: 10.1101/2024.06.01.596487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Advances in deep learning and sparse sensing have emerged as powerful tools for monitoring human motion in natural environments. We develop a deep learning architecture, constructed from a shallow recurrent decoder network, that expands human motion data by mapping a limited (sparse) number of sensors to a comprehensive (dense) configuration, thereby inferring the motion of unmonitored body segments. Even with a single sensor, we reconstruct the comprehensive set of time series measurements, which are important for tracking and informing movement-related health and performance outcomes. Notably, this mapping leverages sensor time histories to inform the transformation from sparse to dense sensor configurations. We apply this mapping architecture to a variety of datasets, including controlled movement tasks, gait pattern exploration, and free-moving environments. Additionally, this mapping can be subject-specific (based on an individual's unique data for deployment at home and in the community) or group-based (where data from a large group are used to learn a general movement model and predict outcomes for unknown subjects). By expanding our datasets to unmeasured or unavailable quantities, this work can impact clinical trials, robotic/device control, and human performance by improving the accuracy and availability of digital biomarker estimates.
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Affiliation(s)
- Megan R Ebers
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195
| | - Mackenzie Pitts
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195
| | - J Nathan Kutz
- Department of Applied Mathematics and Electrical and Computer Engineering, University of Washington, Seattle, WA 98195
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195
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Kim M, Park S. Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor. Sci Rep 2024; 14:9201. [PMID: 38649763 PMCID: PMC11035581 DOI: 10.1038/s41598-024-59949-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
In this study, we address two technical challenges to enhance golf swing trajectory accuracy using a wrist-worn inertial sensor: orientation estimation and drift error mitigation. We extrapolated consistent sensor orientation from specific address-phase signal segments and trained the estimation with a convolutional neural network. We then mitigated drift error by applying a constraint on wrist speed at the address, backswing top, and finish, and ensuring that the wrist's finish displacement aligns with a virtual circle on the 3D swing plane. To verify the proposed methods, we gathered data from twenty male right-handed golfers, including professionals and amateurs, using a driver and a 7-iron. The orientation estimation error was about 60% of the baseline, comparable to studies requiring additional sensor information or calibration poses. The drift error was halved and the single-inertial-sensor tracking performance across all swing phases was about 17 cm, on par with multimodal approaches. This study introduces a novel signal processing method for tracking rapid, wide-ranging motions, such as a golf swing, while maintaining user convenience. Our results could impact the burgeoning field of daily motion monitoring for health care, especially with the increasing prevalence of wearable devices like smartwatches.
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Affiliation(s)
- Myeongsub Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Sukyung Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
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Couvertier M, Pacher L, Fradet L. Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no. J Biomech 2024; 168:112091. [PMID: 38640829 DOI: 10.1016/j.jbiomech.2024.112091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/14/2024] [Accepted: 04/09/2024] [Indexed: 04/21/2024]
Abstract
Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the "rigid component" of soft-tissue artefact impacting all sensors located on one segment.
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Affiliation(s)
- Marien Couvertier
- Equipe RoBioSS, Institut PPRIME, UPR3346 CNRS Université de Poitiers ISAE ENSMA, 11 boulevard Marie et Pierre Curie, Site du Futuroscope TSA 41123, 86073 Poitiers Cedex 9, France.
| | - Léonie Pacher
- Equipe RoBioSS, Institut PPRIME, UPR3346 CNRS Université de Poitiers ISAE ENSMA, 11 boulevard Marie et Pierre Curie, Site du Futuroscope TSA 41123, 86073 Poitiers Cedex 9, France
| | - Laetitia Fradet
- Equipe RoBioSS, Institut PPRIME, UPR3346 CNRS Université de Poitiers ISAE ENSMA, 11 boulevard Marie et Pierre Curie, Site du Futuroscope TSA 41123, 86073 Poitiers Cedex 9, France
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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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8
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Simonet A, Delafontaine A, Fourcade P, Yiou E. Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson's Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems. SENSORS (BASEL, SWITZERLAND) 2024; 24:1302. [PMID: 38400460 PMCID: PMC10891667 DOI: 10.3390/s24041302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND This study tested the agreement between a markerless motion capture system and force-plate system ("gold standard") to quantify stability control and motor performance during gait initiation. METHODS Healthy adults (young and elderly) and patients with Parkinson's disease performed gait initiation series at spontaneous and maximal velocity on a system of two force-plates placed in series while being filmed by a markerless motion capture system. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of motor performance) and the braking index (indicator of stability control). RESULTS Descriptive statistics indicated that both systems detected between-group differences and velocity effects similarly, while a Bland-Altman plot analysis showed that mean biases of both biomechanical indicators were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (braking index) and moderate (motor performance) evidence that both systems provided equivalent values. However, a trial-by-trial analysis of Bland-Altman plots revealed the possibility of differences >10% between the two systems. CONCLUSION Although non-negligible differences do occur, a markerless motion capture system appears to be as efficient as a force-plate system in detecting Parkinson's disease and velocity condition effects on the braking index and motor performance.
