Joy Learning: Smartphone Application For Children With Parkinson Disease

Parkinson's is a Neurologic disorder that not only affects the human body but also their social and personal life. Especially children having the Parkinson's disease come up with infinite difficulties in different areas of life mostly in social interaction, communication, connectedness, and other skills such as thinking, reasoning, learning, remembering. This study gives the solution to learning social skills by using smartphone applications. The children having Parkinson's disease (juvenile) can learn to solve social and common problems by observing real-life situations that cannot be explained properly by instructors. The result shows that the application will enhance their involvement in learning and solving a complex problem

Track Myself:a smartphone-based tool for monitoring Parkinson’s disease

Abstract. Parkinson disease (PD) is a fast-spreading neurological disorder that affects millions of people worldwide, it hinders its patients from performing daily activities with ease. Its symptoms may vary within hours and progress differently for each patient, and usually assessed clinically every six months. It requires customized treatment plan for each patient and demands adherence of patients to complex medication regimens. The goal of this thesis is to design, implement, and test a mobile app named “Track Myself” that can help people with Parkinson’s disease (PwP) resolve these issues. The app has two components that help PwP assess their symptoms level regularly, the first component is an accelerometer-based game that detects the patient’s hand movement and calculate a score for its accuracy, the second component is a self-report symptoms survey filled by the patient every day to rate their severity level. A medication journal is implemented in the app for the patients to log their medication intakes regularly, which are prescribed by their doctors using the app as well, this help keep track of the medication history and calculate the patient’s medication adherence. The app also contains a dashboard made of three charts, representing the medication time-adherence, symptom surveys, and game scores of the patient. The purpose of this dashboard is to help the doctors form relationships between the data in the charts and determine the best future treatment plan. The app was tested for two weeks by ten healthy participants, they were asked to act in the persona of a PD patient and perform certain tasks, where information about the disease and experiences of actual patients were provided for these participants. A questionnaire was sent to the participants after the study, it consists of open-ended questions, rating statements, as well as a validated mobile health app usability questionnaire (MAUQ). The participants rated the app as easy to use for PwP in most features with mean score of 6.04/7 and perceived the app as very useful in helping PwP with mean score of 6.18/7

Designing socially acceptable mHealth technologies for Parkinson's disease self-management

Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life.Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life

Smart Wearable Device for Reduction of Parkinson’s Disease Hand-Tremor

Parkinson\u27s disease is a neurodegenerative disorder that affects over 10 million people worldwide (Health Unlocked, 2017). People diagnosed with Parkinson\u27s Disease can experience tremors, muscular rigidity and slowness of movement. Tremor is the most common symptom and external agents like stress and anxiety can make it worse, which may cause complications to complete simple day-to-day tasks. Therefore Bio Protech proposes the development of a smart wearable device for reduction of the hand-tremors based on medical evidence that by applying vibration to the wrist may result in a reduction of the involuntary tremor. The device imitates the shape of a wristwatch and the vibration is supplied by motors placed around the wrist. The users will be given the possibility to regulate the frequency according to their needs using a mobile application connected via Bluetooth

Co-designing smart home technology with people with dementia or Parkinson's disease

Involving users is crucial to designing technology successfully, especially for vulnerable users in health and social care, yet detailed descriptions and critical reflections on the co-design process, techniques and methods are rare. This paper introduces the PERCEPT (PERrsona-CEntred Participatory Technology) approach for the co-design process and we analyse and discuss the lessons learned for each step in this process. We applied PERCEPT in a project to develop a smart home toolset that will allow a person living with early stage dementia or Parkinson's to plan, monitor and self-manage his or her life and well-being more effectively. We present a set of personas which were co-created with people and applied throughout the project in the co-design process. The approach presented in this paper will enable researchers and designers to better engage with target user groups in co-design and point to considerations to be made at each step for vulnerable users

Management of social isolation and loneliness in Parkinson’s disease: Design principles

Persons with Parkinson’s disease (PwPs) may have difficulty participating in social activities due to motor and non-motor symptoms that may lead to social isolation and loneliness. This paper addresses how to manage social isolation and loneliness among PwPs using digital solutions. Information and Communication Technologies (ICT) have the potential to address social isolation and loneliness, but there are no current solutions that specifically target these issues among PwPs. In this paper, we present an ongoing project based on design science research (DSR) combined with a user-centered approach to identify challenges, requirements, and design objectives. The empirical work includes data from interviews and focus groups with PwPs and healthcare professionals. Based on the empirical material, we formulated design principles on identified challenges and requirements, which were instantiated into a high-fidelity prototype. This initial cycle serves as a foundation for ongoing improvements and evaluations in a continuous DSR process

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Future Opportunities for IoT to Support People with Parkinson’s

Recent years have seen an explosion of internet of things (IoT) technologies being released to the market. There has also been an emerging interest in the potentials of IoT devices to support people with chronic health conditions. In this paper, we describe the results of engagements to scope the future potentials of IoT for supporting people with Parkinson’s. We ran a 2-day multi-disciplinary event with professionals with expertise in Parkinson’s and IoT, to explore the opportunities, challenges and benefits. We then ran 4 workshops, engaging 13 people with Parkinson’s and caregivers, to scope out the needs, values and desires that the community has for utilizing IoT to monitor their symptoms. This work contributes a set of considerations for future IoT solutions that might support people with Parkinson’s in better understanding their condition, through the provision of objective measurements that correspond to their, currently unmeasured, subjective experiences