Aca Gacic

Multisensor Fusion in Smartphones for Lifestyle Monitoring

Smartphones with diverse sensing capabilities are becoming widely available and pervasive in use. With the phone becoming a mobile personal computer, integrated applications can use multi-sensory data to derive information about the users actions and the context in which these actions occur. This paper develops a novel method to assess daily living patterns using a smartphone equipped with microphones and inertial sensors. We develop a feature-space combination approach for fusion of information from sensors sampled at different rates and present a computationally light-weight algorithm to identify various high level activities. Preliminary results from an initial deployment among eight users indicate the potential for accurate, context-aware, and personalized sensing.

Towards automatic detection of falls using wireless sensors

Accurate detection of falls leading to injury is essential for providing timely medical assistance. In this paper, we describe a wireless sensor network system for automatic fall detection. To detect falls, we use a combination of a body- worn triaxial accelerometer with motion detectors placed in the monitored area. While accelerometer provides information about the body motion during a fall, motion detectors monitor general presence or absence of motion. From all sensors, the data is transmitted wirelessly using the IEEE 802.15.4 protocol to a central node for processing. We use an implementation of carrier sense multiple access - collision avoidance scheme for channel reuse. A simple forwarding scheme is used to provide an extended coverage for a home environment. Fall detection is accomplished by a two-stage algorithm that utilizes the triaxial acceleration and the motion data sequentially. In the first stage, the algorithm detects plausible falls using a measure of normalized energy expenditure computed from the dynamic acceleration values. In the second stage, falls are confirmed based on the absence of motion. Systematic evaluation on simulated falls using 15 adult subjects shows that the proposed system provides a highly promising solution for real-time fall detection.

A data-driven approach towards patient identification for telehealth programs

Telehealth provides an opportunity to reduce healthcare costs through remote patient monitoring, but is not appropriate for all individuals. Our goal was to identify the patients for whom telehealth has the greatest impact, as measured through cost savings and patient engagement. For prediction of cost savings, challenges included the high variability of medical costs and the effect of selection bias on the cost difference between intervention patients and controls. Using Medicare claims data, we computed cost savings by comparing each telehealth patient to a group of control patients who had similar healthcare resource utilization. These estimates were then used to train a predictive model using logistic regression. Filtering the patients based on the model resulted in an average cost savings of

Detection of anomalous events from unlabeled sensor data in smart building environments

10K in the group of patients with the highest healthcare utilization, an improvement over the current expected loss of

Patient Identification for Telehealth Programs

2K (without filtering). Groups of patients with lower healthcare utilization also showed improvement, though less pronounced. To identify highly engaged patients, we developed predictive models of telehealth compliance and of patient satisfaction. Performance of these models were generally poor, with an AUC ranging from 0.54 to 0.64.

Activity monitoring device and method

This paper presents a robust unsupervised learning approach for detection of anomalies in patterns of human behavior using multi-modal smart environment sensor data. We model the data using a Gaussian Mixture Model, where the features are weighted based on their discriminative ability and are simultaneously clustered. The number of clusters in this approach is automatically chosen using the Minimum Message Length (MML) criterion. The weight of non-discriminative features is driven towards zero which results in a form of dimensionality reduction. Our results indicate that, in practical applications involving unlabeled, high-dimensional multi-modal sensor data from smart building environments, feature weighting achieves higher accuracy in detecting anomalous events with lower false alarm rates compared to using traditional Gaussian Mixtures.

Method and apparatus for customizing a wireless network architecture

Telehealth provides an opportunity to reduce healthcare costs through remote patient monitoring, but is not appropriate for all individuals. Our goal was to identify the patients for whom telehealth has the greatest impact. Challenges included the high variability of medical costs and the effect of selection bias on the cost difference between intervention patients and controls. Using Medicare claims data, we computed cost savings by comparing each telehealth patient to a group of control patients who had similar healthcare resource utilization. These estimates were then used to train a predictive model using logistic regression. Filtering the patients based on the model resulted in an average cost savings of

Wearable system for monitoring strength training

10K, an improvement over the current expected loss of

Design and implementation of an automated human activity monitoring application for wearable devices

2K (without filtering).