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Muscle weakness assessment tool for automated therapy selection in elbow rehabilitation. ROBOTICA 2022. [DOI: 10.1017/s0263574722000844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Abstract
Clinical observations and subjective judgements have traditionally been used to evaluate patients with muscular and neurological disorders. As a result, identifying and analyzing functional improvements are difficult, especially in the absence of expertise. Quantitative assessment, which serves as the motivation for this study, is an essential prerequisite to forecast the task of the rehabilitation device in order to develop rehabilitation training. This work provides a quantitative assessment tool for muscle weakness in the human upper limbs for robotic-assisted rehabilitation. The goal is to map the assessment metrics to the recommended rehabilitation exercises. Measurable interaction forces and muscle correlation factors are the selected parameters to design a framework for muscular nerve cell condition detection and appropriate limb trajectory selection. In this work, a data collection setup is intended for extracting muscle intervention and assessment using MyoMeter, Goniometer and surface electromyography data for upper limbs. Force signals and human physiological response data are evaluated and categorized to infer the relevant progress. Based upon the most influencing muscles, curve fitting is performed. Trajectory-based data points are collected through a scaled geometric Open-Sim musculoskeletal model that fits the subject’s anthropometric data. These data are found to be most suitable to prescribe relevant exercise and to design customized robotic assistance. Case studies demonstrate the approach’s efficacy, including optimally synthesized automated configuration for the desired trajectory.
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Vincent M, Rossel O, Hayashibe M, Herbet G, Duffau H, Guiraud D, Bonnetblanc F. The difference between electrical microstimulation and direct electrical stimulation – towards new opportunities for innovative functional brain mapping? Rev Neurosci 2016; 27:231-58. [DOI: 10.1515/revneuro-2015-0029] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 10/17/2015] [Indexed: 11/15/2022]
Abstract
AbstractBoth electrical microstimulation (EMS) and direct electrical stimulation (DES) of the brain are used to perform functional brain mapping. EMS is applied to animal fundamental neuroscience experiments, whereas DES is performed in the operating theatre on neurosurgery patients. The objective of the present review was to shed new light on electrical stimulation techniques in brain mapping by comparing EMS and DES. There is much controversy as to whether the use of DES during wide-awake surgery is the ‘gold standard’ for studying the brain function. As part of this debate, it is sometimes wrongly assumed that EMS and DES induce similar effects in the nervous tissues and have comparable behavioural consequences. In fact, the respective stimulation parameters in EMS and DES are clearly different. More surprisingly, there is no solid biophysical rationale for setting the stimulation parameters in EMS and DES; this may be due to historical, methodological and technical constraints that have limited the experimental protocols and prompted the use of empirical methods. In contrast, the gap between EMS and DES highlights the potential for new experimental paradigms in electrical stimulation for functional brain mapping. In view of this gap and recent technical developments in stimulator design, it may now be time to move towards alternative, innovative protocols based on the functional stimulation of peripheral nerves (for which a more solid theoretical grounding exists).
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Affiliation(s)
- Marion Vincent
- 1INRIA, Université de Montpellier, LIRMM, équipe DEMAR, F-34095 Montpellier, France
| | - Olivier Rossel
- 1INRIA, Université de Montpellier, LIRMM, équipe DEMAR, F-34095 Montpellier, France
| | - Mitsuhiro Hayashibe
- 1INRIA, Université de Montpellier, LIRMM, équipe DEMAR, F-34095 Montpellier, France
| | | | | | - David Guiraud
- 1INRIA, Université de Montpellier, LIRMM, équipe DEMAR, F-34095 Montpellier, France
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