Ivett Quiñones-Uriostegui

Musculoskeletal disorders such as rheumatoid arthritis and osteoarthritis affect millions worldwide and are projected to rise sharply by 2050, highlighting the importance of scalable telerehabilitation. This paper introduces Rehabi, a mobile, user-friendly tele-rehabilitation platform that centrally integrates markerless motion for biomechanical assessment and monitoring. Rehabi development followed a user-centered methodology, combining questionnaires, interviews, and natural language processing to elicit requirements from patients and clinicians. The system architecture was implemented in accordance with Clean Architecture principles to ensure modularity and scalability. In a pilot clinical validation of the markerless motion algorithm integrated into Rehabi, 14 post-arthroplasty patients showed moderate agreement for hip flexion (ICC = 0.686) and good agreement for knee flexion (ICC = 0.801). Although the sample was small, the results show a promising trend suggesting that mobile markerless motion capture may be a viable option for remote assessment and monitoring.

Human-Machine-Interfaces (HMIs) can use surface electromyography (sEMG) signals to control equipment that assists disabled people in their activities of daily living (ADLs). The use of sEMG signals in HMIs is currently the subject of extensive research. However, some drawbacks of previous research are that weight loads that directly impact sEMG signals, movement velocities, electrode positioning, and a criterion for selecting sEMG features are not considered for the best performance in HMI. Therefore, the article's main contribution is the presentation of a methodology that allows identifying the muscles and features that have the most significant contribution in sEMG-based gesture recognition, considering electrode positioning and avoiding compensatory movements. The article highlights how load weights affect sEMG signals and how principal component analysis determines the best sEMG features for gesture classification. We compared seventeen machine learning classifier models for classifying four upper limb movements based on decision trees, support vector machines, k-Nearest Neighbors, and ensembled methods classifier models. The results show that the signal square integral and Mean Frequency features of sEMG make it possible for classifiers to get an accuracy of above 90%.

Introduction: stress and anxiety are body reactions that are related to mental or emotional variations, both have been linked to temporomandibular disorders. An imbalance in the masseter muscle can contribute to the loss of cervical lordosis. Videogames usually contribute to well-being, however, there have been reports of anxiety. Objective: to determine if a videogame competition can create emotional changes and affect the masseter muscle activation. Material and methods: thirteen university-level subjects were recruited. We measured heart rate and obtained the stress index. For surface EMG we instrumented the masseter muscle, and they answer the form Y1 of the State-Trait Anxiety Inventory. Subjects were randomized into groups of four and played in competition mode Mario Kart. Results: there exists a significant difference in values at the basal mean heart rate and in-game mean heart rate which went from 83 ± 15 bpm to 102 ± 14 bpm. Also, there was a significant difference when divided by gender the sample in their anxiety levels women 46.50 ± 9.38 point while men 35.06 ± 5.23. For sEMG average activation for both sides were 65 ± 40%. Conclusion: the results showed that when playing videogames in competition mode it can increase anxiousness in subjects that can trigger masseter muscle activation in levels above chewing something hard, which could lead to cervical posture problems in the future.

This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.