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Debnath M, Chang J, Bhandari K, Nagy DJ, Insperger T, Milton JG, Ngu AHH. Pole balancing on the fingertip: model-motivated machine learning forecasting of falls. Front Physiol 2024; 15:1334396. [PMID: 38638278 PMCID: PMC11024436 DOI: 10.3389/fphys.2024.1334396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.
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Affiliation(s)
- Minakshi Debnath
- Department of Computer Science, Texas State University, San Marcos, TX, United States
| | - Joshua Chang
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Keshav Bhandari
- Department of Computer Science, Texas State University, San Marcos, TX, United States
| | - Dalma J. Nagy
- Department of Applied Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary
| | - Tamas Insperger
- Department of Applied Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary
- HUN-REN–BME Dynamics of Machines Research Group, Budapest, Hungary
| | - John G. Milton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - Anne H. H. Ngu
- Department of Computer Science, Texas State University, San Marcos, TX, United States
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2
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Eghbali P, Becce F, Goetti P, Büchler P, Pioletti DP, Terrier A. Glenohumeral joint force prediction with deep learning. J Biomech 2024; 163:111952. [PMID: 38228026 DOI: 10.1016/j.jbiomech.2024.111952] [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: 08/15/2023] [Revised: 11/27/2023] [Accepted: 01/11/2024] [Indexed: 01/18/2024]
Abstract
Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM could predict MSM results and reduce computational costs. Within the total shoulder arthroplasty (TSA) domain, the glenohumeral joint force represents a critical MSM outcome as it can influence joint function, joint stability, and implant durability. Here, we aimed to employ deep learning techniques to predict both the magnitude and direction of the glenohumeral joint force. To achieve this, 959 virtual subjects were generated using the Markov-Chain Monte-Carlo method, providing patient-specific parameters from an existing clinical registry. A DLM was constructed to predict the glenohumeral joint force components within the scapula coordinate system for the generated subjects with a coefficient of determination of 0.97, 0.98, and 0.98 for the three components of the glenohumeral joint force. The corresponding mean absolute errors were 11.1, 12.2, and 15.0 N, which were about 2% of the maximum glenohumeral joint force. In conclusion, DLM maintains a comparable level of reliability in glenohumeral joint force estimation with MSM, while drastically reducing the computational costs.
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Affiliation(s)
- Pezhman Eghbali
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Patrick Goetti
- Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Philippe Büchler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
| | - Dominique P Pioletti
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland
| | - Alexandre Terrier
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland; Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Switzerland.
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3
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Manukian M, Bahdasariants S, Yakovenko S. Artificial physics engine for real-time inverse dynamics of arm and hand movement. PLoS One 2023; 18:e0295750. [PMID: 38091328 PMCID: PMC10718432 DOI: 10.1371/journal.pone.0295750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Simulating human body dynamics requires detailed and accurate mathematical models. When solved inversely, these models provide a comprehensive description of force generation that considers subject morphology and can be applied to control real-time assistive technology, for example, orthosis or muscle/nerve stimulation. Yet, model complexity hinders the speed of its computations and may require approximations as a mitigation strategy. Here, we use machine learning algorithms to provide a method for accurate physics simulations and subject-specific parameterization. Several types of artificial neural networks (ANNs) with varied architecture were tasked to generate the inverse dynamic transformation of realistic arm and hand movement (23 degrees of freedom). Using a physical model, we generated representative limb movements with bell-shaped end-point velocity trajectories within the physiological workspace. This dataset was used to develop ANN transformations with low torque errors (less than 0.1 Nm). Multiple ANN implementations using kinematic sequences solved accurately and robustly the high-dimensional kinematic Jacobian and inverse dynamics of arm and hand. These results provide further support for the use of ANN architectures that use temporal trajectories of time-delayed values to make accurate predictions of limb dynamics.
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Affiliation(s)
- Mykhailo Manukian
- Faculty of Applied Science, Ukrainian Catholic University, Lviv, Ukraine
| | - Serhii Bahdasariants
- Department of Human Performance—Pathophysiology, Rehabilitation, and Performance, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America
| | - Sergiy Yakovenko
- Department of Human Performance—Pathophysiology, Rehabilitation, and Performance, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America
- Rockefeller Neuroscience Institute, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America
- Mechanical and Aerospace Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia, United States of America
- Department of Biomedical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia, United States of America
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Bahdasariants S, Barela AMF, Gritsenko V, Bacca O, Barela JA, Yakovenko S. Does joint impedance improve dynamic leg simulations with explicit and implicit solvers? PLoS One 2023; 18:e0282130. [PMID: 37399198 DOI: 10.1371/journal.pone.0282130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/15/2023] [Indexed: 07/05/2023] Open
Abstract
The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)-describing hip, knee, ankle, and standing foot contact-was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods. Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.
