Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study

BACKGROUND: Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. OBJECTIVE: The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. METHODS: The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. RESULTS: The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. CONCLUSIONS: The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. TRIAL REGISTRATION: ClinicalTrials.gov NCT04053829; https://clinicaltrials.gov/ct2/show/NCT04053829

Персональне освітнє середовище – як один із трендів сучасної освіти

To meet the needs of the modern information society one must constantly improve the education system. The effectiveness of teaching today’s students fully depends on the implementation and use in the study of modern information and communication technologies, including network services that allow you to create an appropriate pedagogy and technology support base of modern information systems for educational purposes, and effectively organize the electronic learning university environment. An analysis of e-learning environments of modern domestic and foreign universities demonstrates quite a high level of qualitative and quantitative indicators of the implementation of electronic resources for educational purposes. However, despite the relatively high level of organization and content of university portals, the creation and implementation of students‘ personalized e-learning environment, which in turn is based on personalization in a global network, student-centered learning, which acts as a basis for the formation of ICT and key competencies of modern student, is still an open issue. The selfspontaneous creation of personalized e-learning environment does not cover the training needs of students, but is only partially able to satisfy them, as knowledge students cannot improve the quality of both formal and informal learning. This paper focuses on the study of students‘ ICT competencies and their ability to use information and communication technologies to carry out information activities in their professional field. The authors also discuss the results of studies on personalized and adaptive learning, based on consideration of learning styles. Based on a statistical analysis of the pedagogical experiments, some recommendations are suggested for technology training for teachers and students in order to to improve training efficiency