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Tanzarella S, Di Domenico D, Forsiuk I, Boccardo N, Chiappalone M, Bartolozzi C, Semprini M. Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies. J Neural Eng 2024; 21:026043. [PMID: 38547534 DOI: 10.1088/1741-2552/ad38dd] [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: 09/06/2023] [Accepted: 03/28/2024] [Indexed: 04/16/2024]
Abstract
Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.
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Affiliation(s)
- Simone Tanzarella
- Event-Driven Perception, Italian Institute of Technology, Via San Quirico, 19, 16163 Genova, GE, Italy
| | - Dario Di Domenico
- Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin 10124, Italy
| | - Inna Forsiuk
- Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy
| | - Nicolò Boccardo
- Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy
- Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Genova, Italy
| | - Michela Chiappalone
- Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy
- Bioengineering Lab, University of Genova, DIBRIS, Genova, Italy
| | - Chiara Bartolozzi
- Event-Driven Perception, Italian Institute of Technology, Via San Quirico, 19, 16163 Genova, GE, Italy
| | - Marianna Semprini
- Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy
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Chen Y, Li T, Wang Z, Yan Z, De Vita R, Tan T. A Metamaterial Computational Multi-Sensor of Grip-Strength Properties with Point-of-Care Human-Computer Interaction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304091. [PMID: 37818760 DOI: 10.1002/advs.202304091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/11/2023] [Indexed: 10/13/2023]
Abstract
Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point-of-care device for comprehensive multi-sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human-computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real-time interactive point-of-care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point-of-care biomechanical platform and advancing the human-computer interactive healthcare, ultimately contributing to a global health ecosystem.
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Affiliation(s)
- Yinghua Chen
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Tianrun Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhemin Wang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhimiao Yan
- State Key Laboratory of Ocean Engineering, Department of Mechanics, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Raffaella De Vita
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Ting Tan
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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Cheng HJ, Chin LF, Kanzler CM, Lehner R, Kuah CWK, Kager S, Josse E, Samkharadze T, Sidarta A, Gonzalez PC, Lie E, Zbytniewska-Mégret M, Wee SK, Liang P, Gassert R, Chua K, Lambercy O, Wenderoth N. Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform. Front Neurol 2023; 14:1246888. [PMID: 38107648 PMCID: PMC10722087 DOI: 10.3389/fneur.2023.1246888] [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: 06/24/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023] Open
Abstract
Background Stroke is a leading cause of lifelong disability worldwide, partially driven by a reduced ability to use the upper limb in daily life causing increased dependence on caregivers. However, post-stroke functional impairments have only been investigated using limited clinical scores, during short-term longitudinal studies in relatively small patient cohorts. With the addition of technology-based assessments, we propose to complement clinical assessments with more sensitive and objective measures that could more holistically inform on upper limb impairment recovery after stroke, its impact on upper limb use in daily life, and on overall quality of life. This paper describes a pragmatic, longitudinal, observational study protocol aiming to gather a uniquely rich multimodal database to comprehensively describe the time course of upper limb recovery in a representative cohort of 400 Asian adults after stroke. Particularly, we will characterize the longitudinal relationship between upper limb recovery, common post-stroke impairments, functional independence and quality of life. Methods Participants with stroke will be tested at up to eight time points, from within a month to 3 years post-stroke, to capture the influence of transitioning from hospital to community settings. We will perform a battery of established clinical assessments to describe the factors most likely to influence upper limb recovery. Further, we will gather digital health biomarkers from robotic or wearable sensing technology-assisted assessments to sensitively characterize motor and somatosensory impairments and upper limb use in daily life. We will also use both quantitative and qualitative measures to understand health-related quality of life. Lastly, we will describe neurophysiological motor status using transcranial magnetic stimulation. Statistics Descriptive analyses will be first performed to understand post-stroke upper limb impairments and recovery at various time points. The relationships between digital biomarkers and various domains will be explored to inform key aspects of upper limb recovery and its dynamics using correlation matrices. Multiple statistical models will be constructed to characterize the time course of upper limb recovery post-stroke. Subgroups of stroke survivors exhibiting distinct recovery profiles will be identified. Conclusion This is the first study complementing clinical assessments with technology-assisted digital biomarkers to investigate upper limb sensorimotor recovery in Asian stroke survivors. Overall, this study will yield a multimodal data set that longitudinally characterizes post-stroke upper limb recovery in functional impairments, daily-life upper limb use, and health-related quality of life in a large cohort of Asian stroke survivors. This data set generates valuable information on post-stroke upper limb recovery and potentially allows researchers to identify different recovery profiles of subgroups of Asian stroke survivors. This enables the comparisons between the characteristics and recovery profiles of stroke survivors in different regions. Thus, this study lays out the basis to identify early predictors for upper limb recovery, inform clinical decision-making in Asian stroke survivors and establish tailored therapy programs. Clinical trial registration ClinicalTrials.gov, identifier: NCT05322837.
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Affiliation(s)
- Hsiao-Ju Cheng
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
| | - Lay Fong Chin
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Institute of Rehabilitation Excellence (IREx), Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
| | - Christoph M Kanzler
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Rea Lehner
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
| | - Christopher W K Kuah
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Institute of Rehabilitation Excellence (IREx), Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Simone Kager
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Eva Josse
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Tengiz Samkharadze
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
| | - Ananda Sidarta
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Pablo Cruz Gonzalez
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Eloise Lie
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Monika Zbytniewska-Mégret
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Seng Kwee Wee
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Institute of Rehabilitation Excellence (IREx), Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
- Singapore Institute of Technology (SIT), Singapore, Singapore
| | - Phyllis Liang
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Roger Gassert
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Karen Chua
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Institute of Rehabilitation Excellence (IREx), Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Olivier Lambercy
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | - Nicole Wenderoth
- Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
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