Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Department of Design, Production and Management, University of Twente, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2023 Mar 20;23(6):3281. doi: 10.3390/s23063281.
This article presents the system architecture and validation of the NeuroSuitUp body-machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke.
The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires.
Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported.
Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.
本文介绍了 NeuroSuitUp 人机接口 (BMI) 的系统架构和验证。该平台由可穿戴机器人夹克和手套以及一个严肃游戏应用程序组成，用于脊髓损伤和慢性中风的自我调节神经康复。
可穿戴机器人实施了传感器层，以近似运动链节的方向，以及一个驱动层。传感器包括商用磁、角速率和重力 (MARG)、表面肌电图 (sEMG) 和柔性传感器，而驱动则通过电肌肉刺激 (EMS) 和气动执行器来实现。板载电子设备连接到基于机器人操作系统的解析器/控制器和基于 Unity 的实时化身表示游戏。通过立体相机计算机视觉方法对夹克进行验证，通过多项抓握活动对手套进行验证，对 BMI 子系统进行了验证。十名健康受试者参加了系统验证试验，完成了三个手臂和三个手部练习（每个练习 10 次运动任务）并完成了用户体验问卷。
在使用夹克进行的 23/30 个手臂练习中观察到了可接受的相关性。在驱动状态下，手套传感器数据没有显著差异。没有报告使用困难、不适或对机器人的负面看法。
后续的设计改进将实施额外的绝对取向传感器、基于 MARG/EMG 的游戏生物反馈、通过增强现实提高沉浸感以及提高系统鲁棒性。