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Affiliation(s)
- Arnaud Simonet
- LADAPT Loiret, Centre de Soins de Suite et de Réadaptation, 45200 Amilly, France;
- CIAMS, Université Paris-Saclay, 91190 Paris, France; (A.D.); (P.F.)
- CIAMS, Université d’Orléans, 45067 Orléans, France
| | - Arnaud Delafontaine
- CIAMS, Université Paris-Saclay, 91190 Paris, France; (A.D.); (P.F.)
- CIAMS, Université d’Orléans, 45067 Orléans, France
- Département de Chirurgie Orthopédique, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
| | - Paul Fourcade
- CIAMS, Université Paris-Saclay, 91190 Paris, France; (A.D.); (P.F.)
- CIAMS, Université d’Orléans, 45067 Orléans, France
| | - Eric Yiou
- CIAMS, Université Paris-Saclay, 91190 Paris, France; (A.D.); (P.F.)
- CIAMS, Université d’Orléans, 45067 Orléans, France
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9
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Niță VA, Magyar P. Improving Balance and Movement Control in Fencing Using IoT and Real-Time Sensorial Feedback. SENSORS (BASEL, SWITZERLAND) 2023; 23:9801. [PMID: 38139647 PMCID: PMC10747936 DOI: 10.3390/s23249801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Fencing, a sport emphasizing the equilibrium and movement control of participants, forms the focal point of inquiry in the current study. The research endeavors to assess the efficacy of a novel system designed for real-time monitoring of fencers' balance and movement control, augmented by modules incorporating visual feedback and haptic feedback, to ascertain its potential for performance enhancement. Over a span of five weeks, three distinct groups, each comprising ten fencers, underwent specific training: a control group, a cohort utilizing the system with a visual real-time feedback module, and a cohort using the system with a haptic real-time feedback module. Positive outcomes were observed across all three groups, a typical occurrence following a 5-week training regimen. However, noteworthy advancements were particularly discerned in the second group, reaching approximately 15%. In contrast, the improvements in the remaining two groups were below 5%. Statistical analyses employing the Wilcoxon signed-rank test for repeated measures were applied to assess the significance of the results. Significance was solely ascertained for the second group, underscoring the efficacy of the system integrated with visual real-time feedback in yielding statistically noteworthy performance enhancements.
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Affiliation(s)
- Valentin-Adrian Niță
- Department of Communications, University Politehnica of Timișoara, 300006 Timișoara, Romania
| | - Petra Magyar
- Department of Physical and Sports Education, West University of Timisoara, 300223 Timișoara, Romania;
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10
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Adamczyk PG, Harper SE, Reiter AJ, Roembke RA, Wang Y, Nichols KM, Thelen DG. Wearable sensing for understanding and influencing human movement in ecological contexts. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100492. [PMID: 37663049 PMCID: PMC10469849 DOI: 10.1016/j.cobme.2023.100492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Wearable sensors offer a unique opportunity to study movement in ecological contexts - that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.
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Affiliation(s)
- Peter Gabriel Adamczyk
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Sara E Harper
- University of Wisconsin – Madison, Department of Biomedical Engineering, 1550 Engineering Dr., Madison, Wisconsin, USA
| | - Alex J Reiter
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Rebecca A Roembke
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Yisen Wang
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Kieran M Nichols
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Darryl G. Thelen
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
- University of Wisconsin – Madison, Department of Biomedical Engineering, 1550 Engineering Dr., Madison, Wisconsin, USA
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11
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Chen H, Schall MC, Martin SM, Fethke NB. Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist. SENSORS (BASEL, SWITZERLAND) 2023; 23:7053. [PMID: 37631592 PMCID: PMC10458653 DOI: 10.3390/s23167053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively.
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Affiliation(s)
- Howard Chen
- Industrial & Systems Engineering and Engineering Management Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA
| | - Mark C. Schall
- Department of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA;
| | - Scott M. Martin
- Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA;
| | - Nathan B. Fethke
- Department of Occupational & Environmental Health, The University of Iowa, Iowa City, IA 52242, USA;
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12
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Jung S, de l’Escalopier N, Oudre L, Truong C, Dorveaux E, Gorintin L, Ricard D. A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment. SENSORS (BASEL, SWITZERLAND) 2023; 23:4000. [PMID: 37112339 PMCID: PMC10145775 DOI: 10.3390/s23084000] [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: 03/08/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
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Affiliation(s)
- Sylvain Jung
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
- ENGIE Lab CRIGEN, F-93249 Stains, France
| | - Nicolas de l’Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Charles Truong
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Eric Dorveaux
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
| | - Louis Gorintin
- Novakamp, 10-12 Avenue du Bosquet, F-95560 Baillet en France, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, F-75005 Paris, France
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13
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Ortigas Vásquez A, Maas A, List R, Schütz P, Taylor WR, Grupp TM. A Framework for Analytical Validation of Inertial-Sensor-Based Knee Kinematics Using a Six-Degrees-of-Freedom Joint Simulator. SENSORS (BASEL, SWITZERLAND) 2022; 23:348. [PMID: 36616945 PMCID: PMC9824828 DOI: 10.3390/s23010348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 06/16/2023]
Abstract
The success of kinematic analysis that relies on inertial measurement units (IMUs) heavily depends on the performance of the underlying algorithms. Quantifying the level of uncertainty associated with the models and approximations implemented within these algorithms, without the complication of soft-tissue artefact, is therefore critical. To this end, this study aimed to assess the rotational errors associated with controlled movements. Here, data of six total knee arthroplasty patients from a previously published fluoroscopy study were used to simulate realistic kinematics of daily activities using IMUs mounted to a six-degrees-of-freedom joint simulator. A model-based method involving extended Kalman filtering to derive rotational kinematics from inertial measurements was tested and compared against the ground truth simulator values. The algorithm demonstrated excellent accuracy (root-mean-square error ≤0.9°, maximum absolute error ≤3.2°) in estimating three-dimensional rotational knee kinematics during level walking. Although maximum absolute errors linked to stair descent and sit-to-stand-to-sit rose to 5.2° and 10.8°, respectively, root-mean-square errors peaked at 1.9° and 7.5°. This study hereby describes an accurate framework for evaluating the suitability of the underlying kinematic models and assumptions of an IMU-based motion analysis system, facilitating the future validation of analogous tools.
