A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.
The findings indicate an overall positive attitude toward sensor technologies for nonobtrusive monitoring. Researchers and practitioners are called upon to address ethical and technical challenges in this emerging domain.
Background At present, the vast majority of older adults reside in the community. Though many older adults live in their own homes, increasing numbers are choosing continuing care retirement communities (CCRCs), which range from independent apartments to assisted living and skilled-nursing facilities. With predictions of a large increase in the segment of the population aged 65 and older, a subsequent increase in demand on CCRCs can be anticipated. With these expectations, researchers have begun exploring the use of smart home information-based technologies in these care facilities to enhance resident quality of life and safety, but little evaluation research exists on older adults' acceptance and use of these technologies. Objective This study investigated the factors that influence the willingness of older adults living in independent and assisted living CCRCs to adopt smart home technology. Subjects and setting Participants (n = 14) were recruited from community-dwelling older adults, aged 65 or older, living in one of two mid-western US CCRC facilities (independent living and assisted living type facilities).
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