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Uhlrich SD, Uchida TK, Lee MR, Delp SL. Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future. J Biomech 2023; 154:111623. [PMID: 37210923 PMCID: PMC10544733 DOI: 10.1016/j.jbiomech.2023.111623] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/05/2023] [Indexed: 05/23/2023]
Abstract
Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.
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Affiliation(s)
- Scott D Uhlrich
- Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
| | - Thomas K Uchida
- Department of Mechanical Engineering, University of Ottawa, 161 Louis-Pasteur, Ottawa, ON K1N 6N5, Canada.
| | - Marissa R Lee
- Department of Mechanical Engineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
| | - Scott L Delp
- Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA; Department of Mechanical Engineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA; Department of Orthopaedic Surgery, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
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Lloyd DG, Saxby DJ, Pizzolato C, Worsey M, Diamond LE, Palipana D, Bourne M, de Sousa AC, Mannan MMN, Nasseri A, Perevoshchikova N, Maharaj J, Crossley C, Quinn A, Mulholland K, Collings T, Xia Z, Cornish B, Devaprakash D, Lenton G, Barrett RS. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport 2023:S1440-2440(23)00070-1. [PMID: 37149408 DOI: 10.1016/j.jsams.2023.04.001] [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: 05/31/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. DESIGN Narrative review. METHODS Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. RESULTS Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. CONCLUSIONS Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Matthew Worsey
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Dinesh Palipana
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Medicine, Dentistry and Health, Griffith University, Australia
| | - Matthew Bourne
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana Cardoso de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Malik Muhammad Naeem Mannan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Nataliya Perevoshchikova
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Jayishni Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claire Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Alastair Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Kyle Mulholland
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Tyler Collings
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Bradley Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; VALD Performance, Australia
| | | | - Rodney S Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
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Hossain I, Zhou S, Ishac K, Lind E, Sharwood L, Eager D. A Measurement of 'Walking-the-Wall' Dynamics: An Observational Study Using Accelerometry and Sensors to Quantify Risk Associated with Vertical Wall Impact Attenuation in Trampoline Parks. SENSORS (BASEL, SWITZERLAND) 2021; 21:7337. [PMID: 34770643 PMCID: PMC8587968 DOI: 10.3390/s21217337] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 11/28/2022]
Abstract
This study illustrates the application of a tri-axial accelerometer and gyroscope sensor device on a trampolinist performing the walking-the-wall manoeuvre on a high-performance trampoline to determine the performer dynamic conditions. This research found that rigid vertical walls would allow the trampolinist to obtain greater control and retain spatial awareness at greater levels than what is achievable on non-rigid vertical walls. With a non-rigid padded wall, the reaction force from the wall can be considered a variable force that is not constrained, and would not always provide the feedback that the trampolinist needs to maintain the balance with each climb up the wall and fall from height. This research postulates that unattenuated vertical walls are safer than attenuated vertical walls for walking-the-wall manoeuvres within trampoline park facilities. This is because non-rigid walls would provide higher g-force reaction feedback from the wall, which would reduce the trampolinist's control and stability. This was verified by measuring g-force on a horizontal rigid surface versus a non-rigid surface, where the g-force feedback was 27% higher for the non-rigid surface. Control and stability are both critical while performing the complex walking-the-wall manoeuvre. The trampolinist experienced a very high peak g-force, with a maximum g-force of approximately 11.5 g at the bottom of the jump cycle. It was concluded that applying impact attenuation padding to vertical walls used for walking-the-wall and similar activities would increase the likelihood of injury; therefore, padding of these vertical surfaces is not recommended.
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Affiliation(s)
- Imam Hossain
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia; (I.H.); (K.I.); (E.L.); (D.E.)
| | - Shilei Zhou
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia; (I.H.); (K.I.); (E.L.); (D.E.)
| | - Karlos Ishac
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia; (I.H.); (K.I.); (E.L.); (D.E.)
| | - Edward Lind
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia; (I.H.); (K.I.); (E.L.); (D.E.)
| | - Lisa Sharwood
- Faculty of Medicine and Health, University of Sydney, Sydney 2006, Australia;
| | - David Eager
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia; (I.H.); (K.I.); (E.L.); (D.E.)
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Pagnon D, Domalain M, Reveret L. Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 1: Robustness. SENSORS (BASEL, SWITZERLAND) 2021; 21:6530. [PMID: 34640862 PMCID: PMC8512754 DOI: 10.3390/s21196530] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022]
Abstract
Being able to capture relevant information about elite athletes' movement "in the wild" is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7° and 3.2° for all conditions and tasks, and mean absolute errors (compared to the reference condition-Ref) ranged between 0.35° and 1.6°. For walking, errors in the sagittal plane were: 1.5°, 0.90°, 0.19° for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.
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Affiliation(s)
- David Pagnon
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, 38330 Montbonnot-Saint-Martin, France;
| | - Mathieu Domalain
- Institut Pprime, Université de Poitiers, CNRS UPR 3346, 86360 Chasseneuil-du-Poitou, France;
| | - Lionel Reveret
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, 38330 Montbonnot-Saint-Martin, France;
- INRIA Grenoble Rhône-Alpes, 38330 Montbonnot-Saint-Martin, France
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