用肌电与机器学习打造低成本假手，识别五种手势并实现触觉反馈。
Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning
- 通过三块肌肉的肌电信号，用浅层神经网络分类手势。
- 时域分析达97.25%准确率，响应更快，用于实际控制。
- 新增手腕旋转与触觉反馈，提升假手功能与使用体验。
肌电图（EMG）是测量肌肉电活动的技术，广泛应用于临床与人机交互。肌电假肢通过分析残肢的电信号并进行分类，进而控制机械手运动。本项目旨在开发一种适合发展中国家截肢者的低成本、高效肌电假手。该假手需基于三块肌肉的EMG信号，准确识别五种手势，并控制机器人手执行动作。机械手由两个舵机驱动，具备两个自由度。在建立高效的信号采集与放大系统后，对EMG信号进行了时域与频域分析，提取双域特征，并训练浅层神经网络。结果表明，时域与频域分类平均准确率分别为97.25%和95.85%。时域分析计算更快，被选为分类系统。设计并测试了手腕旋转机构，由其中两个手势分别控制，增加第三个自由度。最后，开发了包含力传感器与振动马达的触觉反馈系统，使用户能感知手部受力情况。
原文摘要 · Abstract (English)
Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer interaction. Myo-electric prosthetics analyze and classify the electrical signals recorded from the residual limb. The classified output is then used to control the position of motors in a robotic hand and a movement is produced. The aim of this project is to develop a low-cost and effective myo-electric prosthetic hand that would meet the needs of amputees in developing countries. The proposed prosthetic hand should be able to accurately classify five different patterns (gestures) using EMG recordings from three muscles and control a robotic hand accordingly. The robotic hand is composed of two servo motors allowing for two degrees of freedom. After establishing an efficient signal acquisition and amplification system, EMG signals were thoroughly analyzed in the frequency and time domain. Features were extracted from both domains and a shallow neural network was trained on the two sets of data. Results yielded an average classification accuracy of 97.25% and 95.85% for the time and frequency domains respectively. Furthermore, results showed a faster computation and response for the time domain analysis; hence, it was adopted for the classification system. A wrist rotation mechanism was designed and tested to add significant functionality to the prosthetic. The mechanism is controlled by two of the five gestures, one for each direction. Which added a third degree of freedom to the overall design. Finally, a tactile sensory feedback system which uses force sensors and vibration motors was developed to enable sensation of the force inflicted on the hand for the user.
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