# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 1 Introduction
For instance, they can be characterized by cameras that acquire video information about the human body, and then, by using image processing techniques, extract relevant parameters useful for the analysis of motion abilities [27; 28; 29; 30]. A preliminary experiment for the analysis of the StS test, based on vision systems, was presented in [31] to demonstrate the ability of web cameras to detect sits transitions and stands phases in the StS test. Successively, two orthogonal cameras were used in [32] to create a 3D model in voxel space and identify the regions of the StS transitions. An RGB camera was used with an instrumented chair in [33] to have additional information related to the movement of the center of pressure of the observed subject. In [34], instead, a motion capture system was employed to demonstrate that video-based approaches could extract events in the StS test as well as force plates or inertial sensors. A Kinect camera was employed in [35] to measure the mean velocity of the StS transitions in order to assess fall risks. The central leitmotif of the cited works is the segmentation over time of the phases of the StS for an estimate of the time required for the execution of several repetitions (typically a five-time StS). However, recent evidence supports posture analysis during the test execution to provide insight into disease diagnosis [36; 37]. For this reason, we focus on using low-cost RGB cameras to extract skeletal information that can evaluate the execution of the StS test in terms of duration and postural attitude, as well as what physicians do within their evaluations. In addition, vision-based motion analysis systems can collect accurate kinematic data in a noninvasive way and represent good support for medical diagnosis in hospitals and clinics or telehealthcare contexts. This paper proposes a complete framework, consisting of a low-cost vision-based experimental setup and processing modules, that observes older people while performing the StS mobility test and automatically distinguishes patients with neurodegenerative diseases from healthy subjects. In particular, the main contributions of this work are as follows:  
* The experimental setup is based on a low-cost RGB camera, usually employed for video surveillance applications. This makes the sensorial framework more flexible and practical as it can be easily used in hospitals or retirement homes to support medical staff or in domestic contexts for telehealthcare applications. Real data were acquired and processed to validate the proposed system. A surveillance camera was installed in two nursing homes hosting patients with neurodegenerative diseases and healthy elderly. Several video acquisition sessions were carried out while subjects performed an StS test. * The proposed system automatically classifies patients with neurodegenerative problems and healthy subjects, emulating the complex decision process of expert physiotherapists. The video data were processed to select the most informative features and to provide a more generalized model to improve the decision process. * Different machine learning methods were compared using metrics such as accuracy, sensitivity and precision, to extract the one with the best performance in correspondence with the selected features. * In light of the carried out comparisons, we propose the best combination of features and machine learning methodology that provides the best performance in this real context. The remainder of this paper is structured as follows. Section 2 gives an overview of the proposed system and a description of data acquisition and feature extraction phases. In Section 3, the experiments carried out to compare different classifiers with different features are deeply described, and the results are commented on. Finally, the discussion is reported in Section 4, whereas Section 5 gives conclusive remarks.