1
|
Ting LH, Gick B, Kesar TM, Xu J. Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230485. [PMID: 39155720 DOI: 10.1098/rstb.2023.0485] [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: 12/17/2023] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 08/20/2024] Open
Abstract
Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
Collapse
Affiliation(s)
- Lena H Ting
- Coulter Department of Biomedical Engineering at Georgia Tech and Emory, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Bryan Gick
- Department of Linguistics, The University British Columbia, Vancouver, BC V6T 1Z4, Canada
- Haskins Laboratories, Yale University, New Haven, CT 06520, USA
| | - Trisha M Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Jing Xu
- Department of Kinesiology, The University of Georgia, Athens, GA 30602, USA
| |
Collapse
|
2
|
Li G, Ao D, Vega MM, Zandiyeh P, Chang SH, Penny AN, Lewis VO, Fregly BJ. Changes in walking function and neural control following pelvic cancer surgery with reconstruction. Front Bioeng Biotechnol 2024; 12:1389031. [PMID: 38827035 PMCID: PMC11140731 DOI: 10.3389/fbioe.2024.1389031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.
Collapse
Affiliation(s)
- Geng Li
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Payam Zandiyeh
- Biomotion Laboratory, Department of Orthopedic Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shuo-Hsiu Chang
- Department of Physical Medicine and Rehabilitation, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Alexander. N. Penny
- Department of Orthopedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Valerae O. Lewis
- Department of Orthopedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| |
Collapse
|
3
|
Qi H, Tian D, Luan F, Yang R, Zeng N. Pathophysiological changes of muscle after ischemic stroke: a secondary consequence of stroke injury. Neural Regen Res 2024; 19:737-746. [PMID: 37843207 PMCID: PMC10664100 DOI: 10.4103/1673-5374.382221] [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: 01/06/2023] [Revised: 03/30/2023] [Accepted: 06/01/2023] [Indexed: 10/17/2023] Open
Abstract
Sufficient clinical evidence suggests that the damage caused by ischemic stroke to the body occurs not only in the acute phase but also during the recovery period, and that the latter has a greater impact on the long-term prognosis of the patient. However, current stroke studies have typically focused only on lesions in the central nervous system, ignoring secondary damage caused by this disease. Such a phenomenon arises from the slow progress of pathophysiological studies examining the central nervous system. Further, the appropriate therapeutic time window and benefits of thrombolytic therapy are still controversial, leading scholars to explore more pragmatic intervention strategies. As treatment measures targeting limb symptoms can greatly improve a patient's quality of life, they have become a critical intervention strategy. As the most vital component of the limbs, skeletal muscles have become potential points of concern. Despite this, to the best of our knowledge, there are no comprehensive reviews of pathophysiological changes and potential treatments for post-stroke skeletal muscle. The current review seeks to fill a gap in the current understanding of the pathological processes and mechanisms of muscle wasting atrophy, inflammation, neuroregeneration, mitochondrial changes, and nutritional dysregulation in stroke survivors. In addition, the challenges, as well as the optional solutions for individualized rehabilitation programs for stroke patients based on motor function are discussed.
Collapse
Affiliation(s)
- Hu Qi
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Dan Tian
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Fei Luan
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Ruocong Yang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Nan Zeng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| |
Collapse
|
4
|
Tahmid S, Font-Llagunes JM, Yang J. Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling. J Biomech Eng 2024; 146:011005. [PMID: 37902326 DOI: 10.1115/1.4063899] [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: 05/16/2023] [Accepted: 10/23/2023] [Indexed: 10/31/2023]
Abstract
Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29).
Collapse
Affiliation(s)
- Shadman Tahmid
- Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409
| | - Josep M Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona 08028, Catalonia, Spain; Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat 08950, Catalonia, Spain
| | - James Yang
- Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409
| |
Collapse
|
5
|
Ebers MR, Rosenberg MC, Kutz JN, Steele KM. A machine learning approach to quantify individual gait responses to ankle exoskeletons. J Biomech 2023; 157:111695. [PMID: 37406604 DOI: 10.1016/j.jbiomech.2023.111695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023]
Abstract
Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.
Collapse
Affiliation(s)
- Megan R Ebers
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
| | - Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
6
|
Kesar T. The Effects of Stroke and Stroke Gait Rehabilitation on Behavioral and Neurophysiological Outcomes:: Challenges and Opportunities for Future Research. Dela J Public Health 2023; 9:76-81. [PMID: 37701480 PMCID: PMC10494801 DOI: 10.32481/djph.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
Stroke continues to be a leading cause of adult disability, contributing to immense healthcare costs. Even after discharge from rehabilitation, post-stroke individuals continue to have persistent gait impairments, which in turn adversely affect functional mobility and quality of life. Multiple factors, including biomechanics, energy cost, psychosocial variables, as well as the physiological function of corticospinal neural pathways influence stroke gait function and training-induced gait improvements. As a step toward addressing this challenge, the objective of the current perspective paper is to outline knowledge gaps pertinent to the measurement and retraining of stroke gait dysfunction. The paper also has recommendations for future research directions to address important knowledge gaps, especially related to the measurement and rehabilitation-induced modulation of biomechanical and neural processes underlying stroke gait dysfunction. We posit that there is a need for leveraging emerging technologies to develop innovative, comprehensive, methods to measure gait patterns quantitatively, to provide clinicians with objective measure of gait quality that can supplement conventional clinical outcomes of walking function. Additionally, we posit that there is a need for more research on how the stroke lesion affects multiple parts of the nervous system, and to understand the neuroplasticity correlates of gait training and gait recovery. Multi-modal clinical research studies that can combine clinical, biomechanical, neural, and computational modeling data provide promise for gaining new information about stroke gait dysfunction as well as the multitude of factors affecting recovery and treatment response in people with post-stroke hemiparesis.
