Table of interest
Dibyanshu Jaiswal, Andrew Gigie, Tapas Chakravarty, Avik Ghose, Archan Misra · 2018
Energy overheads continue to be a major impediment for wearable based activity recognition systems. We proposed a hybrid approach, which combines wearable-based human sensing with object interaction tracking, for robust detection of ADLs in smart homes. Our proposed framework includes: (a) battery less, low sampling rate, wearable RF sensor tags, that are powered intermittently by an RFID reader, and (b) additional passive RF tags, mounted on daily use objects, that capture the presence and use of specific objects while performing such ADLs. Using an initial experimental set up, we show the ability to recognize activities like eating, typing and reading, which are generally performed on a table, with an accuracy of 96%. Moreover, by capturing the item-level interactions of a user while performing ADLs, this approach can help observe the evolution of fine-grained behavioral changes and anomalies in an individual.