Similarity Analysis of Upper Extremity’s Trajectories in Activities of Daily Living for Use in an Intelligent Control System of a Rehabilitation Exoskeleton

Rehabilitation robotic systems have been developed to perform therapy with minimal supervision from a specialist. Hence, they require algorithms to assess and support patients’ motions. Artificial intelligence brings an opportunity to implement new exercises based on previously modelled ones. This study focuses on analysing the similarities in upper extremity movements during activities of daily living (ADLs). This research aimed to model ADLs by registering and segmenting real-life movements and dividing them into sub-tasks based on joint motions. The investigation used IMU sensors placed on the body to capture upper extremity motion. Angular measurements were converted into joint variables using Matlab computations. Then, these were divided into segments assigned to the sub-functionalities of the tasks. Further analysis involved calculating mathematical measures to evaluate the similarity between the different movements. This approach allows the system to distinguish between similar motions, which is critical for assessing rehabilitation scenarios and anatomical correctness. Twenty-two ADLs were recorded, and their segments were analysed to build a database of typical motion patterns. The results include a discussion on the ranges of motion for different ADLs and gender-related differences. Moreover, the similarities and general trends for different motions are presented. The system’s control algorithm will use these results to improve the effectiveness of robotic-assisted physiotherapy.