Browsing by Subject "Biomedical monitoring"
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Item Automated Functional and Behavioral Health Assessment of Older Adults with Dementia(IEEE, 2016-08-18) Alam, Mohammad Arif Ul; Roy, Nirmalya; Holmes, Sarah; Gangopadhyay, Aryya; Galik, ElizabethDementia is a clinical syndrome of cognitive deficits that involves both memory and functional impairments. While disruptions in cognition is a striking feature of dementia, it is also closely coupled with changes in functional and behavioral health of older adults. In this paper, we investigate the challenges of improving the automatic assessment of dementia, by better exploiting the emerging physiological sensors in conjunction with ambient sensors in a real field environment with IRB approval. We hypothesize that the cognitive health of older individuals can be estimated by tracking their daily activities and mental arousal states. We employ signal processing on wearable sensor data streams (e.g., Electrodermal Activity (EDA), Photoplethysmogram (PPG), accelerometer (ACC)) and machine learning algorithms to assess cognitive impairments and its correlation with functional health decline. To validate our approach, we quantify the score of machine learning, survey and observation based Activities of Daily Living (ADLs) and signal processing based mental arousal state, respectively for functional and behavioral health measures among 17 older adults living in a continuing care retirement community in Baltimore. We compare clinically observed scores with technology guided automated scores using both machine learning and statistical techniques.Item Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling(IEEE, 2015-09-14) Hossain, H M Sajjad; Roy, Nirmalya; Khan, MD Abdullah Al HafizFollowing healthy lifestyle is a key for active living. Regular exercise, controlled diet and sound sleep play an invisible role on the well being and independent living of the people. Sleep being the most durative activities of daily living (ADL) has a major synergistic influence on people's mental, physical and cognitive health. Understanding the sleep behavior longitudinally and its underpinning clausal relationships with physiological signals and contexts (such as eye or body movement etc.) horizontally responsible for a sound or disruptive sleep pattern help provide meaningful information for promoting healthy lifestyle and designing appropriate intervention strategy. In this paper we propose to detect the microscopic states of the sleep which fundamentally constitute the components of a good or bad sleeping behavior and help shape the formative assessment of sleep quality. We initially investigate several classification techniques to identify and correlate the relationship of microscopic sleep states with the overall sleep behavior. Subsequently we propose an online algorithm based on change point detection to better process and classify the microscopic sleep states and then test a lightweight version of this algorithm for real time sleep monitoring activity recognition and assessment at scale. For a larger deployment of our proposed model across a community of individuals we propose an active learning based methodology by reducing the effort of ground truth data collection. We evaluate the performance of our proposed algorithms on real data traces, and demonstrate the efficacy of our models for detecting and assessing fine-grained sleep states beyond an individual.Item SoccerMate: A Personal Soccer Attribute Profiler using Wearables(IEEE, 2017-05-04) Hossain, H M Sajjad; Khan, Md Abdullah Al Hafiz; Roy, NirmalyaThe use of smartphone and wearable devices in various sporting events is an optimistic opportunity to profile player's physical fitness and physiological health conditioning attributes. Recently a variety of commercial wearables with respect to different sports are available in the market. As these wearables differ for distinctive sports, it becomes a hassle to effectively profile them for multiple sports sessions in day to day practice events. Wrist worn devices like smartwatches are becoming a trend in sports analytics recently and researchers are leveraging them to infer various contexts of the players to improve the quality, tactics, strategy of playing matches against the opponents. Visual observation is the most popular way to track a player's abilities in soccer, but as a player it is not always possible to self-assess your own strengths and weaknesses in a field. In this paper, we propose to exploit the wrist worn devices with built in accelerometer to help represent attributes of technical judgement, tactical awareness and physical aspects of a soccer player. We propose to use deep learning to build our classification model which analyzes different soccer events like in-possession, pass, kick, sprint, run and dribbling. Based on these soccer events, we evaluate the overall ability of a soccer player. Our experiments show that, these wearable technology guided attributes profiling can help a coach or scout to better understand the competence of a player in addition to traditional visual observation