In-Home Behavioral Monitoring Using Simultaneous Localization and Activity Detection.

Shifting demographics in the U.S. has created an urgent need to reform the policies, practices, and technology associated with delivering healthcare to geriatric populations. Automated monitoring systems can improve the quality of life while reducing healthcare costs for individuals aging in place. For these systems to be successful, both activity detection and localization are important, but most existing research focuses on only one of these technologies and systems that do collect both data treat these data sources separately. Here, we present SLAD {Simultaneous Localization and Activity Detection a novel framework for simultaneously processing data collected from localization and activity classification systems. Using a hidden Markov model and machine learning techniques, SLAD fuses these two sources of data in realtime using a probabilistic likelihood framework, which allows activity data to refine localization, and vice-versa. To evaluate the system, a wireless sensor network was deployed to collect RSSI data and IMU data concurrently from a wrist-worn watch; the RSSI data was processed using a radial basis function neural network localization algorithm, and the resulting position likelihoods were combined with the likelihoods from an IMU acitivty classification algorithm. In an experiment conducted in an indoor office environment, the proposed method produces 97% localization accuracy and 85% activity classification.

Smart watch RSSI localization and refinement for behavioral classification using laser-SLAM for mapping and fingerprinting.

As a first step toward building a smart home behavioral monitoring system capable of classifying a wide variety of human behavior, a wireless sensor network (WSN) system is presented for RSSI localization. The low-cost, non-intrusive system uses a smart watch worn by the user to broadcast data to the WSN, where the strength of the radio signal is evaluated at each WSN node to localize the user. A method is presented that uses simultaneous localization and mapping (SLAM) for system calibration, providing automated fingerprinting associating the radio signal strength patterns to the user's location within the living space. To improve the accuracy of localization, a novel refinement technique is introduced that takes into account typical movement patterns of people within their homes. Experimental results demonstrate that the system is capable of providing accurate localization results in a typical living space.

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment.

Inexpensive, high-throughput, low maintenance systems for precise temporal and spatial measurement of mouse home cage behavior (including movement, feeding, and drinking) are required to evaluate products from large scale pharmaceutical design and genetic lesion programs. These measurements are also required to interpret results from more focused behavioral assays. We describe the design and validation of a highly-scalable, reliable mouse home cage behavioral monitoring system modeled on a previously described, one-of-a-kind system. Mouse position was determined by solving static equilibrium equations describing the force and torques acting on the system strain gauges; feeding events were detected by a photobeam across the food hopper, and drinking events were detected by a capacitive lick sensor. Validation studies show excellent agreement between mouse position and drinking events measured by the system compared with video-based observation--a gold standard in neuroscience.