Year: 2022
Type: article
Abstract: Analyzing human mobility patterns is valuable for understanding human behavior and providing location-anticipating services. In this work, we theoretically estimate the predictability of human movement for indoor settings, a problem that has not yet been tackled by the community. To validate the model, we utilize location data collected by ambient sensors in residential settings. The data support the model and allow us to contrast the predictability of various groups, including single-resident homes, homes with multiple residents, and homes with pets.
Authors: Tinghui Wang, Diane J. Cook, Thomas R. Fischer
Institution: Washington State University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Awards: G1624904720, CPS: TTP Option: Synergy: Collaborative Research: The Science of Activity-Predictive Cyber-Physical Systems (APCPS), Multi-modal functional health assessment and intervention for individuals experiencing cognitive decline, Smart environment technology for longitudinal behavior analysis and intervention, Multidisciplinary undergraduate training program in Health-assistive Smart Environments, A clinician-in-the-loop smart environment to support management of chronic health conditions
OpenAlex ID: W4285257182