PARK: Parkinson's Analysis with Remote Kinetic-tasks

Parkinsonism & Related Disorders, 2021

There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS). Methods: A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. Results: Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. Conclusion: Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.

Sensors, 2014

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired

Neurology Research International, 2016

Purpose.To evaluate the feasibility of assessing a person’s symptoms of Parkinson’s disease (PD) in their home using the videoconferencing technology they already possess, without a home visit.Method.Eleven participants with PD completed the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) face-to-face and then via videoconferencing within a two-week period. Participants used free software and the computers and webcams available at their home to complete the videoconference assessment with a clinical rater scoring remotely. Clinical raters and participants provided feedback on the experience.Results.Excluding rigidity and postural stability, between zero and seven items could not be completed in the assessment of each participant (median 2.0, IQR 1.0–4.0). Between face-to-face and videoconference assessments, the median difference in scores was 3.0 (IQR 1.5–9.0). Content analysis of feedback identified the clinical raters’ reasons why some scoring could...

Journal of Medical Internet Research, 2021

Background Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases—fueled mostly by environmental pollution and an aging population—can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective In this paper, we propose a web-based framework that can help anyone anywhere around the world recor...

Journal of Neuroscience Methods, 2012

Patients with Parkinson's disease (PD) receive therapies aimed at addressing a diverse range of motor symptoms. Motor complications in the form of symptom fluctuations and dyskinesias that commonly occur with chronic PD medication use may not be effectively captured by Unified Parkinson's Disease Rating Scale (UPDRS) assessments performed in the clinic. Therefore, home monitoring may be a viable adjunct tool to provide insight into PD motor symptom response to treatment. In this pilot study, we sought to evaluate the feasibility of capturing PD motor symptoms at home using a computer-based assessment system. Ten subjects diagnosed with idiopathic PD used the system at home and ten non-PD control subjects used the system in a laboratory. The Kinesia system consists of a wireless finger-worn motion sensor and a laptop computer with software for automated tremor and bradykinesia severity score assessments. Data from control subjects were used to develop compliance algorithms for rejecting motor tasks performed incorrectly. These algorithms were then applied to data collected from the PD subjects who used the Kinesia system at home to complete motor exams 3-6 times per day over 3-6 days. Motor tasks not rejected by the compliance algorithms were further processed for symptom severity. PD subjects successfully completed motor assessments at home, with approximately 97% of all motor task data files (1222/1260) accepted. These findings suggest that objective home monitoring of PD motor fluctuations is feasible.

npj Digital Medicine, 2023

We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson's disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0-4, following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters' average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.

Parkinsonism & related disorders, 2015

Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Twenty participants performed an average of 2.7 ...

2010

Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a webbased application provide reliable quantitative information that can be used for clinical decision making.

2014

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired

Artificial Intelligence in Medicine, 2018

Background and objective: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end. Methods: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. Results: The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and ehealth monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis. Conclusions: The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016.

Movement Disorders, 2012

2021

Hypomimia is a condition in the early stages of the progression of Parkinson's disease that limits the movement of facial muscles, restricting accurate depiction of facial expressions. Also known as facial masking, this condition is an early symptom of Parkinson's disease, a neurodegenerative disorder that affects movement. To date, no specific test exists to diagnose the disease. Instead, doctors rely on the patient’s medical history and symptoms to confirm the onslaught of the disease, delaying treatment. This study aims to develop a diagnostic tool for Parkinson’s disease utilizing the Facial Action Coding System, a comprehensive system describing all facially discernible movement. In addition, this project generates image datasets by simulating faces or action unit sets for both Parkinson’s patients and non-affected individuals through coding. Accordingly, the model is trained using supervised learning and neural networks. The model’s efficiency is compared using Convolu...

Using a Mobile Phone to monitor the progression of Parkinson's with Non-invasive tests, 2022

Parkinson's Disease is a neurodegenerative disorder caused by a loss of nerves in the brain, specifically in the Substantia Nigra. This part of the brain is responsible for creating dopamine, a chemical transmitter, and hormone used by neurons in a paracrine connection. This hormone is correlated with feelings of pleasure, however, it has further purposes such as learning, memory, and most physically relevant to Parkinson's Disease-motor control. Today, there is no effective way of monitoring Parkinson's Disease in a clinical setting. So far, the only way to detect the signs of Parkinson's Disease is to study the patient's ancestry in relation to Parkinson's and to perform qualitative tests monitored by a physician. Through this project, "MobiTest", I aim to digitize these tests to monitor the progression of Parkinson's Disease. The main goal of this project is to ensure that constant monitoring no longer requires the assistance of a physician, and to bring monitoring to those suffering from Parkinson's disease all over the world-onto their smartphones. MobiTest utilizes Advanced Statistics algorithms and Support Vector Classifiers to quantify drawings of spirals into a score on a UPDRS (Unified Parkinson's Disease Rating Scale) from 1 to 5, where 1 is least severe and 5 is most severe. MobiTest also provides charts in which users can identify trends in their scores that can be sent to physicians. Apart from progression monitoring, MobiTest also includes a specialized keyboard that serves to help the caretakers of speech-impaired Parkinson's Disease patients to look up commonly used words. This keyboard uses a Bayesian theorem for a progressive NLP prediction algorithm to make suggestions for a fast and efficient lookup time.

Sensors

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, ...

Sensors, 2021

Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a med...