Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease”

In this paper we investigated the use of smartphone sensors and Artificial Intelligence techniques for the automatic quantification of the MDS-UPDRS-Part III Leg Agility (LA) task, representative of lower limb bradykinesia. Methods: We collected inertial data from 93 PD subjects. Four expert neurologists provided clinical evaluations. We employed a novel Artificial Neural Network approach in order to get a continuous output, going beyond the MDS-UPDRS score discretization. Results: We found a Pearson correlation of 0.92 between algorithm output and average clinical score, compared to an inter-rater agreement index of 0.88. Furthermore, the classification error was less than 0.5 scale point in about 80% cases.Conclusions:Weproposedanobjectiveandreliabletoolfor theautomaticquantificationoftheMDS-UPDRSLegAgilitytask. In perspective, this tool is part of a larger monitoring program to be carried out during activities of daily living, and managed by the patients themselves

Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson's disease

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Bradykinesia Detection in Parkinson’s Disease Using Smartwatches’ Inertial Sensors and Deep Learning Methods

Bradykinesia is the defining motor symptom of Parkinson’s disease (PD) and is reflected as a progressive reduction in speed and range of motion. The evaluation of bradykinesia severity is important for assessing disease progression, daily motor fluctuations, and therapy response. However, the clinical evaluation of PD motor signs is affected by subjectivity, leading to intra- and inter-rater variability. Moreover, the clinical assessment is performed a few times a year during pre-scheduled follow-up visits. To overcome these limitations, objective and unobtrusive methods based on wearable motion sensors and machine learning (ML) have been proposed, providing promising results. In this study, the combination of inertial sensors embedded in consumer smartwatches and different ML models is exploited to detect bradykinesia in the upper extremities and evaluate its severity. Six PD subjects and seven age-matched healthy controls were equipped with a consumer smartwatch and asked to perform a set of motor exercises for at least 6 weeks. Different feature sets, data representations, data augmentation methods, and ML models were implemented and combined. Data recorded from smartwatches’ motion sensors, properly augmented and fed to a combination of Convolutional Neural Network and Random Forest model, provided the best results, with an accuracy of 0.86 and an area under the curve (AUC) of 0.94. Results suggest that the combination of consumer smartwatches and ML classification methods represents an unobtrusive solution for the detection of bradykinesia and the evaluation of its severity

Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease

Remote monitoring of Parkinson's Disease (PD) patients with inertia sensors is a relevant method for a better assessment of symptoms. We present a new approach for symptom quantification based on motion data: the automatic Unified Parkinson Disease Rating Scale (UPDRS) classification in combination with an animated 3D avatar giving the neurologist the impression of having the patient live in front of him. In this study we compared the UPDRS ratings of the pronation-supination task derived from: (a) an examination based on video recordings as a clinical reference;(b) an automatically classified UPDRS;and (c) a UPDRS rating from the assessment of the animated 3D avatar. Data were recorded using Magnetic, Angular Rate, Gravity (MARG) sensors with 15 subjects performing a pronation- supination movement of the hand. After preprocessing, the data were classified with a J48 classifier and animated as a 3D avatar. Video recording of the movements, as well as the 3D avatar, were examined by movement disorder specialists and rated by UPDRS. The mean agreement between the ratings based on video and (b) the automatically classified UPDRS is 0.48 and with (c) the 3D avatar it is 0.47. The 3D avatar is similarly suitable for assessing the UPDRS as video recordings for the examined task and will be further developed by the research team

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

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Attention and time constraints in performing and learning a table tennis forehand shot

This is a section on p. S95 of article 'Verbal and Poster: Motor Development, Motor Learning and Control, and Sport and Exercise Psychology' in Journal of Sport and Exercise Psychology, 2010, v.32, p.S36-S237published_or_final_versio

EEG coherence between the verbal-analytical region (T3) and the motor-planning region (Fz) increases under stress in explicit motor learners but not implicit motor learners

This journal supplement contains abstracts of NASPSPA 2010Free Communications - Verbal and Poster: Motor Learning and Controlpublished_or_final_versionThe Annual Conference of the North American Society for the Psychology of Sport and Physical Activity (NASPSPA 2010), Tucson, AZ., 10-12 June 2010. In Journal of Sport and Exercise Psychology, 2010, v. 32 suppl., p. S13