Advancing rehabilitative robotics through signal processing and machine learning algorithms

Abstract

Rehabilitative robotics holds tremendous promise in improving the quality of life for individuals with motor impairments. The integration of signal processing and machine learning algorithms into rehabilitative robotics systems has emerged as a powerful approach to enhance the effectiveness and efficiency of rehabilitation therapies. This thesis aims to explore and contribute to the advancements in this exciting field. The first part of this research focuses on signal processing techniques applied to the analysis and interpretation of sensor data in rehabilitative robotics. Various signal processing methods such as filtering, feature extraction, and time-frequency analysis are investigated to extract relevant information from sensory signals captured by robotic devices. These processed signals serve as valuable inputs for subsequent machine learning algorithms. The second part of the thesis delves into the application of machine learning algorithms in rehabilitative robotics. Supervised, unsupervised, and reinforcement learning techniques are studied to model and predict user intent, adapt robot behaviour, and optimize rehabilitation exercises. These algorithms play a pivotal role in personalizing the rehabilitation process, enabling tailored interventions based on individual needs and progress. The integration of signal processing and machine learning presents unique opportunities for real-time adaptation and closed-loop control in rehabilitative robotics. The combination of sensor data processing and machine learning enables the creation of intelligent robotic systems that can dynamically adjust therapy parameters, ensuring optimal engagement and challenging the user at an appropriate level. In addition to technological advancements, this research also addresses practical challenges in the implementation of signal processing and machine learning algorithms in real-world rehabilitative robotics applications. Considerations such as computational efficiency, robustness to noise and variability, and user acceptance are carefully examined to ensure the feasibility and effectiveness of the proposed approaches. Overall, this thesis aims to contribute to the field of rehabilitative robotics by advancing the integration of signal processing and machine learning algorithms.