Mars Lan

SmartFall: an automatic fall detection system based on subsequence matching for the SmartCane

Fall-induced injury has become a leading cause of death for the elderly. Many elderly people rely on canes as an assistive device to overcome problems such as balance disorder and leg weakness, which are believed to have led to many incidents of falling. In this paper, we present the design and the implementation of SmartFall, an automatic fall detection system for the SmartCane system we have developed previously. SmartFall employs subsequence matching, which differs fundamentally from most existing fall detection systems based on multi-stage thresholding. The SmartFall system achieves a near perfect fall detection rate for the four types of fall conducted in the experiments. After augmenting the algorithm with an assessment on the peak impact force, we have successfully reduced the false-positive rate of the system to close to zero for all six non-falling activities performed in the experiment.

SmartLDWS: A robust and scalable lane departure warning system for the smartphones

Lane Departure Warning Systems (LDWS) have recently become an integral part of many advance vision-based drive assistance systems. However, high cost and the requirement of professional installation have limited such systems to mostly commercial or luxury vehicles. To help bring the technology to the mainstream market, we have leveraged the popularity of smartphones and built SmartLDWS, the first LDWS that runs on these devices. SmartLDWS employs a novel lane detection algorithm that is both robust and scalable to overcome poor camera quality and limited processing power faced by most smartphones. Experimental results show that the system performs reliably with extremely low false-positive under different weather and lighting conditions, detecting various types of lane markings at over 30fps.

Remote patient monitoring: what impact can data analytics have on cost?

While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This paper is particularly interested in exploring potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct a retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.

MET calculations from on-body accelerometers for exergaming movements

The use of accelerometers to approximate energy expenditure and serve as inputs for exergaming, have both increased in prevalence in response to the worldwide obesity epidemic. Exergames have a need to show energy expenditure values to validate their results, often using accelerometer approximations applied to general daily-living activities. This work presents a method for estimating the metabolic equivalent of task (MET) values achieved when users perform exergaming-specific movements. This shows the caloric expenditure achieved by active video games, based upon raw gravity values of accelerations. Results show that, while a fusion of sensors monitoring the entire body achieves the best results, sensors placed closest to the primary location of movement achieve the most accurate approximations to the METs achieved per activity as well as the overall MET achieved for the soccer exergame under consideration. The METs achieved approach 7, the value considered to be actual casual soccer game play.

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.

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.

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 systems 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.

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 %.

HERO: Hybrid Emergency Route-Opening Protocol

This paper presents an ad-hoc vehicular protocol to support the logistics of first responders. First responders often travel several kilometers to reach an area impacted by an accident or other emergency. The street-path between an emergency responder and an operational field often includes densely populated areas with busy vehicular traffic. It is key that emergency vehicles are able to traverse them safely and as quickly as possible. Vehicular communications is essential for supporting this traversal. Traditionally, this communication is accomplished through intense sirens and lights. Our protocol improves upon traditional techniques by forwarding a first responders current location and estimated path over an ad-hoc vehicular network. Vehicles lying along this path receive packets and take appropriate actions giving way to an emergency vehicle. Our protocol improves over traditional methods in two ways. First, packets propagated over a vehicular network affords a more timely notification withoug congesting the network. In addition, notifications supply vehicles with a first responders GPS location and intended path traversal. These two improvements allow vehicles to safely and efficiently clear pathways long before an emergency vehicles arrival.

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.