A pilot study using global positioning systems (GPS) devices and surveys to ascertain older adults' travel patterns.

Some studies indicate that older adults lead active lives and travel to many destinations including those not in their immediate residential neighborhoods. We used global positioning system (GPS) devices to track the travel patterns of 40 older adults (mean age: 69) in San Francisco and Los Angeles. Study participants wore the GPS devices for 7 days in fall 2010 and winter 2011. We collected survey responses concurrently about travel patterns. GPS data showed a mean of four trips/day, and a mean trip distance of 7.6 km. Survey data indicated that older adults commonly made trips for four activities (e.g., volunteering, work, visiting friends) at least once each week. Older adults regularly travel outside their residential neighborhoods. GPS can document the mode of travel, the path of travel, and the destinations. Surveys can document the purpose of the travel and the impressions or experiences in the specific locations.

Generalized precursor pattern discovery for biomedical signals.

With the advent of low-cost, high-fidelity, and long lasting sensors in recent years, it has become possible to acquire biomedical signals cheaply and remotely over a prolonged period of time. Oftentimes different types of sensors are deployed in the hope of capturing precursor patterns that are highly correlated to a particular clinical episode, such as seizure, congestive heart failure etc. While there have been several studies that successfully identify patterns as reliable precursors for specific medical conditions, most of them require domain-specific knowledge and expertise. The developed algorithms are also unlikely to be applicable to other medical conditions. In this paper we present a generalized algorithm that discovers potential precursor patterns without prior knowledge or domain expertise. The algorithm makes use of wavelet transform and information theory to extract generic features, and it is also classifier agnostic. Based on experiment results using three distinct datasets collected from real-world patients, our algorithm has attained performance comparable to those obtained from previous studies that rely heavily on domain-expert knowledge. Furthermore, the algorithm also discovers non-trivial knowledge in the process.

Dynamic self-adaptive remote health monitoring system for diabetics.

Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Wireless data collection of self-administered surveys using tablet computers.

The accurate and expeditious collection of survey data by coordinators in the field is critical in the support of research studies. Early methods that used paper documentation have slowly evolved into electronic capture systems. Indeed, tools such as REDCap and others illustrate this transition. However, many current systems are tailored web-browsers running on desktop/laptop computers, requiring keyboard and mouse input. We present a system that utilizes a touch screen interface running on a tablet PC with consideration for portability, limited screen space, wireless connectivity, and potentially inexperienced and low literacy users. The system was developed using C#, ASP.net, and SQL Server by multiple programmers over the course of a year. The system was developed in coordination with UCLA Family Medicine and is currently deployed for the collection of data in a group of Los Angeles area clinics of community health centers for a study on drug addiction and intervention.

Salient Segmentation of Medical Time Series Signals.

Searching and mining medical time series databases is extremely challenging due to large, high entropy, and multidimensional datasets. Traditional time series databases are populated using segments extracted by a sliding window. The resulting database index contains an abundance of redundant time series segments with little to no alignment. This paper presents the idea of "salient segmentation". Salient segmentation is a probabilistic segmentation technique for populating medical time series databases. Segments with the lowest probabilities are considered salient and are inserted into the index. The resulting index has little redundancy and is composed of aligned segments. This approach reduces index sizes by more than 98% over conventional sliding window techniques. Furthermore, salient segmentation can reduce redundancy in motif discovery algorithms by more than 85%, yielding a more succinct representation of a time series signal.

Missing data imputation for remote CHF patient monitoring systems.

Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the system's effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. In this study, we exploit machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall. This approach also shows an improved ability to predict missing data when training on entire populations, as opposed to training unique classifiers for each individual.