Espinoza Bernal Victor C, Hiremath Shivayogi V, Wolf Bethany, Riley Brooke, Mendonca Rochelle J, Johnson Michelle J
Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, USA.
Director of Personal Health Informatics & Rehabilitation Engineering Laboratory, Temple University, Philadelphia, PA, USA.
J Rehabil Assist Technol Eng. 2021 Oct 7;8:20556683211044640. doi: 10.1177/20556683211044640. eCollection 2021 Jan-Dec.
Stroke is the leading cause of disability worldwide. It has been well-documented that rehabilitation (rehab) therapy can aid in regaining health and function for individuals with stroke. Yet, tracking in-home rehab continues to be a challenge because of a lack of resources and population-scale demands. In order to address this gap, we implemented a methodology to classify and track rehab interventions in individuals with stroke.
We developed personalized classification algorithms, including neural network-based algorithms, to classify four rehab exercises performed by two individuals with stroke who were part of a week-long therapy camp in Jamaica, a low- and middle-income country. Accelerometry-based wearable sensors were placed on each upper and lower limb to collect movement data during therapy.
The classification accuracy for traditional and neural network-based algorithms utilizing feature data (e.g., number of peaks) from the sensors ranged from 64 to 94%, respectively. In addition, the study proposes a new method to assess change in bilateral mobility over the camp duration.
The results of this pilot study indicate that personalized supervised learning algorithms can be used to classify and track rehab activities and functional outcomes in resource limited settings such as LMICs.
中风是全球致残的主要原因。已有充分文献证明，康复治疗有助于中风患者恢复健康和功能。然而，由于缺乏资源和大规模人群需求，对居家康复的跟踪仍然是一项挑战。为了弥补这一差距，我们实施了一种方法来对中风患者的康复干预进行分类和跟踪。
我们开发了个性化分类算法，包括基于神经网络的算法，以对两名中风患者进行的四项康复锻炼进行分类，这两名患者参加了在牙买加（一个低收入和中等收入国家）举办的为期一周的治疗营。在治疗期间，基于加速度计的可穿戴传感器被放置在每个上肢和下肢上，以收集运动数据。
利用传感器的特征数据（如峰值数量）的传统算法和基于神经网络的算法的分类准确率分别为64%至94%。此外，该研究提出了一种新方法来评估在营地期间双侧活动能力的变化。
这项初步研究的结果表明，个性化监督学习算法可用于在低收入和中等收入国家等资源有限的环境中对康复活动和功能结果进行分类和跟踪。
