Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2018

Abstract

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.

Keywords

Battery-Less Wearable, Activity Recognition, Passive RFID tags, Behaviour Analysis, Probabilistic Model

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

WearSys '18: Proceedings of the 4th ACM Workshop on Wearable Systems and Applications, Munich, Germany, June 10

First Page

39

Last Page

44

ISBN

9781450358422

Identifier

10.1145/3211960.3211976

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3211960.3211976

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