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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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De Pasquale P, Bonanno M, Mojdehdehbaher S, Quartarone A, Calabrò RS. The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions. Bioengineering (Basel) 2024; 11:538. [PMID: 38927774 PMCID: PMC11200415 DOI: 10.3390/bioengineering11060538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.
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Affiliation(s)
- Paolo De Pasquale
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy; (P.D.P.); (A.Q.); (R.S.C.)
| | - Mirjam Bonanno
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy; (P.D.P.); (A.Q.); (R.S.C.)
| | - Sepehr Mojdehdehbaher
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale Ferdinando Stagno d’Alcontres, 31, 98166 Messina, Italy;
| | - Angelo Quartarone
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy; (P.D.P.); (A.Q.); (R.S.C.)
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy; (P.D.P.); (A.Q.); (R.S.C.)
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Özateş ME, Yaman A, Salami F, Campos S, Wolf SI, Schneider U. Identification and interpretation of gait analysis features and foot conditions by explainable AI. Sci Rep 2024; 14:5998. [PMID: 38472287 PMCID: PMC10933258 DOI: 10.1038/s41598-024-56656-4] [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: 05/19/2023] [Accepted: 03/08/2024] [Indexed: 03/14/2024] Open
Abstract
Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.
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Affiliation(s)
| | - Alper Yaman
- Fraunhofer IPA, Nobelstrasse 12, Stuttgart, Germany.
| | - Firooz Salami
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
| | - Sarah Campos
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
| | - Sebastian I Wolf
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
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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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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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Bonanno M, De Nunzio AM, Quartarone A, Militi A, Petralito F, Calabrò RS. Gait Analysis in Neurorehabilitation: From Research to Clinical Practice. Bioengineering (Basel) 2023; 10:785. [PMID: 37508812 PMCID: PMC10376523 DOI: 10.3390/bioengineering10070785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/16/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.
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Affiliation(s)
- Mirjam Bonanno
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Alessandro Marco De Nunzio
- Department of Research and Development, LUNEX International University of Health, Exercise and Sports, Avenue du Parc des Sports, 50, 4671 Differdange, Luxembourg
| | - Angelo Quartarone
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Annalisa Militi
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Francesco Petralito
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
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