Song Xinyu, van de Ven Shirdi Shankara, Chen Shugeng, Kang Peiqi, Gao Qinghua, Jia Jie, Shull Peter B
The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
The Department of Rehabilitation Medicine, The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Front Physiol. 2022 Jun 3;13:811950. doi: 10.3389/fphys.2022.811950. eCollection 2022.
Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user's affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.
中风常导致手部运动功能障碍，有效的康复需要让患者保持参与度和积极性。在现有的自动化康复方法中，基于数据手套的系统由于痉挛，患者佩戴起来并不容易，而基于单一传感器的方法通常提供的信息极为有限。因此，我们提出一种用于中风后手运动训练的可穿戴多模态严肃游戏方法。我们提出了一种基于肌动图（FMG）、肌电图（EMG）和惯性测量单元（IMU）的多传感器融合模型，用于手部运动分类，该模型佩戴在用户受影响的手臂上。开发了两款基于运动识别的严肃游戏，用于手部运动和认知训练。十名轻度至中度运动障碍（手部Brunnstrom分期为II - VI期）的中风患者在玩交互式严肃游戏时进行了实验，这些游戏需要进行从Fugl Meyer评估中选取的12项日常生活活动（ADL）手部动作。通过运动分类准确率和患者定性问卷对可行性进行了评估。对于这12种动作，使用FMG - EMG - IMU组合的离线分类准确率为81.0%，显著高于任何单一传感方式；仅EMG、仅FMG和仅IMU的准确率分别为69.6%、63.2%和47.8%。患者报告称，与传统方法相比，他们在玩严肃游戏时对手部运动训练更有热情，并强烈同意他们主观上认为所提出的训练对改善上肢运动功能有益。这些结果表明，多模态传感器融合提高了中风患者手部姿势分类的准确率，并证明了这种方法作为中风后上肢运动训练的潜力。
