Victor Chen

WANDA B.: Weight and activity with blood pressure monitoring system for heart failure patients

Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial.

Autonomous Robotic Sensing Experiments at San Joaquin River

Distributed, high-density spatiotemporal observations are proposed for answering many river related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. In spite of the recent advancements in technology, currently available systems have many constraints that preclude long term, remote, autonomous, high resolution monitoring in the real environment. We present here a case study of an autonomous, high resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. The scientific objective of this investigation was to characterize the transport and mixing phenomena at the confluence of two distinctly different river streams - San Joaquin River and its tributary Merced River. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. These include deterministic dense raster scans and in-field adapted experimental design. Preliminary analysis from these experiments illustrating the range of investigations is presented with the focus on adaptive experiments that enable sparse sampling to provide larger spatial coverage without discounting the dynamics in the phenomena. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

Mobile Robot Sensing for Environmental Applications

This paper reports the first application of iterative experimental design methodology for high spatiotemporal resolution characterization of river and lake aquatic systems performed using mobile robot sensing systems. Both applications involve dynamic phenomena spread over large spatial domain: 1) Characterization of contaminant concentration and flow at the confluence of two major rivers displaying dynamics due to flow of the water; and 2) Characterization of rapidly evolving biological processes such as phytoplankton dynamics in a lake system. We describe the development and application of a new general purpose method for mobile robot sensing in such environments - Iterative experiment Design for Environmental Applications (IDEA). IDEA introduces in-field adaptation of mobile robotic sensing system. Analysis of the complex spatial and temporal structures associated with each observed environment is presented. Detailed characterization of the observed environment using IDEA methodology is used as an informed prior to improve the performance of the existing adaptive experimental design approaches for mobile robotic systems - stratified adaptive sampling and hierarchical non-stationary Gaussian Processes.

Towards spatial and semantic mapping in aquatic environments

High fidelity data acquisition of dynamic spatiotemporal phenomena for aquatic environmental research suggests the use of actuated sensors. Furthermore, characterization of the floor in aquatic environments is beneficial for environmental science, as well as can be applied to robot localization. The NIMS AQ cable robot platform is designed to meet these requirements and satisfy the constraints of large scale, in-field deployments. In addition to a set of water quality sensors it also carries an ultra-miniature side-scan sonar. In this paper we show the development of methods for autonomous range detection, spatial and semantic mapping in underwater environments. These methods are demonstrated to be important for future developments including localization, navigation, and path planning, particularly for 3D mobility. Experiments have been performed in both controlled environments and a lake environment and results are discussed.

Human Assisted Robotic Team Campaigns for Aquatic Monitoring

Large-scale environmental sensing, e.g., understanding microbial processes in an aquatic ecosystem, requires coordination across a multidisciplinary team of experts working closely with a robotic sensing and sampling system. We describe a human-robot team that conducted an aquatic sampling campaign in Lake Fulmor, San Jacinto Mountains Reserve, California during three consecutive site visits (May 9–11, June 19–22, and August 28–31, 2006). The goal of the campaign was to study the behavior of phytoplankton in the lake and their relationship to the underlying physical, chemical, and biological parameters. Phytoplankton form the largest source of oxygen and the foundation of the food web in most aquatic ecosystems. The reported campaign consisted of three system deployments spanning four months. The robotic system consisted of two subsystems—NAMOS (networked aquatic microbial observing systems) comprised of a robotic boat and static buoys, and NIMS-RD (rapidly deployable networked infomechanical systems) comprised of an infrastructure-supported tethered robotic system capable of high-resolution sampling in a two-dimensional cross section (vertical plane) of the lake. The multidisciplinary human team consisted of 25 investigators from robotics, computer science, engineering, biology, and statistics.We describe the lake profiling campaign requirements, the robotic systems assisted by a human team to perform high fidelity sampling, and the sensing devices used during the campaign to observe several environmental parameters. We discuss measures taken to ensure system robustness and quality of the collected data. Finally, we present an analysis of the data collected by iteratively adapting our experiment design to the observations in the sampled environment. We conclude with the plans for future deployments.

Coordinated static and mobile sensing for environmental monitoring

Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. While substantial progress in sensor network performance has appeared, new challenges have also emerged as these systems have been deployed in the natural environment. First, in order to achieve minimum sensing fidelity performance, the rapid spatiotemporal variation of environmental phenomena requires impractical deployment densities. The presence of obstacles in the environment introduces sensing uncertainty and degrades the performance of sensor fusion systems in particular for the many new applications of image sensing. The physical obstacles encountered by sensing may be circumvented by a new mobile sensing method or Networked Infomechanical Systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility. NIMS now includes coordinated mobility methods that exploits adaptive articulation of sensor perspective and location as well as management of sensor population to provide the greatest certainty in sensor fusion results. The architecture, applications, and implementation of NIMS will be discussed here. In addition, results of environmentally-adaptive sampling, and direct measurement of sensing uncertainty will be described.