A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson s disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson s disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability

The Effect of Balance Training on Postural Control in Patients with Parkinson s Disease Using a Virtual Rehabilitation System

[EN] Objectives: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor clinical alterations among others. Postural problems have serious consequences for patients, not only limiting their daily life but also increasing some risks, like the risk of fall. Inadequate postural control and postural instability is a major problem in PD patients. A Virtual Motor Rehabilitation System (VMR) has been tested in patients with PD in the intervention period. Our pur-pose was to analyze the evolution of the spatial postural control during the intervention period, to see if there are any changes caused precisely by this intervention. Methods: Ten people with PD carried out 15 virtual rehabilitation sessions. We tested a groundbreaking system based on Virtual Motor Rehabilitation in two periods of time (baseline evaluation and final evaluation). In the training sessions, the participants performed a customizable treatment using a low-cost system, the Active Balance Rehabilitation system (ABAR). We stored the pressure performed by the participants every five hundredths of a second, and we analyzed the patients' pressure when they maintained their body on the left, on the right, and in the center in sitting position. Our system was able to measure postural control in every patient in each of the virtual rehabilitation sessions. Results: There are no significant differences in the performance of postural control in any of the positions evaluated throughout the sessions. Moreover, the results show a trend to an improvement in all positions. This improvement is especially remarkable in the left/right positions, which are the most important positions in order to avoid problems such as the risk of fall. With regard to the suitability of the ABAR system, we have found outstanding results in enjoyment, success, clarity, and helpfulness. Conclusions: Although PD is a progressive neurodegenerative disorder, the results demonstrate that patients with PD maintain or even improve their postural control in all positions. We think that the main factor influencing these results is that patients use more of their available cognitive processing to improve their postural control. The ABAR system allows us to make this assumption because the system requires the continuous attention of patients, promoting cognitive processing.This contribution was partially funded by the Gobierno de Aragon, Departamento de Industria e Innovacion, y Fondo Social Europeo "Construyendo Europa desde Aragon" and by the Programa Ibercaja-CAI de Estancias de Investigacion.Albiol-Perez, S.; Gil-Gómez, J.; Muñoz-Tomás, M.; Gil Gómez, H.; Vial Escolano, R.; Lozano Quilis, JA. (2017). 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A smartphone-based system for detecting hand tremors in unconstrained environments

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based applica- tion has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volun- teers for several days, which corroborated its high performance

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

PD_manager: an mHealth platform for Parkinson's disease Management

Parkinson’s disease (PD) current clinical management is mostly based on patient’s subjective report about the effects of treatments and on medical examinations that unfortunately represent only a snapshot of a highly fluctuating clinical condition. This traditional approach requires time, it is biased by patient’s judgment and is often not completely reliable, especially in moderate advanced stages. The main purpose of the EU funded project PD_manager (Horizon 2020, Grant Agreement n° 643706) is to build and evaluate an innovative, mHealth, patient-centric system for PD remote monitoring. After a first phase of research and development, a set of wearable devices has been selected and tested on 20 patients. The raw data recorded have been used to feed algorithms necessary to recognize motor symptoms. In parallel, other applications have been developed to test also the main non-motor symptoms. On a second phase, a case- control randomized multicentric study has been designed and performed to assess the acceptability and utility of the PD_manager system at patients’ home, compared to the current gold standard for home monitoring, represented by symptoms diaries. 136 couples of patients and caregivers have been recruited, and at the end of the trial the system was found to be very well tolerated and easy to use, compared to diaries. The developed System is able to recognize motor and non-motor symptoms, helping healthcare professionals in taking decisions on therapeutic strategies. Moreover, PD_manager could represent a useful tool for patient's self-monitoring and self-care promotion