Human-robot interaction (HRI) faces challenges
in achieving seamless and intuitive communication, particularly
due to limitations of vision-based methods such as occlusions
and privacy concerns. Surface electromyography (sEMG) pro-
vides a wearable alternative for intent recognition, but existing
research predominantly focuses on gesture classification rather
than continuous motion estimation. This thesis proposes a
novel muscle-driven control framework that uses dual-channel
sEMG signals for real-time hand trajectory estimation and
robotic following tasks. A Transformer-based deep learning
model is trained to decode raw sEMG data into spatial hand
positions, while a machine learning mapping directly translates
these positions into robotic joint angles for low-latency control.
Experimental validation demonstrates the system’s capability
in dynamic interactions such as high-fives and object follow-
ing, with performance benchmarked against a vision-based
pose estimation system. The results highlight the feasibility of
sEMG-based continuous motion decoding for camera-free HRI,
offering a privacy-preserving and physiologically grounded
approach for assistive and collaborative robotics.