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IMU-Based Intention Recognition and Motion State Estimation for Upper-Limb Prosthesis Control

IMU-Based Intention Recognition and Motion State Estimation for Upper-Limb Prosthesis Control

This thesis aims to develop an IMU-based system for recognizing user motion intention and estimating movement states for upper-limb prosthesis control.

Accelerometer and gyroscope data from wearable IMU sensors will be analyzed using signal-processing and machine-learning techniques to identify motion patterns and gesture transitions in real time. The developed framework will support intuitive, robust, and responsive prosthetic operation during daily activities.

Advisor:

Requirements:

  • Knowledge of IMU sensors and motion data acquisition
  • Signal processing and time-series data analysis
  • Machine learning using Python
  • Embedded systems / microcontroller programming (Arduino, ESP32, STM32, etc.)