Institute of Automatic Control, Lodz University of Technology, Stefanowskiego 18, 90-537 Lodz, Poland.
Miejskie Centrum Medyczne im. dr Karola Jonschera, Milionowa 14, 93-113 Lodz, Poland.
Sensors (Basel). 2022 Mar 21;22(6):2414. doi: 10.3390/s22062414.
Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2-a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.
患有瘫痪或偏瘫手的中风患者需要经常进行锻炼，以促进神经可塑性并改善手部关节活动度。现有的手部锻炼设备旨在为具有一定手部控制能力的人提供服务，或提供有限患者参与的连续被动运动。患者可以通过自我锻炼受益，即用另一只手锻炼瘫痪或偏瘫的手。然而，中风后的神经心理并发症、冷漠和认知障碍（如健忘）使得定期自我锻炼变得困难。本文介绍了 Przypominajka v2-a 系统，旨在支持自我锻炼、提醒锻炼并激励患者。我们提出了一种基于手套的设备，具有基于设备的机器学习的锻炼评分、基于平板电脑的界面和基于网络的治疗师应用程序。我们在四名健康参与者上评估了设备上推断的可行性和正确锻炼分类的准确性。在手患有偏瘫的患者的案例研究中描述了整个系统的使用情况。异常分类的准确性为 91.3%，F1 值为 91.6%，但对于新用户的效果较差（分别为 78%和 81%）。案例研究表明，患者对使用 Przypominajka 进行锻炼有积极的反应，但传感器手套存在一些问题：易于穿戴和说明清晰。本文提出了一种新的方法，即传感器系统可以通过基于设备的机器学习分类来支持中风后患者的康复，这种分类可以准确地评分并有助于激励患者。
