Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done through BSN-based Kinematic Variables?

In this paper, by characterizing the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and Unified Parkinson's Disease Rating Scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system, amenable to be integrated in Internet of Things (IoT) scenarios, for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.Comment: 10 page

An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson's disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson's disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD

A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease

A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD

The 2016–2017 earthquake sequence in Central Italy: macroseismic survey and damage scenario through the EMS-98 intensity assessment

In this paper we describe the macroseismic effects produced by the long and destructive seismic sequence that hit Central Italy from 24 August 2016 to January 2017. Starting from the procedure adopted in the complex field survey, we discuss the characteristics of the building stock and its classification in terms of EMS-98 as well as the issues associated with the intensity assessment due to the evolution of damage caused by multiple shocks. As a result, macroseismic intensity for about 300 localities has been determined; however, most of the intensities assessed for the earthquakes following the first strong shock on 24 August 2016, represent the cumulative effect of damage during the sequence. The earthquake parameters computed from the macroseismic datasets are compared with the instrumental determinations in order to highlight critical issues related to the assessment of macroseismic parameters of strong earthquakes during a seismic sequence. The results also provide indications on how location and magnitude computation can be strongly biased when dealing with historical seismic sequences.Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile (DPC)Published2407–24314T. Sismicità dell'Italia1SR TERREMOTI - Sorveglianza Sismica e Allerta Tsunami2SR TERREMOTI - Gestione delle emergenze sismiche e da maremoto5SR TERREMOTI - Convenzioni derivanti dall'Accordo Quadro decennale INGV-DPCJCR Journa

Low-complexity inertial sensor-based characterization of the UPDRS score in the gait task of parkinsonians

In this paper, we focus on the Gait Analysis (GA) for patients affected by Parkinson's Disease (PD) using a wireless Body Sensor Network (BSN) equipped with Inertial Measurement Units (IMUs). We estimate spatio-temporal parameters and other kinematic variables to characterize the gait, in both Parkinsonians and healthy people. Gait features are compared with scores assigned by neurologists within the Unified Parkinson's Disease Rating Scale (UPDRS), with the ultimate goal of automatically determining the UPDRS score of the Gait Task (GT) carried out by Parkinsonians. Preliminary results show a high correlation between a few gait parameters (such as double support, stride length, and thigh range of rotation) and UPDRS scores

Body-Sensor-Network-Based Kinematic Characterization and Comparative Outlook of UPDRS Scoring in Leg Agility, Sit-to-Stand, and Gait Tasks in Parkinson's Disease

Recently, we have proposed a body-sensor-network-based approach, composed of a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinson's 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 published studies, where UPDRS tasks were the sole focus, in this paper, we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, after providing an accurate description of the features identified for the kinematic characterization of the three tasks, we comment on the correlation between the most relevant kinematic parameters and the UPDRS scoring. We analyzed the performance achieved by the automatic UPDRS scoring system and compared the estimated UPDRS evaluation with the one performed by neurologists, showing that the proposed system compares favorably with typical interrater variability. We then investigated the correlations between the UPDRS scores assigned to the various tasks by both the neurologists and the automatic system. The results, based on a limited number of subjects with Parkinson's disease (PD) (34 patients, 47 clinical trials), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease. An aggregate UPDRS score is also considered as a concise parameter, which can provide additional information on the overall level of the motor impairments of a Parkinson's patient. Finally, we discuss a possible implementation of a practical e-health application for the remote monitoring of PD patients

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 Parkinson's 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 Parkinson's 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

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 Parkinson's disease (PD), through kinematic features, which are then linked to the Unified Parkinson's disease rating scale (UPDRS) scores. We propose to use a single bodyworn 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