Identification of Mechanical Impedance Parameters of Human Upper Limbs Using Mechanical Perturbation Method

Zou, Huaiwu; Tao, Haoran; Zhou, Zhou; Hu, Bingshan

doi:10.1007/978-981-16-5963-8_20

Cited by 2 publications

(1 citation statement)

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“…Therefore, to complement the intrinsic safety characteristics of a rehabilitation device [e.g., electromechanical properties ( Mehrholz et al, 2018 ; Bessler et al, 2021 )], human-robot interaction could be exploited to implement monitoring strategies that study changes in patients’ motor control during rehabilitation. Robotic devices can be used to accurately estimate changes in muscle tone (or its manifestation in terms of change in joint stiffness) during limb mobilization ( Artemiadis et al, 2010 ; Tucker et al, 2013 ; Dehem et al, 2017 ; Ranzani et al, 2019 ; Zou et al, 2021 ; van der Velden et al, 2022 ). Most frequently, to do this, position- or velocity-controlled perturbations are applied to the passive limb while force sensors measure the reaction force/torque of joint and muscles during the movement, which can be used to quantify muscle tone and limb biomechanics, as well as spasticity when different perturbation speeds are considered ( De-la-Torre et al, 2020 ; Guo et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

An online method to monitor hand muscle tone during robot-assisted rehabilitation

et al. 2023

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Introduction: Robot-assisted neurorehabilitation is becoming an established method to complement conventional therapy after stroke and provide intensive therapy regimes in unsupervised settings (e.g., home rehabilitation). Intensive therapies may temporarily contribute to increasing muscle tone and spasticity, especially in stroke patients presenting tone alterations. If sustained without supervision, such an increase in muscle tone could have negative effects (e.g., functional disability, pain). We propose an online perturbation-based method that monitors finger muscle tone during unsupervised robot-assisted hand therapy exercises.Methods: We used the ReHandyBot, a novel 2 degrees of freedom (DOF) haptic device to perform robot-assisted therapy exercises training hand grasping (i.e., flexion-extension of the fingers) and forearm pronosupination. The tone estimation method consisted of fast (150 ms) and slow (250 ms) 20 mm ramp-and-hold perturbations on the grasping DOF, which were applied during the exercises to stretch the finger flexors. The perturbation-induced peak force at the finger pads was used to compute tone. In this work, we evaluated the method performance in a stiffness identification experiment with springs (0.97 and 1.57 N/mm), which simulated the stiffness of a human hand, and in a pilot study with subjects with increased muscle tone after stroke and unimpaired, which performed one active sensorimotor exercise embedding the tone monitoring method.Results: The method accurately estimates forces with root mean square percentage errors of 3.8% and 11.3% for the soft and stiff spring, respectively. In the pilot study, six chronic ischemic stroke patients [141.8 (56.7) months after stroke, 64.3 (9.5) years old, expressed as mean (std)] and ten unimpaired subjects [59.9 (6.1) years old] were tested without adverse events. The average reaction force at the level of the fingertip during slow and fast perturbations in the exercise were respectively 10.7 (5.6) N and 13.7 (5.6) N for the patients and 5.8 (4.2) N and 6.8 (5.1) N for the unimpaired subjects.Discussion: The proposed method estimates reaction forces of physical springs accurately, and captures online increased reaction forces in persons with stroke compared to unimpaired subjects within unsupervised human-robot interactions. In the future, the identified range of muscle tone increase after stroke could be used to customize therapy for each subject and maintain safety during intensive robot-assisted rehabilitation.

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Section: Introductionmentioning

confidence: 99%

An online method to monitor hand muscle tone during robot-assisted rehabilitation

et al. 2023

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Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Scibilia

Pedrocchi

Fortuna

2022

Sensors

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The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

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Identification of Mechanical Impedance Parameters of Human Upper Limbs Using Mechanical Perturbation Method

Cited by 2 publications

References 6 publications

An online method to monitor hand muscle tone during robot-assisted rehabilitation

An online method to monitor hand muscle tone during robot-assisted rehabilitation

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

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