用手臂肌电和多传感器融合控制机器人，实现智能家居辅助的精准操作。
Multi-Sensor Fusion-Based Mobile Manipulator Remote Control for Intelligent Smart Home Assistance
- 用卷积-循环网络分析肌电信号，实时转换手势意图。
- 抓取与转移任务成功率91.1%，轨迹偏差仅3.6厘米。
- 适合残障人士或老人使用，交互直观且响应快。
本文提出一种可穿戴式移动操作机器人远程控制系统，用于智能家庭辅助。系统集成MEMS电容麦克风、惯性测量单元（IMU）、振动马达和压力反馈，捕捉前臂肌肉活动并转化为实时操控信号。可穿戴设备通过CNN-LSTM模型对六类手部动作-力度组合进行离线分类，准确率达88.33%；五名参与者实测中，系统实际准确率为83.33%，平均响应时间1.2秒。在人机协同导航与抓取任务中，机器人任务成功率达98%，平均轨迹偏差仅为3.6厘米。在物体抓取与传送测试中，系统抓取成功率93.3%，传送成功率95.6%，全任务成功率91.1%，共评估9种不同材质组合。三组实验验证了基于MEMS的可穿戴传感与多传感器融合在智能家居场景下可靠、直观的助老/助残机器人控制有效性。
原文摘要 · Abstract (English)
This paper proposes a wearable-controlled mobile manipulator system for intelligent smart home assistance, integrating MEMS capacitive microphones, IMU sensors, vibration motors, and pressure feedback to enhance human-robot interaction. The wearable device captures forearm muscle activity and converts it into real-time control signals for mobile manipulation. The wearable device achieves an offline classification accuracy of 88.33\%\ across six distinct movement-force classes for hand gestures by using a CNN-LSTM model, while real-world experiments involving five participants yield a practical accuracy of 83.33\%\ with an average system response time of 1.2 seconds. In Human-Robot synergy in navigation and grasping tasks, the robot achieved a 98\%\ task success rate with an average trajectory deviation of only 3.6 cm. Finally, the wearable-controlled mobile manipulator system achieved a 93.3\%\ gripping success rate, a transfer success of 95.6\%\, and a full-task success rate of 91.1\%\ during object grasping and transfer tests, in which a total of 9 object-texture combinations were evaluated. These three experiments' results validate the effectiveness of MEMS-based wearable sensing combined with multi-sensor fusion for reliable and intuitive control of assistive robots in smart home scenarios.
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