Abstract:
This thesis presents a data-driven observer for estimating internal muscle states—activation,
fiber length, and tendon force—from surface EMG signals under isometric conditions.
A simplified Hill-type model (Menegaldo 2017) is used to simulate muscle dynamics.
A Kazantzis–Kravaris–Luenberger (KKL) observer, implemented via neural
networks, embeds the nonlinear system into a linear latent space for robust state
estimation. The observer is validated on synthetic and real EMG signals, including
pathological cases. Results show strong generalization and robustness, offering a
promising approach for real-time muscle monitoring and control. Future work will
integrate KKL with modulating function methods for online parameter identification.
