# A Multimodal Biomedical Sensing Approach for Muscle Activation Onset Detection

## TL;DR

This paper introduces a new method for detecting when muscles start to activate, using both electromyography and optical motion data to improve accuracy and reduce errors.

## Contribution

A lightweight temporal attention method for slow muscle activation onset detection is proposed and validated.

## Key findings

- The proposed method achieves 92% onset detection accuracy, 90% recall, and 93% precision.
- Average detection error and delay are reduced to 41ms and 28ms, respectively.
- The method shows strong robustness under varying noise levels and cross-subject settings.

## Abstract

Muscle onset detection is a fundamental problem in electromyography signal analysis, human–machine interaction, and rehabilitation assessment. In medical and biomedical applications, slow muscle activation onset processes are widely encountered in scenarios such as rehabilitation training, postural regulation, and fine motor control. Such processes are typically characterized by slowly varying amplitudes, long temporal durations, and high susceptibility to noise interference, which poses significant challenges for accurate identification of onset timing. To address these issues, a lightweight temporal attention method for slow muscle activation onset detection is proposed and systematically validated under multimodal experimental settings. The proposed method takes surface electromyography signals as the primary input, while synchronously acquired optical motion image data are incorporated into the experimental design and result analysis, thereby aligning with the common joint use of optical imaging and physiological signals in medical and biomedical research. From a methodological perspective, the proposed framework is composed of lightweight temporal feature encoding, a slow activation-aware temporal attention mechanism, and noise suppression with stable decision strategies. Under the constraint of low computational complexity, the ability to model progressive activation signals is effectively enhanced. Experiments are conducted on a dataset containing multiple types of slow activation movements, and model performance is evaluated using five-fold cross-validation. The results demonstrate that under regular signal-to-noise ratio conditions, the proposed method significantly outperforms traditional threshold-based approaches, classical machine learning models, and several deep learning baselines in terms of onset detection accuracy, recall, and precision. Specifically, onset detection accuracy reaches approximately 92%, recall is around 90%, and precision is approximately 93%. Meanwhile, the average onset detection error and detection delay are reduced to about 41ms and 28ms, respectively, with the false positive rate controlled at approximately 2.2%. Stable performance is further maintained under different noise levels and cross-subject settings, indicating strong robustness and generalization capability.