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Tactile Sensor-Based Grip Force Estimation and Adaptive Object Interaction for Prosthetic Hand

Tactile Sensor-Based Grip Force Estimation and Adaptive Object Interaction for Prosthetic Hand

This project focuses on developing a tactile sensor-based framework for grip force estimation and adaptive object interaction in upper-limb prosthetic hands.

Tactile and pressure sensor data from prosthetic fingertips and palm regions will be processed using signal-processing and machine-learning techniques to detect contact conditions, estimate grip force, and identify object interaction states in real time. The proposed system aims to improve grasp stability, object handling safety, and adaptive prosthetic control during daily manipulation tasks.

Advisor:

Requirements:

  • Knowledge of tactile/pressure sensors and data acquisition
  • Signal processing and sensor fusion techniques
  • Machine learning using Python or MATLAB
  • Embedded systems and microcontroller programming (Arduino, ESP32, STM32, etc.)