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Affiliation(s)
- Ariana Ortigas Vásquez
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
| | - Allan Maas
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
| | - Renate List
- Human Performance Lab., Schulthess Clinic, 8008 Zurich, Switzerland
| | - Pascal Schütz
- Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland
| | - William R. Taylor
- Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland
| | - Thomas M. Grupp
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
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14
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Laidig D, Weygers I, Seel T. Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints. SENSORS (BASEL, SWITZERLAND) 2022; 22:9850. [PMID: 36560219 PMCID: PMC9785932 DOI: 10.3390/s22249850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.
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Affiliation(s)
- Daniel Laidig
- Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany
| | - Ive Weygers
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Thomas Seel
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
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15
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Choi M, Kim Y. Biomechanical Asymmetry of Strength and Dynamic Balance Kinetics in Middle-Ages with Adhesive Capsulitis of the Hip. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13093. [PMID: 36293688 PMCID: PMC9603790 DOI: 10.3390/ijerph192013093] [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: 09/09/2022] [Revised: 10/01/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The representative clinical features of adhesive capsulitis of the hip (ACH) are restricted range of motion (ROM) and pain. However, reports on kinetics such as strength and dynamic balance that explain physical functions are rare. This study compared subjective hip scores using Copenhagen Hip and Groin Outcome Score (HAGOS) and ROM using a manual goniometer as well as strength using isokinetic equipment, and dynamic balance through the Y-balance test, between patients with ACH and healthy individuals. Data of 193 middle-aged participants (men: 99 and women: 94) were analyzed. The ACH group scored significantly lower in all six HAGOS sub-sections. Hip joint flexion, abduction, internal and external rotation ROM were significantly lower in ACH compared to healthy group. These results were the same for men and women. In the strength of men and women, flexion, adduction, and abduction, and dynamic balance in all three directions were significantly decreased in ACH. Meanwhile, there were no significant between-group differences in the functional decrease in extension and adduction of ROM, and extension strength. In conclusion, subjective evaluation and dynamic balance of patients with ACH were decreased in the all parts. In ROM, flexion, abduction, internal rotation, and external rotation were restricted except for extension and adduction. Men and women with ACH maintained extensor strength, but had weakened strength in flexion, adduction and abduction. This information will be useful for therapists to understand the biomechanical properties of ACH and to design effective rehabilitation programs.
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Affiliation(s)
- Moonyoung Choi
- Department of Sports Science Convergence, Dongguk University, Seoul 04620, Korea
| | - Yonghwan Kim
- Department of Physical Education, Gangneung-Wonju National University, Gangneung 25457, Korea
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Carcreff L, Payen G, Grouvel G, Massé F, Armand S. Three-Dimensional Lower-Limb Kinematics from Accelerometers and Gyroscopes with Simple and Minimal Functional Calibration Tasks: Validation on Asymptomatic Participants. SENSORS 2022; 22:s22155657. [PMID: 35957218 PMCID: PMC9370908 DOI: 10.3390/s22155657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/15/2022] [Accepted: 07/26/2022] [Indexed: 02/04/2023]
Abstract
The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use.
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Affiliation(s)
- Lena Carcreff
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Nantes Université, Movement-Interactions-Performance, MIP, UR4334, F-44000 Nantes, France
- Correspondence:
| | - Gabriel Payen
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Gautier Grouvel
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
| | - Fabien Massé
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Stéphane Armand
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
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Alarcón-Aldana AC, Callejas-Cuervo M, Bastos-Filho T, Bó APL. A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System. SENSORS (BASEL, SWITZERLAND) 2022; 22:4898. [PMID: 35808393 PMCID: PMC9269534 DOI: 10.3390/s22134898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022]
Abstract
This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.
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Affiliation(s)
| | - Mauro Callejas-Cuervo
- Faculty of Engineering, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, Brazil;
| | - Antônio Padilha Lanari Bó
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia;
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