Alleviating Muscle Fatigue in Robotic Prosthetic Arms Through Multi-Sensor Fusion and Adaptive Real-Time Grip Assistance

The proposed approach demonstrates a simple but potent adaptive gripping algorithm within the framework of a multi-sensor system intended to minimize the effects of muscle fatigue in robotic prosthetic arms. It is made possible by seamlessly integrating Electromyography (EMG) signals with computer vision and pressure feedback, thereby providing a basis for real-time adjustment of grip strength and movement. EMG sensors continuously monitor muscle activity to detect possible early signs of fatigue, ensuring the adaptive reduction of strain on the user. In parallel, computer vision recognizes objects and determines the optimal grip techniques. Pressure sensors are embedded on the palm to continuously provide feedback to fine-tune the applied force. This new methodology improves comfort and efficiency in performing tasks over precise control with minimal muscle strain. The system also shows significant adaptability and can be easily integrated with existing prosthetic arms without significant hardware modifications, thus improving the long-term functionality and usability of such prosthetic devices in everyday activities.