摘要
For elders with dementia, wandering is among the most problematic, frequent and dangerous behavior. Managing wandering behavior has become increasingly imperative due to its high prevalence, negative outcomes and burden on caregivers. We study to propose an active infrared-based method to identify wandering locomotion by monitoring rhythmical repetition of an elder’s indoor motion events. Specifically, we utilize our customized active infrared sensors to collect human indoor motions that will be converted into motion events by using hardware redundancy technique. Each motion event is a directed motion obtained via introducing temporal and dimensions into the spatial motion data. Based on the most cited spatial-temporal patterns of wandering locomotion, a spatiotemporal model is then proposed to identify wandering locomotion from an ongoing sequence of motion events. Experimental evaluation on eight individuals’ real-world motion datasets has shown that our proposed method is able to effectively identify wandering locomotion from repetitive events collected from active infrared sensors with a value over 98% for both accuracy and precision based on properly chosen parameters. Wandering in elders with dementia that follow specific spatiotemporal patterns can be reliably identified by analyzing repetitive motion events collected from active infrared sensors based on the well-known spatiotemporal patterns of wandering locomotion.
For elders with dementia, wandering is among the most problematic, frequent and dangerous behavior. Managing wandering behavior has become increasingly imperative due to its high prevalence, negative outcomes and burden on caregivers. We study to propose an active infrared-based method to identify wandering locomotion by monitoring rhythmical repetition of an elder’s indoor motion events. Specifically, we utilize our customized active infrared sensors to collect human indoor motions that will be converted into motion events by using hardware redundancy technique. Each motion event is a directed motion obtained via introducing temporal and dimensions into the spatial motion data. Based on the most cited spatial-temporal patterns of wandering locomotion, a spatiotemporal model is then proposed to identify wandering locomotion from an ongoing sequence of motion events. Experimental evaluation on eight individuals’ real-world motion datasets has shown that our proposed method is able to effectively identify wandering locomotion from repetitive events collected from active infrared sensors with a value over 98% for both accuracy and precision based on properly chosen parameters. Wandering in elders with dementia that follow specific spatiotemporal patterns can be reliably identified by analyzing repetitive motion events collected from active infrared sensors based on the well-known spatiotemporal patterns of wandering locomotion.
