| Summary: | "Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights.
The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset.
The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users. === 2017-02-17T00:00:00Z
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