A temporal analysis system for early detection of health changes

Abstract from public.pdf.To make it possible for elders to live independently at home and yet get help from health care providers when small changes in health conditions take place, smart home technologies are developed to enhance safety and monitor health conditions via noninvasive sensors and other devices. To better analyze the wealth of the activity information from various kinds of sensors to locate trends that correspond states of wellbeing, this thesis proposes a new system to build adaptive models for detecting health changes based on temporal analysis, including outlier detection, customization and adaption to new changes. Our hope is that by using more sophisticated temporal analysis method we can capture more predictive alerts and more customized alerts that can help us detect more meaningful health changes before they become big problems. Since we cannot have full access to all the embedded sensor data from TigerPlace at the moment, the system is tested using synthetic datasets which simulate gradual changes, sudden changes, changes of baseline health condition and system noise that might happen in the real-world data. Based on the experiments on the synthetic datasets, the system is proved to have the ability to adapt to gradual changes, find anomalies and spawn a new component for the GMM when there is an emerging new normal pattern. The system achieves our goals when tested on the synthetic datasets over extended period of time. We hope that by using the system in Tiger Place, it will help by detecting health changes before real health issue happens

Characterizing rescue performance in a tertiary care medical center: a systems approach to provide management decision support

Background: Allocation of limited resources to improve quality, patient safety, and outcomes is a decision-making challenge health care leaders face every day. While much valuable health care management research has concentrated on administrative data analysis, this approach often falls short of providing actionable information essential for effective management of specific system implementations and complex systems. This comprehensive performance analysis of a hospital-wide system illustrates application of various analysis approaches to support understanding specific system behaviors and identify leverage points for improvement. The study focuses on performance of a hospital rescue system supporting early recognition and response to patient deterioration, which is essential to reduce preventable inpatient deaths. Methods: Retrospective analysis of tertiary care hospital inpatient and rescue data was conducted using a systems analysis approach to characterize: patient demographics; rescue activation types and locations; temporal patterns of activation; and associations of patient factors, including complications, with post-rescue care disposition and outcomes. Results: Increases in bedside consultations (20% per year) were found with increased rescue activations during periods of resource limitations and changes (e.g., shift changes, weekends). Cardiac arrest, respiratory failure, and sepsis complications present the highest risk for rescue and death. Distributions of incidence of rescue and death by day of patient stay may suggest opportunities for earlier recognition. Conclusions: Specific findings highlight the potential of using rescue-related risk and targeted resource deployment strategies to improve early detection of deterioration. The approach and methods applied can be used by other institutions to understand performance and allow rational incremental improvements to complex care delivery systems

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.status: publishe

Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.Comment: 6 pages, conference paper, already accepted at hotmobile 201