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Tsakanikas V, Gatsios D, Pardalis A, Tsiouris KM, Georga E, Bamiou DE, Pavlou M, Nikitas C, Kikidis D, Walz I, Maurer C, Fotiadis D. Automated assessment of balance rehabilitation exercises: A data-driven scoring model (Preprint). JMIR Rehabil Assist Technol 2022; 9:e37229. [PMID: 36044258 PMCID: PMC9475421 DOI: 10.2196/37229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/23/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022] Open
Abstract
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
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Affiliation(s)
- Vassilios Tsakanikas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Dimitris Gatsios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Athanasios Pardalis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Kostas M Tsiouris
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Eleni Georga
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Doris-Eva Bamiou
- Ear Institute, University College London, London, United Kingdom
- Biomedical Research Centre Hearing and Deafness, University College London Hospitals, London, United Kingdom
| | - Marousa Pavlou
- Centre for Human and Applied Physiological Sciences, King's College London, London, United Kingdom
| | - Christos Nikitas
- First Department of Otolaryngology-Head and Neck Surgery, Hippokrateio General Hospital, National Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios Kikidis
- First Department of Otolaryngology-Head and Neck Surgery, Hippokrateio General Hospital, National Kapodistrian University of Athens, Athens, Greece
| | - Isabelle Walz
- Department of Neurology and Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christoph Maurer
- Department of Neurology and Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dimitrios Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Biomedical Research Institute, Ioannina, Greece
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