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Automatic Identification of Lower-Limb Neuromuscular Activation Patterns During Gait Using a Textile Wearable
Federica Amitrano1, Armando Coccia1, Federico Colelli Riano1
1Movement Analysis and Robotics Laboratory (MARLab), Istituti Clinici Scientifici Maugeri IRCCS, Telese Terme Institute, 82037 Benevento, Italy.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study developed a comfortable wearable system using surface Electromyography (sEMG) and pressure insoles to monitor muscle activation during walking. The system effectively captured Tibialis Anterior and Gastrocnemius Lateralis activity patterns in healthy adults.
Area of Science:
- Biomedical Engineering
- Kinesiology
- Wearable Technology
Background:
- Wearable sensors are vital for assessing neuromuscular function during daily activities.
- Accurate monitoring of muscle activation during gait is crucial for clinical assessments.
Purpose of the Study:
- To present and evaluate a multisensor wearable system for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) muscle activation during walking.
- To assess the feasibility and effectiveness of integrating surface Electromyography (sEMG) and plantar pressure sensing.
Main Methods:
- Developed a system combining a textile-based sEMG sleeve and a pressure-sensing insole.
- Collected synchronized sEMG and plantar pressure data from 11 healthy adults during overground walking.
- Utilized a dedicated algorithm to detect muscle activation intervals across gait cycles.
Main Results:
- The wearable system was comfortable and provided stable recordings.
- Detected 1-4 muscle activation bursts per gait cycle, with consistent TA activity in terminal swing and GL activity in mid- to terminal stance.
- Observed inter- and intra-subject variability in muscle activation patterns, aligning with physiological gait modulation.
Conclusions:
- The multisensor wearable system is feasible for characterizing muscle activation during walking.
- The system's comfort, signal quality, and ease of integration support its use in clinical gait analysis and remote monitoring.
- Future research will focus on system optimization and validation in diverse populations.
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