Browsing by Subject "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 Pilot or Watchdog? A Theory of Endogenous Choice of Advisory Role by Boards of Directors(2005) Chen, DongAbstract Corporate board is a multi-role institutional entity. In a stylized two period symmetric information model, we derive the optimal choice of advising role by directors given that every board has to perform a monitoring role. It is argued that advising the management,has cost as well as benet.,Even though advising increases the mean of the prot, board shouldn’t advise unless its prior ability exceeds CEO’s by a positive cuto,value. We prove that this cuto,value is higher under \strong owner" characterized by concentrated shareholding than \weak owner" characterized by diused,ownership structure, which suggests one potential negative eect,imposed by large shareholder setup. We also prove that instead of committing to,re the board after one period to induce their advising benet,under large shareholder setup, shareholders might just be better o to be back to diused ownership case, aside from the consideration of large shareholder extracting private benet,from the control of the corporation.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 Smart-Energy Group Anomaly Based Behavioral Abnormality Detection(IEEE, 2016-12-15) Alam, Mohammad Arif Ul; Roy, Nirmalya; Petruska, Michelle; Zemp, AndreaMonitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance's energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.