Real-time tendon strain estimation of rotator-cuff muscles during active robotic-assisted rehabilitation
I.L.Y. Beck (TU Delft - Mechanical Engineering)
A. Seth – Mentor (TU Delft - Mechanical Engineering)
J.M. Prendergast – Graduation committee member (TU Delft - Mechanical Engineering)
I. Belli – Coach (TU Delft - Mechanical Engineering)
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Abstract
In this research, we propose a novel method to estimate and monitor rotator cuff tendon strains during active robotic-assisted rehabilitation. Physiotherapists are often conservative in rehabilitation treatment to prevent (re-)injury because the internal state of the shoulder is not directly observable. By leveraging a robotic device and a musculoskeletal model, our approach provides quantitative information on the risk of re-injury by monitoring rotator cuff tendon strains. These strains are influenced by the shoulder state and muscle activation, making it crucial to obtain physiologically realistic data on real-time muscle function.
To address the muscle redundancy problem, we developed an innovative algorithm that incorporates constraints on accelerations, the glenohumeral joint, and active muscle dynamics. The effectiveness of the algorithm was validated through comparisons with electromyography (EMG) measurements. In addition, the proposed approach was demonstrated using a collaborative robot arm during rehabilitation exercises.
The results of this study provide new insights into the relationship between rotator cuff muscles, external forces, and shoulder pose. These findings pave the way for improved therapy strategies that consider the risk of injury to individual muscles and allow exploration of a broader range of motion. By providing physiotherapists with valuable quantitative information on rotator cuff tendon strains, this method enables the optimization of rehabilitation protocols and supports more personalized and effective care.