@文章{info:doi/ 10.2199 /37229，作者="Tsakanikas, Vassilios和Gatsios, Dimitris和Pardalis, Athanasios和Tsiouris, Kostas M和Georga, Eleni和Bamiou, Doris-Eva和Pavlou, Marousa和Nikitas, Christos和Kikidis, Dimitrios和Walz, Isabelle和Maurer, Christoph和Fotiadis, Dimitrios"，标题="用数据驱动的评分模型自动评估平衡康复练习:算法开发与验证研究”，期刊=“JMIR Rehabil Assist technology”，年=“2022”，月=“8”，日=“31”，卷=“9”，数=“3”，页=“e37229”，关键词=“平衡康复练习;评分模型;运动评价;背景:平衡康复项目是平衡障碍最常见的治疗方法。然而，缺乏资源和缺乏高度专业的物理治疗师是患者接受个性化康复疗程的障碍。因此，平衡康复计划通常转移到家庭环境中，有相当大的风险，患者执行不当的练习或根本没有遵守计划。全息平衡是一种有说服力的指导系统，能够在家里提供全面的康复服务。全息平衡包括几个模块，从康复项目管理到增强现实教练演示。目的:本研究的目的是设计、实施、测试和评估一个基于数据驱动技术的平衡康复练习的准确评估评分模型。 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 ", issn="2369-2529", doi="10.2196/37229", url="https://rehab.www.mybigtv.com/2022/3/e37229", url="https://doi.org/10.2196/37229", url="http://www.ncbi.nlm.nih.gov/pubmed/3604425" }