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In this paper, we propose a tree based regression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. By function approximation based on multivariable polynomial regression and passing only the coefficients returned by the regression function instead of aggregated data, TREG achieves the following goals: (1) the(More)
Keywords: Scene understanding Activity analysis Fuzzy logic Activities of daily living Eldercare Depth images Image features a b s t r a c t We describe a novel technique to combine motion data with scene information to capture activity characteristics of older adults using a single Microsoft Kinect depth sensor. Specifically, we describe a method to learn(More)
A two-stage fall detection technique developed by our team was tested in a real hospital setting with falls acted out in a patient room. To further test the algorithm, data were collected at the University of Missouri hospital with actual patients. Features extracted from three dimensional point clouds created from Kinect depth images were used as input to(More)
In this paper, we present results of an automatic vision-based gait assessment tool, using two cameras. Elderly residents from TigerPlace, a retirement community, were recruited to participate in the validation and test of the system in scripted scenarios representing everyday activities. The residents were first tested on a GAITRite mat, an electronic(More)
As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as(More)
We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a(More)
The purpose of this study was to test the implementation of a fall detection and "rewind" privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with(More)
The latest acoustic fall detection system (acoustic FADE) has achieved encouraging results on real-world dataset. However, the acoustic FADE device is difficult to be deployed in real environment due to its large size. In addition, the estimation accuracy of sound source localization (SSL) and direction of arrival (DOA) becomes much lower in(More)
—We present an approach for activity state recognition implemented on data collected from various sensors—standard web cameras under normal illumination, web cameras using in-frared lighting, and the inexpensive Microsoft Kinect camera system. Sensors such as the Kinect ensure that activity segmentation is possible during the daytime as well as night. This(More)