Li Sujiao, Xiang Shuhan, Ma Qiqi, Cai Wenqian, Liu Suiyi, Fang Fanfu, Yu Hongliu
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China.
Shanghai Engineering Research Center of Assistive Devices, Shanghai, China.
Front Bioeng Biotechnol. 2024 Apr 24;12:1400912. doi: 10.3389/fbioe.2024.1400912. eCollection 2024.
The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.
康复机器人可以有效协助偏瘫患者完成训练计划，但它仅专注于帮助患者的训练过程，且需要康复治疗师根据患者的病情手动调整训练参数。因此，迫切需要开展康复机器人的智能训练处方研究以促进其临床应用。本研究提出了一种基于规则推理（RBR）和案例推理（CBR）的混合推理的上肢康复机器人训练决策支持系统。该系统的专家知识库基于上海三个不同康复科室的10名专业康复治疗师建立，他们在使用桌面式上肢康复机器人方面经验丰富。选择基于规则的推理来构建周期计划推理模型，为患者制定21天的训练计划。案例库由54名接受桌面式上肢康复机器人康复训练的中风患者的历史病例数据组成。构建了结合随机森林优化算法的案例推理，以实时为患者调整训练参数。该系统推荐的康复训练方案平均准确率为91.5%，平均AUC值为0.924，平均召回率为88.7%，平均F1分数为90.1%。该系统在康复机器人中的应用对治疗师将很有帮助。
