# Surface EMG Sensing and Granular Gesture Recognition for Rehabilitative Pouring Tasks: A Case Study

## TL;DR

This paper explores using sEMG and deep learning to improve recognition of pouring gestures for rehabilitation, showing significant accuracy improvements.

## Contribution

A novel deep learning approach combining ConvMixer with feature fusion and granular computing for precise gesture recognition in rehabilitation.

## Key findings

- Adding hand-crafted features boosted ConvMixer model accuracy from 0.9512 to 0.9929.
- The approach shows promise for rehabilitation technologies and assistive systems.
- Pouring gestures require precise muscle coordination, making them ideal for study.

## Abstract

Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities.