Thesis Project Form

Wearable robotics and assistive technologies are rapidly evolving fields with the potential to significantly improve healthcare, rehabilitation, and mobility assistance.
A key challenge in this area is understanding how the human body moves and how soft exoskeletons interact with the natural gait cycle to provide effective and intuitive support. Accurate detection of gait events is essential to synchronize control strategies with user movement.
Resistive insole sensors offer a compact, low-cost, and versatile solution to capture foot-ground interaction dynamics, making them suitable for integration into wearable robotic systems. This thesis will directly contribute to the ongoing development of a new soft lower limb exoskeleton, supporting the design of its sensing and control framework for user-centered gait assistance.

The purpose of the thesis is to characterize and exploit resistive insole sensors for accurate gait phase detection and signal analysis, contributing to the development of a new soft lower-limb exoskeleton. The work aims to evaluate sensor performance, signal integrity, and the extraction of meaningful biomechanical information during walking.

The main steps of the project are:
1. Signal characterization: Analyze the raw resistive signals during walking to assess their repeatability, sensitivity, and stability in different gait cycles.
2. Gait phase detection: Develop and validate algorithms for identifying key gait events — Heel Strike, Midstance, Heel Off, and Toe Off — from the insole pressure data.
3. Pressure distribution analysis: Study the spatial distribution of plantar pressure during gait to evaluate the ability of the sensors to capture dynamic load variations.
4. Signal degradation assessment: Examine how sensor performance evolves over time and under different usage conditions, identifying sources of noise or drift.
5. Problem-solving and enhancement: Design, implement, and test data processing or filtering techniques to mitigate signal degradation and improve reliability.

This thesis is designed not only as an experimental study but also as an opportunity for hands-on learning and creative problem-solving. The student will:
• Gain practical experience with sensor characterization, signal acquisition, and data analysis techniques.
• Learn to design algorithms for gait phase detection and pressure distribution assessment.
• Contribute to the development of a new soft lower limb exoskeleton by integrating resistive insoles into its high-level control framework.
• Work in a supportive environment with the freedom to test, iterate, and refine new approaches.