A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis

Abstract

The observation of mobility tests can greatly help neurodegenerative disease diagnosis. In particular, among the different mobility protocols, the sit-to-stand (StS) test has been recognized as very significant as its execution, both in terms of duration and postural evaluation, can indicate the presence of neurodegenerative diseases and their advancement level. The assessment of an StS test is usually done by physicians or specialized physiotherapists who observe the test and evaluate the execution. Thus, it mainly depends on the experience and expertise of the medical staff. In this paper, we propose an automatic visual system, based on a low-cost camera, that can be used to support medical staff for neurodegenerative disease diagnosis and also to support mobility evaluation processes in telehealthcare contexts. The visual system observes people while performing an StS test, then the recorded videos are processed to extract relevant features based on skeleton joints. Several machine learning approaches were applied and compared in order to distinguish people with neurodegenerative diseases from healthy subjects. Real experiments were carried out in two nursing homes. In light of these experiments, we propose the use of a quadratic SVM, which outperformed the other methods. The obtained results were promising. The designed system reached an accuracy of 95.2% demonstrating its effectiveness.

Keywords

• computer vision
• machine learning
• mobility test
• neurodegenerative disease detection