Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning
Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning
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DOI：
10.1109/access.2021.3055960
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Lv, Zhihan
中科院分区：
文献类型：
--
作者：
Miao, Sheng;Shen, Chen;Lv, Zhihan
目前，脑卒中幸存者的康复训练主要是在医生的指导下完成的。治疗方法多种多样，但大多受医师经验、训练强度等多种因素的影响。治疗效果不能及时反馈，缺乏客观的评价数据。此外，治疗方法复杂、昂贵，而且高度依赖医生。此外，中风幸存者的依从性较差，这导致了各种限制。本文结合物联网、机器学习和智能系统技术，设计了一种基于智能手机的智能系统，帮助中风幸存者改善上肢康复。通过智能手机内置的多模态传感器，可以获取用户的训练动作数据，然后通过互联网传输到服务器。本研究提出了一种DTW-KNN联合算法来识别康复动作的准确性，并对多个训练完成程度进行分类。实验结果表明，DTW-KNN算法可以对康复动作进行评价，分类中优、良、正常的准确率分别为85.7%、66.7%和80%。本文提出的智能系统可以帮助中风幸存者独立远程进行康复训练，降低医疗费用和心理负担。
Nowadays, rehabilitation training for stroke survivors is mainly completed under the guidance of the physician. There are various treatment ways, however, most of them are affected by various factors such as experience of physician and training intensity. The treatment effect cannot be fed back in time, and objective evaluation data is lacking. In addition, the treatment method is complicated, costly, and highly dependent on physicians. Moreover, stroke survivors' compliance is poor, which leads to various limitations. This paper combines the Internet-of-Things, machine learning, and intelligence system technologies to design a smartphone-based intelligence system to help stroke survivors to improve upper limb rehabilitation. With the built-in multi-modal sensors of the smart phone, training action data of users can be obtained, and then transfer to the server through the Internet. This research presents a DTW-KNN joint algorithm to recognize accuracy of rehabilitation actions and classify to multiple training completion levels. The experimental results show that the DTW-KNN algorithm can evaluate the rehabilitation actions, the accuracy rates of the classification in excellent, good, and normal are 85.7%, 66.7%, and 80% respectively. The intelligence system presented in this paper can help stroke survivors to proceed rehabilitation training independently and remotely, which reduces medical costs and psychological burden.