Musculoskeletal modeling has the opportunity to improve the rehabilitation process after a shoulder injury by monitoring the muscle activations and tendon strain during rehabilitation exercises. The PTbot project combines a robot arm and a Thoracoscapular Shoulder Model (TSM) in order to track the shoulder movement and give a physiotherapist insight on the what happens in a human body, but the performance of the TSM is not yet analyzed. This paper aims to get a better understanding of the TSM by testing its performance in a wide range of motion.

With an existing isometric dataset, the TSM is positioned in five different shoulder configurations. First, the model is scaled in four steps. With the help of the muscle moment arms an normalized fiber length of this scaled model, improvements have been made to the latissimus dorsi. This improved model is scaled a second time whereafter an RMR solver is run. This RMR solver computed the muscle activation levels and joint torque residuals of the TSM. The RMR solver is first run with experimental external forces applied on the hand, in order to validate the TSM by comparing the muscle activations to EMG data. After that, the RMR solver is run with artificial external forces, in order to understand which muscles are activated during push and pull exercises.

The Mean Average Error (MAE) between the muscle activation levels and experimental EMG measurements has been calculated for the validation of the TSM. They show that the TSM performs well when the shoulder planar elevation and shoulder elevation angles, but the performance decreases when these angles increase. The active fiber forces have been calculated and the muscles with the major contribution have been identified for pulling and pushing. The Infraspinatus, rapezius and deltoid play a major role for pushing exercises and the subscapularis, teres major and biceps for pulling exercises. The total muscle force needed for pushing is higher than for pulling.

Manual wheelchair users, especially those with spinal cord injuries, often suffer from shoulder overuse and pain. Understanding the specific role of the muscles involved in wheelchair propulsion is essential to prevent these complications. Yet, there is uncertainty about which muscles are primarily recruited, and their contribution to propulsion power. For example, some studies have suggested that the rotator cuff muscles are the primary contributors to wheelchair propulsion. Several studies have explored muscle activation patterns using musculoskeletal models, but often omit glenohumeral stability and key muscles like the trapezius, serratus anterior, and rhomboideus. Investigating muscle work provides insights into the actual mechanical output, offering a more accurate assessment of efficiency by considering factors like muscle force and movement distance.

The aim of this study was to examine individual muscle contributions to mechanical work during the push and recovery phases of wheelchair propulsion using a musculoskeletal model and to identify the role of rotator cuff muscles. The thoracoscapular shoulder model featuring accurate scapula kinematics, inclusive of the rhomboideus, serratus anterior, and trapezius muscles, was employed. In a previous experiment electromyography, kinematic and pushrim forces of 5 persons with paraplegia were collected. The filtered pushrim forces and the kinematic data as well as the scaled musculoskeletal model were inputs to the rapid muscle redundancy solver, that enforced glenohumeral joint stability while estimating muscle activations and muscle power. Muscle work was integrated from the estimated muscle power and normalized by the total mechanical work. During the push phase, significant contributions came from the pectoralis major, anterior deltoid, infraspinatus, serratus anterior, triceps brachii, and biceps brachii. In contrast, the recovery phase primarily involved the teres major, subscapularis, trapezius, posterior deltoid, middle deltoid, and rhomboideus. The contribution of rotator cuff muscles to propulsion was noted but to a lesser extent than previous reports, with no contribution from the supraspinatus. Surprisingly, the teres major showed high work values, possibly due to insufficient activation of the latissimus dorsi. Our findings support the importance of incorporating muscles like the serratus anterior, trapezius, and rhomboids into musculoskeletal models. Furthermore, the contribution of reserve actuators to total work generation was below the threshold of 5%. The comparison between estimated muscle activations and measured electromyographic activations generally showed excellent to good magnitude matching. This study's outcomes can aid in designing ergonomic wheelchairs and guide the development of tailored rehabilitation and training methods to decrease upper extremity stress in wheelchair users.

In this work, we propose a method of processing patient input on discomfort level during robot shoulder physiotherapy into discomfort maps. These maps represent the patient's discomfort distribution throughout the range of motion of the shoulder, interpretable by both physiotherapists and robots. This method consists of three parts: the patient can input discomfort with a linear push-button; a collaborative robot arm is used to track the motion of the patient's shoulder; and audiovisual feedback of inputted discomfort is given to the patient and the therapist.
The method was validated in human factors experiments simulating shoulder physiotherapy sessions, where the subject is tasked with recreating a reference discomfort map through an auditory reference signal that emulates this discomfort. Here the robot also acts as the physiotherapist, moving the subject's shoulder. The signal is a beeping sound, whose rate scales with the discomfort intensity at the measured pose in the reference discomfort map.
We performed experiments with a total of 10 participants, demonstrating the viability of our method during patient-robot interaction. The results we collected also highlighted the presence of a time delay between the discomfort signal and the user input, and its effect on discomfort maps.