GEM: Gear-based Environment-Integrated Mobility for Adaptive Indoor Human Sensing

TL;DR

GEM introduces a gear-based system embedded in surfaces to enable mobile, adaptive indoor human sensing, combining infrastructure and moving sensors for efficient monitoring.

Contribution

This work presents a novel gear matrix design that transforms surfaces into mobile platforms for sensors, enhancing flexibility and scalability in indoor sensing.

Findings

Designed and fabricated a 3x3 gear matrix prototype for sensor mobility.

Validated scalability through simulation of up to 64x64 gear matrix with multiple sensors.

Demonstrated effective movement of sensors across surfaces for adaptive indoor sensing.

Abstract

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.