# Hand Prosthesis with Soft Robotics Technology and Artificial Intelligence for Fine Motor Control

## TL;DR

This paper introduces a soft robotic hand prosthesis that uses artificial intelligence and muscle-inspired design to help users perform fine motor tasks like grasping small objects.

## Contribution

A novel soft robotic prosthesis with AI-based control and vacuum-based reinforcement for improved grasp adaptability without force sensors.

## Key findings

- The prosthesis achieved response times between 0.49 and 2.00 seconds during functional tests.
- It demonstrated 80% grasping effectiveness for small objects with varied geometries.
- The system uses a lightweight neural network on a low-power microcontroller for real-time EMG signal processing.

## Abstract

The development of prostheses that accurately reproduce fine motor skills remains a key challenge for daily assistance applications. This research presents the development of a soft robotic hand prosthesis prototype inspired by the natural behavior of muscles and tendons, incorporating internal vacuum-based reinforcement and textured fingertip surfaces to enhance friction and grasp adaptability, without relying on force sensors. The prosthesis reproduces open-hand and tripod pinch movements through myoelectric signals (EMG) acquired via a wearable armband equipped with eight surface electrodes. The signals are processed in real-time and classified by a lightweight dense neural network implemented on a low-power microcontroller. Tendon-driven actuation enables biomimetic motion with smooth and compliant behavior. The proposed system was validated through laboratory-based functional tests using user-specific models, showing response times ranging from 0.49 to 2.00 s and an overall grasping effectiveness of approximately 80% when manipulating small everyday objects with different geometries. These results indicate that the prototype constitutes an accessible and functional solution for fine motor assistance, with potential applicability in low-cost and resource-constrained myoelectric prosthetic systems.