GEM: Gear-based Environment-Integrated Mobility for Adaptive Indoor Human Sensing
This work proposes a novel hybrid sensing paradigm for indoor human monitoring, potentially reducing maintenance and improving adaptability, but remains at an early prototype stage.
GEM introduces mobility to infrastructure-based sensing by integrating a gear matrix into surfaces to move sensors, addressing inefficiencies of static deployments and burdens of mobile devices. A 3x3 prototype and simulations up to 64x64 validate the concept.
Infrastructure-based sensing systems, like Wi-Fi, thermal, vibration-based approaches, provide continuous and unobtrusive indoor human monitoring services. They are often deployed statically for long-term continuous monitoring, which often leads to inefficient sensing/inflexible deployment due to human mobility or high maintenance/data volume for dense deployments. In contrast, autonomous and human carried mobile devices can better adapt to human mobility. However, their physical presence (e.g., drones or robots) may induce observer effects, while their operation often imposes additional burdens, such as wearing (e.g., wearables) and frequent charging. We present GEM, a hybrid scheme that introduces the mobility to infrastructure-based sensing. GEM integrates a matrix of gears into everyday surfaces (e.g., floors, walls) to turn them into "public transportation" for moving infrastructure sensors around. We design and fabricate a 3 x 3 gear matrix prototype that can effectively move sensors from one location to another. We further validate the scalability of the design through simulation of up to 64 x 64 gear matrix with concurrent sensors.