Personal Health and Wellness Management with Technologies

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Now showing 1 - 5 of 11
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    Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis
    ( 2019-01-08) Rastegari, Elham ; Azizian, Sasan ; Ali, Hesham
    Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD.
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    Browsing to Breathe: Social Media for Stress Reduction
    ( 2019-01-08) Coates, Rebecca ; Sykora, Martin ; Jackson, Tom
    In a pressurized world, it is important that research continually works towards discovering new ways to improve the mental and physical wellness of society. Traditional approaches for measuring stress have been vastly explored, however rising concerns for chronic stress calls for new methodologies to sense stress on Social Media, which, as a tool, could provide valuable insight into wellness. Over a period of two-weeks, a rigorous mixed methods approach (daily surveys, Social Media data collection and post-study interviews) aided the discovery that Social Media, particularly browsing, can improve the wellness of placement students, as it helped them to cope with stress. The adoption of an established coping survey for stress helped in the identification of behavioral differences between participants. This paper explores the positive impact that Social Media can have on stress and highlights the potential of digital coping mechanisms.
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    DYNECOM: Augmenting Empathy in VR with Dyadic Synchrony Neurofeedback
    ( 2019-01-08) Järvelä, Simo ; Salminen, Mikko ; Ruonala, Antti ; Timonen, Janne ; Mannermaa, Kristiina ; Ravaja, Niklas ; Jacucci, Giulio
    In a novel experimental setting, we augmented a variation of traditional compassion meditation with our custom built VR environment for multiple concurrent users. The system incorporates respiration and brainwave based biofeedback that enables responsiveness to the shared physiological states of the users. The presence of another user’s avatar in the shared virtual space supported low level social interactions and provided active targets for evoked compassion. We enhanced interoception and the deep empathetic processes involved in compassion meditation with real time visualizations of breathing rates and the level of approach motivation assessed from EEG frontal asymmetry, and the dyadic synchrony of those signals between the two users. We found how the different biofeedback types increased both the amount of physiological synchrony between the users and their self-reported empathy, illustrating how dyadic synchrony biofeedback can expand the possibilities of biofeedback in affective computing and VR solutions for health and wellness.
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    Location-based Mobile Games in mHealth: A Preliminary Study of Pokémon Go in Promoting Health Exercising
    ( 2019-01-08) Wei, Fang-Yi Flora ; Wang, Ken
    Location-based mobile games such as Pokémon Go might improve players’ physical activities (e.g., walking) and social interactions. With a limited research on mobile exergaming activities, this study examined relationships among Pokémon Go players’ gaming activities, willingness to communicate, and the likelihood of engaging in exercises. Our study showed that the longer participants had been playing the game, the higher the likelihood that they would engage in exercises. Our findings revealed a positive relationship between exercise during gameplay and willingness to communicate with other players. Our study provides implications to the use of location-based mobile games to promote health campaigns and improve the general health of the population.
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    HalleyAssist: A Personalised Internet of Things Technology to Assist the Elderly in Daily Living
    ( 2019-01-08) Forkan, Abdur Rahim Mohammad ; Branch, Philip ; Jayaraman, Prem Prakash ; Ferretto, Andre