Collapse
Affiliation(s)
- Trisha Kesar
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine
| |
Collapse
|
7
|
Ebers MR, Rosenberg MC, Kutz JN, Steele KM. A machine learning approach to quantify individual gait responses to ankle exoskeletons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524757. [PMID: 36711530 PMCID: PMC9882260 DOI: 10.1101/2023.01.20.524757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton ( Exo ) gait, and (iii) the Discrepancy ( i.e. , response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R 2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), ( p < 0.042)) than the Nominal model (median R 2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R 2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.
Collapse
Affiliation(s)
- Megan R Ebers
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
8
|
Li G, Ao D, Vega MM, Shourijeh MS, Zandiyeh P, Chang SH, Lewis VO, Dunbar NJ, Babazadeh-Naseri A, Baines AJ, Fregly BJ. A computational method for estimating trunk muscle activations during gait using lower extremity muscle synergies. Front Bioeng Biotechnol 2022; 10:964359. [PMID: 36582837 PMCID: PMC9792665 DOI: 10.3389/fbioe.2022.964359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
One of the surgical treatments for pelvic sarcoma is the restoration of hip function with a custom pelvic prosthesis after cancerous tumor removal. The orthopedic oncologist and orthopedic implant company must make numerous often subjective decisions regarding the design of the pelvic surgery and custom pelvic prosthesis. Using personalized musculoskeletal computer models to predict post-surgery walking function and custom pelvic prosthesis loading is an emerging method for making surgical and custom prosthesis design decisions in a more objective manner. Such predictions would necessitate the estimation of forces generated by muscles spanning the lower trunk and all joints of the lower extremities. However, estimating trunk and leg muscle forces simultaneously during walking based on electromyography (EMG) data remains challenging due to the limited number of EMG channels typically used for measurement of leg muscle activity. This study developed a computational method for estimating unmeasured trunk muscle activations during walking using lower extremity muscle synergies. To facilitate the calibration of an EMG-driven model and the estimation of leg muscle activations, EMG data were collected from each leg. Using non-negative matrix factorization, muscle synergies were extracted from activations of leg muscles. On the basis of previous studies, it was hypothesized that the time-varying synergy activations were shared between the trunk and leg muscles. The synergy weights required to reconstruct the trunk muscle activations were determined through optimization. The accuracy of the synergy-based method was dependent on the number of synergies and optimization formulation. With seven synergies and an increased level of activation minimization, the estimated activations of the erector spinae were strongly correlated with their measured activity. This study created a custom full-body model by combining two existing musculoskeletal models. The model was further modified and heavily personalized to represent various aspects of the pelvic sarcoma patient, all of which contributed to the estimation of trunk muscle activations. This proposed method can facilitate the prediction of post-surgery walking function and pelvic prosthesis loading, as well as provide objective evaluations for surgical and prosthesis design decisions.
Collapse
Affiliation(s)
- Geng Li
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Payam Zandiyeh
- Biomotion Laboratory, Department of Orthopaedic Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shuo-Hsiu Chang
- Department of Physical Medicine and Rehabilitation, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States,Neurorecovery Research Center, TIRR Memorial Hermann, Houston, TX, United States
| | - Valerae O. Lewis
- Department of Orthopaedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Nicholas J. Dunbar
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Ata Babazadeh-Naseri
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Andrew J. Baines
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States,*Correspondence: Benjamin J. Fregly,
| |
Collapse
|
9
|
Vega MM, Li G, Shourijeh MS, Ao D, Weinschenk RC, Patten C, Font-Llagunes JM, Lewis VO, Fregly BJ. Computational evaluation of psoas muscle influence on walking function following internal hemipelvectomy with reconstruction. Front Bioeng Biotechnol 2022; 10:855870. [PMID: 36246391 PMCID: PMC9559731 DOI: 10.3389/fbioe.2022.855870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient's post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.
Collapse
Affiliation(s)
- Marleny M. Vega
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Geng Li
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Robert C. Weinschenk
- Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, UC Davis School of Medicine, Sacramento, CA, United States
- UC Davis Center for Neuroengineering and Medicine, University of California, Davis, CA, United States
- VA Northern California Health Care System, Martinez, CA, United States
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Valerae O. Lewis
- Department of Orthopaedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| |
Collapse
|
10
|
Ao D, Vega MM, Shourijeh MS, Patten C, Fregly BJ. EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation. Front Bioeng Biotechnol 2022; 10:962959. [PMID: 36159690 PMCID: PMC9490010 DOI: 10.3389/fbioe.2022.962959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called "synergy extrapolation" or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were "unmeasured" for walking datasets collected from two individuals post-stroke-one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.
