Time filter

Source Type

San Diego, CA, United States

West Health is an initiative combining four organizations with a common mission of pioneering new and smarter technologies, policies and practices, to make high-quality healthcare more accessible at a lower cost to all Americans. The ecosystem includes the Gary and Mary West Health Institute, the Gary and Mary West Health Policy Center, the Gary and Mary West Health Investment Fund, and the West Health Incubator. Unveiled Aug. 15, 2012, the organization was founded on March 30, 2009 as the Gary and Mary West Wireless Health Institute. Wikipedia.

Ghasemzadeh H.,West Wireless Health Institute | Jafari R.,University of Texas at Dallas
IEEE Transactions on Industrial Informatics

Monitoring human activities using wearable sensor nodes has the potential to enable many useful applications for everyday situations. Limited computation, battery lifetime and communication bandwidth make efficient use of these platforms crucial. In this paper, we introduce a novel classification model that identifies physical movements from body-worn inertial sensors while taking collaborative nature and limited resources of the system into consideration. Our action recognition model uses a decision tree structure to minimize the number of nodes involved in classification of each action. The decision tree is constructed based on the quality of action recognition in individual nodes. A clustering technique is employed to group similar actions and measure quality of per-node identifications. We pose an optimization problem for finding a minimal set of sensor nodes contributing to the action recognition. We then prove that this problem is NP-hard and provide fast greedy algorithms to approximate the solution. Finally, we demonstrate the effectiveness of our distributed algorithm on data collected from five healthy subjects. In particular, our system achieves a 72.4% reduction in the number of active nodes while maintaining 93.3% classification accuracy. © 2006 IEEE. Source

Roham M.,West Wireless Health Institute
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

This paper reports on a miniaturized device for wireless monitoring of extracellular dopamine levels in the brain of an ambulatory rat using fast-scan cyclic voltammetry at a carbon-fiber microelectrode. The device comprises integrated circuitry for neurochemical recording fabricated in 0.5-microm double-poly triple-metal CMOS technology, which is assembled and packaged on a miniature rigid-flex substrate together with a few external components for supply generation, biasing, and chip programming. The device operates from a single 3-V battery, weighs 2.3 g (including the battery), and upon implantation successfully captures the effects of the psychostimulant amphetamine on electrically and non-electrically evoked dopamine neurotransmission in the caudateputamen region of an ambulatory rat's forebrain. Source

Loseu V.,University of Texas at Dallas | Ghasemzadeh H.,University of Texas at Dallas | Ghasemzadeh H.,West Wireless Health Institute | Jafari R.,University of Texas at Dallas
Proceedings of the IEEE

Recent years have witnessed a large influx of applications in the field of cyber-physical systems. An important class of these systems is body sensor networks (BSNs) where lightweight embedded processors and communication systems are tightly coupled with the human body. BSNs can provide researchers, care providers and clinicians access to tremendously valuable information extracted from data that are collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset, or simply assess user's wellness. One major obstacle is managing repositories that store the large amount of sensing data. To address this issue, we propose a data mining approach inspired by the experience in the areas of text and natural language processing. We represent sensor readings with a sequence of characters, called motion transcripts. Transcripts reduce complexity of the data significantly while maintaining morphological and structural properties of the physiological signals. To further take advantage of the physiological signal's structure, our data mining technique focuses on the characteristic transitions in the signals. These transitions are efficiently captured using the concept of n-grams. To facilitate a lightweight and fast mining approach, we reduce the overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of the proposed approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of the F-score combining both precision and recall. © 2012 IEEE. Source

Pantelopoulos A.,West Wireless Health Institute
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

In this paper a wireless modular, multi-modal, multi-node patch platform is described. The platform comprises low-cost semi-disposable patch design aiming at unobtrusive ambulatory monitoring of multiple physiological parameters. Owing to its modular design it can be interfaced with various low-power RF communication and data storage technologies, while the data fusion of multi-modal and multi-node features facilitates measurement of several biosignals from multiple on-body locations for robust feature extraction. Preliminary results of the patch platform are presented which illustrate the capability to extract respiration rate from three different independent metrics, which combined together can give a more robust estimate of the actual respiratory rate. Source

Majerus S.J.A.,Case Western Reserve University | Fletter P.C.,Advanced Platform Technology Center | Damaser M.S.,Advanced Platform Technology Center | Garverick S.L.,West Wireless Health Institute
IEEE Transactions on Biomedical Engineering

This letter describes the design, fabrication, and testing of a wireless bladder-pressure-sensing system for chronic, point-of-care applications, such as urodynamics or closed-loop neuromodulation. The system consists of a miniature implantable device and an external RF receiver and wireless battery charger. The implant is small enough to be cystoscopically implanted within the bladder wall, where it is securely held and shielded from the urine stream. The implant consists of a custom application-specific integrated circuit (ASIC), a pressure transducer, a rechargeable battery, and wireless telemetry and recharging antennas. The ASIC includes instrumentation, wireless transmission, and power-management circuitry, and on an average draws less than 9 μA from the 3.6-V battery. The battery charge can be wirelessly replenished with daily 6-h recharge periods that can occur during the periods of sleep. Acute in vivo evaluation of the pressure-sensing system in canine models has demonstrated that the system can accurately capture lumen pressure from a submucosal implant location. © 2006 IEEE. Source

Discover hidden collaborations