Development of a VCSEL-based optical sensor for muscle movement monitoring

Abstract

This diploma thesis presents the development and evaluation of a non-invasive optical biosensor based on Self-Mixing Interferometry (SMI) for the detection and classification of human hand movements. Using an 850 nm VCSEL operating within the tissue optical window, the contactless system measures tendon and muscle motion at the wrist through optical feedback. A two-stage signal processing pipeline, combining RMS envelope extraction, dynamic thresholding, and FFT-based spectral analysis, enables the segmentation and characterization of different motor tasks. The results demonstrate that distinct hand movements exhibit characteristic spectral signatures, allowing reliable discrimination between finger flexion and fist contraction. Overall, the study demonstrates the potential of SMI as the basis for an optical mechanomyography sensor capable of extracting reliable features for real-time motion classification.