Jaén-Vargas Milagros, Pagán Josué, Li Shiyang, Trujillo-Guerrero María Fernanda, Kazemi Niloufar, Sansò Alessio, Codina-Casals Benito, Abi Zeid Daou Roy, Serrano Olmedo Jose Javier
Bioinstrumentation and Nanomedicine Laboratory, Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain.
Instituto Nacional de Investigaciones Científicas Avanzadas en Tecnologías de Información y Comunicación (INDICATIC AIP), Panama City, Panama.
PeerJ Comput Sci. 2025 Mar 14;11:e2695. doi: 10.7717/peerj-cs.2695. eCollection 2025.
There are 2.2 billion visually impaired individuals and 285 million blind people worldwide. The vestibular system plays a fundamental role in the balance of a person related to sight and hearing, and thus blind people require physical therapy to improve their balance. Several clinical tests have been developed to evaluate balance, such as the mini-BESTest. This test has been used to evaluate the balance of people with neurological diseases, but there have been no studies that evaluate the balance of blind individuals before. Furthermore, despite the scoring of these tests being not subjective, the performance of some activities are subject to the physiotherapist's bias. Tele-rehabilitation is a growing field that aims to provide physical therapy to people with disabilities. Among the technologies used in tele-rehabilitation are inertial measurement units that can be used to monitor the balance of individuals. The amount of data collected by these devices is large and the use of deep learning models can help in analyzing these data. Therefore, the objective of this study is to analyze for the first time the balance of blind individuals using the mini-BESTest and inertial measurement units and to identify the activities that best differentiate between blind and sighted individuals. We use the OpenSense RT monitoring device to collect data from the inertial measurement unit, and we develop machine learning and deep learning models to predict the score of the most relevant mini-BESTest activities. In this study 29 blind and sighted individuals participated. The one-legged stance is the activity that best differentiates between blind and sighted individuals. An analysis on the acceleration data suggests that the evaluation of physiotherapists is not completely adjusted to the test criterion. Cluster analysis suggests that inertial data are not able to distinguish between three levels of evaluation. However, the performance of our models shows an F1-score of 85.6% in predicting the score evaluated by the mini-BESTest in a binary classification problem. The results of this study can help physiotherapists have a more objective evaluation of the balance of their patients and to develop tele-rehabilitation systems for blind individuals.
全球有22亿视力受损者和2.85亿盲人。前庭系统在与视觉和听觉相关的人体平衡中起着基础性作用，因此盲人需要物理治疗来改善平衡。已经开发了几种临床测试来评估平衡，比如迷你BESTest测试。该测试已被用于评估患有神经系统疾病者的平衡，但此前尚无研究评估盲人的平衡。此外，尽管这些测试的评分并非主观的，但某些活动的表现仍会受到物理治疗师偏见的影响。远程康复是一个不断发展的领域，旨在为残疾人提供物理治疗。远程康复中使用的技术包括可用于监测个体平衡的惯性测量单元。这些设备收集的数据量很大，使用深度学习模型有助于分析这些数据。因此，本研究的目的是首次使用迷你BESTest测试和惯性测量单元来分析盲人的平衡，并确定最能区分盲人和有视力者的活动。我们使用OpenSense RT监测设备从惯性测量单元收集数据，并开发机器学习和深度学习模型来预测最相关的迷你BESTest活动的得分。本研究有29名盲人和有视力者参与。单腿站立是最能区分盲人和有视力者的活动。对加速度数据的分析表明，物理治疗师的评估并不完全符合测试标准。聚类分析表明，惯性数据无法区分三个评估级别。然而，我们模型的表现显示，在二元分类问题中预测迷你BESTest测试评估得分时，F1分数为85.6%。本研究结果可帮助物理治疗师对患者的平衡进行更客观的评估，并为盲人开发远程康复系统。