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Affiliation(s)
- Serhii Bahdasariants
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Ana Maria Forti Barela
- Institute of Physical Activity and Sport Sciences, Cruzeiro do Sul University, São Paulo, SP, Brazil
| | - Valeriya Gritsenko
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, United States of America
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Odair Bacca
- Institute of Physical Activity and Sport Sciences, Cruzeiro do Sul University, São Paulo, SP, Brazil
| | - José Angelo Barela
- Department of Physical Education, São Paulo State University, Rio Claro, SP, Brazil
| | - Sergiy Yakovenko
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, United States of America
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
- Department of Mechanical and Aerospace Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States of America
- Department of Chemical and Biomedical Engineering, B.M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States of America
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5
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Demuth OE, Herbst E, Polet DT, Wiseman ALA, Hutchinson JR. Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods. J Exp Biol 2023; 226:jeb245132. [PMID: 36810943 PMCID: PMC10042237 DOI: 10.1242/jeb.245132] [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] [Indexed: 02/24/2023]
Abstract
Here, we review the modern interface of three-dimensional (3D) empirical (e.g. motion capture) and theoretical (e.g. modelling and simulation) approaches to the study of terrestrial locomotion using appendages in tetrapod vertebrates. These tools span a spectrum from more empirical approaches such as XROMM, to potentially more intermediate approaches such as finite element analysis, to more theoretical approaches such as dynamic musculoskeletal simulations or conceptual models. These methods have much in common beyond the importance of 3D digital technologies, and are powerfully synergistic when integrated, opening a wide range of hypotheses that can be tested. We discuss the pitfalls and challenges of these 3D methods, leading to consideration of the problems and potential in their current and future usage. The tools (hardware and software) and approaches (e.g. methods for using hardware and software) in the 3D analysis of tetrapod locomotion have matured to the point where now we can use this integration to answer questions we could never have tackled 20 years ago, and apply insights gleaned from them to other fields.
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Affiliation(s)
- Oliver E. Demuth
- Department of Earth Sciences, University of Cambridge, Cambridge, CB2 3EQ, UK
| | - Eva Herbst
- Palaeontological Institute and Museum, University of Zurich, 8006 Zürich, Switzerland
| | - Delyle T. Polet
- Structure and Motion Laboratory, Department of Comparative Biomedical Sciences, Royal Veterinary College, North Mymms, AL9 7TA, UK
| | - Ashleigh L. A. Wiseman
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 3ER, UK
| | - John R. Hutchinson
- Structure and Motion Laboratory, Department of Comparative Biomedical Sciences, Royal Veterinary College, North Mymms, AL9 7TA, UK
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Bahdasariants S, Barela AMF, Gritsenko V, Bacca O, Barela JA, Yakovenko S. Does joint impedance improve dynamic leg simulations with explicit and implicit solvers? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527805. [PMID: 36798166 PMCID: PMC9934618 DOI: 10.1101/2023.02.09.527805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)-describing hip, knee, ankle, and standing foot contact-was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods . Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.
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Affiliation(s)
- Serhii Bahdasariants
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Ana Maria Forti Barela
- Institute of Physical Activity and Sport Sciences, Cruzeiro do Sul University, São Paulo, SP, Brazil
| | - Valeriya Gritsenko
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, USA
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Odair Bacca
- Institute of Physical Activity and Sport Sciences, Cruzeiro do Sul University, São Paulo, SP, Brazil
| | - José Angelo Barela
- Department of Physical Education, São Paulo State University, Rio Claro, SP, Brazil
| | - Sergiy Yakovenko
- Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, USA
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, USA
- Department of Mechanical and Aerospace Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USA
- Department of Chemical and Biomedical Engineering, B.M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USA
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Thiamchoo N, Phukpattaranont P. Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions. PeerJ Comput Sci 2022; 8:e949. [PMID: 35634122 PMCID: PMC9138131 DOI: 10.7717/peerj-cs.949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object grasp classification. These represent feature projections that are combinations of either linear or nonlinear, and supervised or unsupervised types. All pairs of the four types of feature projection with seven types of classifiers were evaluated, with data from six EMG channels and an IMU sensors for nine upper limb positions in the transverse plane. The results showed that SRELM outperformed LDA with supervised feature projections, and t-SNE was superior to PCA with unsupervised feature projections. The classification errors from SRELM and t-SNE paired with the seven classifiers were from 1.50% to 2.65% and from 1.27% to 17.15%, respectively. A one-way ANOVA test revealed no statistically significant difference by classifier type when using the SRELM projection, which is a nonlinear supervised feature projection (p = 0.334). On the other hand, we have to carefully select an appropriate classifier for use with t-SNE, which is a nonlinear unsupervised feature projection. We achieved the lowest classification error 1.27% using t-SNE paired with a k-nearest neighbors classifier. For SRELM, the lowest 1.50% classification error was obtained when paired with a neural network classifier.
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Affiliation(s)
- Nantarika Thiamchoo
- Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, Thailand
| | - Pornchai Phukpattaranont
- Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, Thailand
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