AtomGID: An Atomic Gesture Identifier for Qualitative Spatial Reasoning

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.