# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
###### Abstract  
Movement monitoring in patients with Parkinson's disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland-Altman plot showed a mean difference of \(-3.29\) and \(-1\) between the algorithm and the video recording for walking bout detection and steps identification, respectively. gait analysis; home monitoring; inertial measurement system; [PERSON]'s disease; wearable sensor +
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therefore, improve the response to treatment. For these purposes, a widespread medical method for a better overview of the patient's motor symptoms over a long-lasting period consists of a diary self-reported by the patient during his daily life at home. Motion analysis provides objective information about human locomotion and it is frequently used for treatment planning. Several methods have been successfully utilized to extract objective gait parameters; among them, stereophotogrammetry, dynamometric force platforms, treadmills, and electromyography are frequently adopted. Despite these instruments ensuring the highest performances in terms of accuracy and reliability, they are expensive, uncomfortable for patients, restricted to specialized laboratories and cannot be applied in large populations [5]. Moreover, such equipment cannot be applied in home environments which represent the most appropriate scenario for the monitoring of patient's activities of daily living (ADL) [6; 7]. In [8], the authors showed that conventional measures in a laboratory environment do not reflect the daily-living activities of PD patients. In fact, daily-living measures provide important information that is not captured in a conventional one-time laboratory assessment of gait, balance or the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). With the advanced growth in technologies, the use of wearable sensors for an objective long-term monitoring of PD patients in the home scenario can play an important role [9; 10]. The wearable sensors are better suited to quantify gait in unsupervised and free-living environments, such as in a home scenario, providing a more comprehensive overview of the continuous walking monitoring. Several validation studies in controlled settings exist, but only a few of them have examined the validity of wearable and associated algorithms in uncontrolled environments [11]. Various works have focused on the detection of tremor episodes in constrained and unsupervised environments [12; 13; 14], on the PD turning analysis in patients suffering from freezing [15] and fall risk [16; 17], and on the detection of on/off states [18]. Other works have carried out a walking analysis in laboratory and home scenarios, providing gait quantity (i.e., number of steps and number of walking bouts) or gait quality (i.e., step length (m), step regularity, and the amplitude of dominant frequency) [8; 19; 20; 21; 22].