Laura Contin

Automatic UPDRS Evaluation in the Sit-to-Stand Task of Parkinsonians: Kinematic Analysis and Comparative Outlook on the Leg Agility Task

In this study, we first characterize the sit-to-stand (S2S) task, which contributes to the evaluation of the degree of severity of the Parkinsons disease (PD), through kinematic features, which are then linked to the Unified Parkinsons disease rating scale (UPDRS) scores. We propose to use a single body-worn wireless inertial node placed on the chest of a patient. The experimental investigation is carried out considering 24 PD patients, comparing the obtained results directly with the kinematic characterization of the leg agility (LA) task performed by the same set of patients. We show that i) the S2S and LA tasks are rather unrelated and ii) the UPDRS distributions (for both S2S and LA tasks) across the patients have a direct impact on the observed system performance.

Inertial BSN-Based Characterization and Automatic UPDRS Evaluation of the Gait Task of Parkinsonians

The analysis and assessment of motor tasks, such as gait, can provide important information on the progress of neurological disorders such as Parkinsons disease (PD). In this paper, we design a Boby Sensor Network (BSN)-based system for the characterization of gait in Parkinsonians through the extraction of kinematic features, in both time and frequency domains, embedding information on the status of the PD. The gait features extraction is performed on a set of 34 PD patients using a BSN formed by only three inertial nodes (one on the chest and one per thigh). We investigate also the relationship between the selected kinematic features and the Unified Parkinsons Disease Rating Scale (UPDRS) scores assigned to patients by expert neurologists. This work extends a previously proposed approach to the analysis of leg agility and sit-to-stand tasks and, as such, represents a further step to develop a system for automatic and comprehensive evaluation of different PD motor tasks. A performance analysis of different classification techniques is carried out, showing the feasibility of an automatic (and, eventually, remote) UPDRS scoring system, suitable for tele-health applications in the realm of affective medicine.

On the correlation between UPDRS scoring in the leg agility, sit-to-stand, and gait tasks for parkinsonians

Recently, we have proposed a unified approach, based on the use of a Body Sensor Network (BSN) formed by a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinsons Disease Rating Scale (UPDRS) scores in the following tasks: Leg Agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the works appeared in the literature, where UPDRS tasks are investigated singularly, in the current paper we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, we focus on the correlation between UPDRS values assigned to the three tasks by both an expert neurologist and our automatic system. We also consider an aggregate UPDRS score in order to highlight the relevance of each task in the assessment of the gravity of the Parkinson;s Disease (PD).