# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 1 Introduction  
In recent years, a growing body of literature has presented both technologies and methodologies in the context of active assisted living (AAL) for addressing several issues [1] related to the health and well-being of people. Wearable sensors, smart everyday objects and environmental sensors have been proposed to develop systems for basic activity monitoring (vital signs monitoring, physical activity, cognitive training and many others) and also for preventive measures and telehealthcare diagnosis [2; 3; 4]. Data processing can be afforded with simple data analysis techniques when threshold-based or distance-based methods can be applied, or with more complex methodologies such as machine learning ones, when complex data have to be analyzed and processed [5; 6; 7]. In this paper, we consider one issue that is relevant for medical staff and mainly concerns the older population, particularly people with dementia or neurodegenerative diseases. The assessment of the motor skills of the elderly can significantly assist in the diagnosis of neurodegenerative diseases and the evaluation of their progress. Motion skills are generally evaluated through the observation of mobility tests administratedrated and rated by physicians or specialized physiotherapists in controlled situations [8; 9; 10]. The resulting assessments depend on the experience and expertise of the evaluator. The use of technologies for the automatic evaluation of mobility tests can guarantee an objective assessment of the mobility abilities of the elderly. Different mobility protocols are available to check the motion abilities of older people. These protocols, defined by medical staff, consist of several mobility tests such as balance, walking, or sit to stand (StS). In this paper, we focus our attention on the StS test, as it is significant for quantifying the functional strength of the lower limbs or identifying the movement strategies a person uses to complete transitional movements between sitting and standing. Thus, its execution, in terms of duration and body posture, can indicate the presence of neurodegenerative disease and its level of advancement. In the StS test, the person sits down and stands up several times with their arms crossed on the chest. Various instrumented systems have been proposed for real-time assessment of the mobility capabilities of older people. The majority of works propose wearable sensors based on inertial measurement units or inertial and magnetic measurement systems for the physical function evaluation of individuals such as postural stability or fall detection [11; 12; 13]. Among the possible mobility tests, the walking test is undoubtedly the one that has received the most significant attention from the scientific community [14], and several technologies, both wearable and environmental, have been used to provide data on gait parameters for automatic analysis [15; 16; 17]. These sensors include accelerometers, gyroscopes and magnetometers, which are used to measure the acceleration or angular velocity of the body or of the body segments they are attached to [18]. Although wearable sensors return valid information related to the movement of people, they present some drawbacks as their output strictly depends on the position at which they are placed, their orientation and the activities to be monitored [19; 20]. As a consequence, different wearable sensor modalities were proposed in [21] to mitigate the shortcomings of each sensor. In [22; 23; 24; 25; 26], wearable sensors were mostly used to monitor StS transitions and duration. The main drawback of the approaches based on wearable sensors is that older people, especially those with neurological disorders, do not readily accept wearing unfamiliar devices. Contrary to wearable sensors, nonwearable ones, such as vision-based systems, are noninvasive for people as they are placed in the environment.