# Automatic Identification of Lower-Limb Neuromuscular Activation Patterns During Gait Using a Textile Wearable Multisensor System

## TL;DR

A wearable system using textile sensors successfully identifies muscle activation patterns during walking, offering potential for gait analysis and remote monitoring.

## Contribution

A novel textile-based wearable system combining sEMG and pressure sensing for automatic identification of lower-limb neuromuscular activation during gait.

## Key findings

- The system detected consistent Tibialis Anterior activity in terminal swing and Gastrocnemius Lateralis activity in mid- to terminal stance.
- Activation patterns showed variability across participants and gait phases, reflecting known physiological modulation.
- The system provided stable and comfortable recordings suitable for further clinical and remote monitoring applications.

## Abstract

Wearable sensing technologies are increasingly used to assess neuromuscular function during daily-life activities. This study presents and evaluates a multisensor wearable system integrating a textile-based surface Electromyography (sEMG) sleeve and a pressure-sensing insole for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) activation during gait. Eleven healthy adults performed overground walking trials while synchronised sEMG and plantar pressure signals were collected and processed using a dedicated algorithm for detecting activation intervals across gait cycles. All participants completed the walking protocol without discomfort, and the system provided stable recordings suitable for further analysis. The detected activation patterns showed one to four bursts per gait cycle, with consistent TA activity in terminal swing and GL activity in mid- to terminal stance. Additional short bursts were observed in early stance, pre-swing, and mid-stance depending on the pattern. The area under the sEMG envelope and the temporal features of each burst exhibited both inter- and intra-subject variability, consistent with known physiological modulation of gait-related muscle activity. The results demonstrate the feasibility of the proposed multisensor system for characterising muscle activation during walking. Its comfort, signal quality, and ease of integration encourage further applications in clinical gait assessment and remote monitoring. Future work will focus on system optimisation, simplified donning procedures, and validation in larger cohorts and populations with gait impairments.