ニューロモーフィックプロセッサAltAiを用いたEMGと視線追跡による義手操作システム
Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
筋電位と視線誘導の視覚情報を組み合わせ、ニューロモーフィックプロセッサ上でスパイキングニューラルネットワークを動かすことで、低消費電力かつ高精度な義手制御を実現した研究。
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著者: Roman Akinshin, Elizaveta Lopatina, Kirill Bogatikov, Nikolai Kiz, Anna V. Makarova, Mikhail Lebedev, Miguel Altamirano Cabrera, Dzmitry Tsetserukou, Valerii Kangler
分類: cs.RO
原文アブストラクト
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.
