Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step towards the development of wearable hand robots. Electromyography(EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition towards application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.

中文翻译：

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使用肌电图和运动学实现辅助和康复机器人中的手部模式识别

可穿戴式手部机器人正在成为促进中风患者日常生活和手部康复锻炼的一种有吸引力的手段。模式识别是可穿戴手机器人发展的关键一步。肌电图（EMG）是一种常用的手部模式识别生物信号。然而，基于肌电图的模式识别在中风后辅助和康复机器人中的性能仍然不能令人满意。此外，诸如Leap Motion之类的低成本运动传感器最近被用于各种应用中的模式识别。本研究提出了特征融合和决策融合方法，将肌电图特征和运动学特征相结合，用于手部模式识别，以应用于上肢辅助和康复机器人。参与实验的十名正常受试者和五名中风后患者接受了日常活动的八种手部模式测试，同时记录肌电图和运动学。结果显示，脑卒中患者的平均手部模式识别准确率（仅肌电特征为 83%）、仅运动学特征为 84.71%、肌电和运动学特征融合为 96.43%、肌电和运动学决策融合为 91.18%。特征融合和决策融合非常稳健，因为向分类器提供了三种不同级别的噪声，导致分类精度略有下降。不同通道组合的比较表明，尽管特定肌电通道出现故障，融合分类器仍具有鲁棒性，这意味着该系统在辅助和康复机器人领域具有广阔的潜力。未来的工作将对中风幸存者进行实时模式分类。