First prototype of EMG-controlled power hand orthosis for restoring hand extension in stroke patients

Abstract

Weakness in finger extensors is a common post-stroke deficit that can disturb hand functioning. Despite introducing several powered hand orthoses in literature, most of these devices focused on providing finger flexion. There is a little consideration for providing active hand extension in stroke patients. Moreover, in many devices, the finger extensions were restored passively by spring component. In this study, a new Electromyography (EMG)-controlled powered hand orthosis was designed to improve hand function by restoring and training hand extension in stroke patients with paretic hand. This orthosis was a glove-like device that was developed from two mechanical and electrical sections. After construction and verifying of the orthosis, its applicability was tested on two patients with Cerebrovascular accident (1 woman and 1 man) with paretic hands in an 18-session therapeutic approach. To evaluate the effectiveness of orthosis, Wolf Motor Function Test and Box and Block test were conducted before and after the training sessions. The primary assessment of the prototype was conducted on a healthy subject and three stroke patients. These evaluations showed that the new powered hand orthosis could be effective for finger extension task and training. Furthermore, after the 18-session training approach, significant improvements were seen in the scores of both Wolf and Box and Block tests. The preliminary findings suggested that the first prototype of orthosis could provide a desirable function for stroke patients with paretic hand. Moreover, it could be used as a training device in the rehabilitation of these patients.

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

“…Purely binary or proportional controllers were used to control hand orthoses (DiCicco et al, 2004 ; Fujita et al, 2016 ; Dunaway et al, 2017 ; Lince et al, 2017 ; Yap et al, 2017a ; Fardipour et al, 2018 ; Wang et al, 2018 ; Gerez et al, 2019 , 2020 ; Yoo et al, 2019 ; Bos et al, 2020 ; Nam et al, 2020 ; Yurkewich et al, 2020a ), and wrist (Yoo et al, 2019 ; Lambelet et al, 2020 ; Nam et al, 2020 ), elbow (Ambrosini et al, 2014a ; Bermúdez i Badia et al, 2014 ; Fujita et al, 2016 ; Dunaway et al, 2017 ; Koh et al, 2017 ; Nam et al, 2020 ), or shoulder orthoses (Ambrosini et al, 2014a ; Fujita et al, 2016 ; Scheuner et al, 2016 ; Zhou et al, 2021 ). Three studies did not provide unambiguous information about the used control method (Pedrocchi et al, 2013 ; Mohammadi et al, 2018 ; Rose and O'Malley, 2019 ).…”

“…In 21 studies, conventional manual triggers such as buttons/switches (Ochoa et al, 2009 ; Pedrocchi et al, 2013 ; Ambrosini et al, 2014a ; Yap et al, 2016 , 2017a ; Meeker et al, 2017 ; Fardipour et al, 2018 ; Otten et al, 2018 ; Butzer et al, 2019 , 2021 ; Farinha et al, 2019 ; Correia et al, 2020 ; Gerez et al, 2020 ; Muehlbauer et al, 2021 ), joysticks (Hasegawa and Oura, 2011 ; Dalla Gasperina et al, 2019 ; Ismail et al, 2019 ; Tiseni et al, 2019 ), or touchscreens (Yap et al, 2017b ; Mohammadi et al, 2018 ; Sandison et al, 2020 ) have been used.…”

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

“…With adequate spatial resolution and a proper EMG pattern recognition pipeline, motions can be deciphered with remarkably high accuracy (>90% accuracy). Myoelectric control has been used in a variety of human-computer interfaces such as upper-limb prostheses or orthoses [1,2], electric wheelchairs [3], muscle-derived speech decoding devices [4,5], virtual reality control devices [6] and other clinical and consumer device designs [7]. While myoelectric control has been touted for decades as an intuitive means of control for assistive-devices, performance of these devices in daily living conditions has been notably inferior to benchmarks achieved in controlled laboratory environments.…”

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

“…With adequate spatial resolution and a proper EMG pattern recognition pipeline, motions can be deciphered with remarkably high accuracy (>90% accuracy). Myoelectric control has been used in a variety of human-computer interfaces such as electric wheelchairs [1], orthoses [2,3], muscle-derived speech decoding devices [4,5], virtual reality control devices [6] and other clinical and consumer device designs [7]. While myoelectric control has been touted for decades as an intuitive means of control for assistive-devices, performance of these devices in daily living conditions has been notably inferior to benchmarks achieved in controlled laboratory environments.…”