用脑启发芯片融合肌电与眼神，实现低功耗智能假手控制。
Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
- 通过脉冲神经网络在AltAi芯片上实时解析肌电信号。
- 识别准确率达95%，且避免不安全抓握动作。
- 适合需要低功耗、高安全性的假肢使用者。
本文提出一种新型类脑控制架构，用于上肢假肢，结合表面肌电（sEMG）与眼动追踪视觉识别。系统在神经形态处理器AltAi上部署脉冲神经网络，实时分类肌电信号；同时，眼动头戴设备与场景相机识别用户关注物体。原型中，原为传统GPU设计的肌电识别模型被转化为脉冲网络部署于AltAi，在亚瓦级功耗下实现与原有性能相当的识别效果，支持轻量可穿戴。针对六种功能手势，系统表现媲美现有先进肌电接口；当视觉模块将决策空间限制为三类与当前物体匹配的手势时，识别准确率提升至约95%，并排除不安全的抓握行为。结果表明，该类脑、上下文感知控制器具备节能、可靠的优势，有望提升截肢者日常活动中的安全性与可用性。
原文摘要 · Abstract (English)
This paper presents a novel neuromorphic control architecture for upper-limb prostheses that combines surface electromyography (sEMG) with gaze-guided computer vision. The system uses a spiking neural network deployed on the neuromorphic processor AltAi to classify EMG patterns in real time while an eye-tracking headset and scene camera identify the object within the user's focus. In our prototype, the same EMG recognition model that was originally developed for a conventional GPU is deployed as a spiking network on AltAi, achieving comparable accuracy while operating in a sub-watt power regime, which enables a lightweight, wearable implementation. For six distinct functional gestures recorded from upper-limb amputees, the system achieves robust recognition performance comparable to state-of-the-art myoelectric interfaces. When the vision pipeline restricts the decision space to three context-appropriate gestures for the currently viewed object, recognition accuracy increases to roughly 95% while excluding unsafe, object-inappropriate grasps. These results indicate that the proposed neuromorphic, context-aware controller can provide energy-efficient and reliable prosthesis control and has the potential to improve safety and usability in everyday activities for people with upper-limb amputation.
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