Collapse
Affiliation(s)
- Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, VA Northern California Health Care System, Martinez, CA, United States
- Department of Physical Medicine and Rehabilitation, Davis School of Medicine, University of California, Sacramento, CA, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| |
Collapse
|
11
|
Febrer-Nafría M, Fregly BJ, Font-Llagunes JM. Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury. Front Neurorobot 2022; 15:748148. [PMID: 35140596 PMCID: PMC8818856 DOI: 10.3389/fnbot.2021.748148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.
Collapse
Affiliation(s)
- Míriam Febrer-Nafría
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Benjamin J Fregly
- Deptartment of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Josep M Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| |
Collapse
|
12
|
Hase K. Current perspectives on quantitative gait analysis for patients with hemiparesis. JAPANESE JOURNAL OF COMPREHENSIVE REHABILITATION SCIENCE 2022; 13:1-3. [PMID: 37859851 PMCID: PMC10545024 DOI: 10.11336/jjcrs.13.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/01/2021] [Indexed: 10/21/2023]
Abstract
Hase K. Current perspectives on quantitative gait analysis for patients with hemiparesis. Jpn J Compr Rehabil Sci 2022; 13: 1-3.
Collapse
Affiliation(s)
- Kimitaka Hase
- Department of Physical Medicine and Rehabilitation, Kansai Medical University, Osaka, Japan
| |
Collapse
|
13
|
Zhao J, Yu Y, Wang X, Ma S, Sheng X, Zhu X. A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements. J Neural Eng 2022; 19. [PMID: 34986472 DOI: 10.1088/1741-2552/ac4851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. APPROACH Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. MAIN RESULTS The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. SIGNIFICANCE The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.
Collapse
Affiliation(s)
- Jiamin Zhao
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Yang Yu
- Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan RD. Minhang District, Shanghai, 200240, CHINA
| | - Xu Wang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Shihan Ma
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Xinjun Sheng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Xiangyang Zhu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| |
Collapse
|
14
|
Koelewijn AD, Audu M, del-Ama AJ, Colucci A, Font-Llagunes JM, Gogeascoechea A, Hnat SK, Makowski N, Moreno JC, Nandor M, Quinn R, Reichenbach M, Reyes RD, Sartori M, Soekadar S, Triolo RJ, Vermehren M, Wenger C, Yavuz US, Fey D, Beckerle P. Adaptation Strategies for Personalized Gait Neuroprosthetics. Front Neurorobot 2021; 15:750519. [PMID: 34975445 PMCID: PMC8716811 DOI: 10.3389/fnbot.2021.750519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.
Collapse
Affiliation(s)
- Anne D. Koelewijn
- Biomechanical Data Analysis and Creation (BIOMAC) Group, Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Musa Audu
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Antonio J. del-Ama
- Applied Mathematics, Materials Science and Technology and Electronic Technology Department, Rey Juan Carlos University, Mostoles, Spain
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Antonio Gogeascoechea
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Sandra K. Hnat
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Nathan Makowski
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center, Cleveland, OH, United States
| | - Juan C. Moreno
- Neural Rehabilitation Group, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | - Mark Nandor
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Roger Quinn
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marc Reichenbach
- Chair of Computer Engineering, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ryan-David Reyes
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Massimo Sartori
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Surjo Soekadar
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Ronald J. Triolo
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mareike Vermehren
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Christian Wenger
- IHP-Leibniz Institut Fuer Innovative Mikroelektronik, Frankfurt (Oder), Germany
| | - Utku S. Yavuz
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Dietmar Fey
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Beckerle
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| |
Collapse
|
15
|
Myoelectric control and neuromusculoskeletal modeling: Complementary technologies for rehabilitation robotics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
16
|
A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11052037] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.
Collapse
|
17
|
Ao D, Shourijeh MS, Patten C, Fregly BJ. Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies. Front Comput Neurosci 2020; 14:588943. [PMID: 33343322 PMCID: PMC7746870 DOI: 10.3389/fncom.2020.588943] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/09/2020] [Indexed: 12/14/2022] Open
Abstract
Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called "synergy extrapolation" or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.
Collapse
Affiliation(s)
- Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, VA Northern California Health Care System, Martinez, CA, United States
- Department of Physical Medicine and Rehabilitation, Davis School of Medicine, University of California, Sacramento, CA, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| |
Collapse
|
18
|
Arones MM, Shourijeh MS, Patten C, Fregly BJ. Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking. Front Bioeng Biotechnol 2020; 8:588925. [PMID: 33324623 PMCID: PMC7725798 DOI: 10.3389/fbioe.2020.588925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022] Open
Abstract
Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject's CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.
Collapse
Affiliation(s)
- Marleny M. Arones
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | | | - Carolynn Patten
- Department of Physical Medicine and Rehabilitation, University of California, Davis, Davis, CA, United States
| | - Benjamin J. Fregly
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
| |
Collapse
|