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TAO: Context Detection from Daily Activity Patterns Using Temporal Analysis and Ontology

Authors:

Sudershan Boovaraghavan,

Prasoon Patidar,

Yuvraj AgarwalAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 3

Article No.: 87, Pages 1 - 32

https://doi.org/10.1145/3610896

Published: 27 September 2023 Publication History

Abstract

Translating fine-grained activity detection (e.g., phone ring, talking interspersed with silence and walking) into semantically meaningful and richer contextual information (e.g., on a phone call for 20 minutes while exercising) is essential towards enabling a range of healthcare and human-computer interaction applications. Prior work has proposed building ontologies or temporal analysis of activity patterns with limited success in capturing complex real-world context patterns. We present TAO, a hybrid system that leverages OWL-based ontologies and temporal clustering approaches to detect high-level contexts from human activities. TAO can characterize sequential activities that happen one after the other and activities that are interleaved or occur in parallel to detect a richer set of contexts more accurately than prior work. We evaluate TAO on real-world activity datasets (Casas and Extrasensory) and show that our system achieves, on average, 87% and 80% accuracy for context detection, respectively. We deploy and evaluate TAO in a real-world setting with eight participants using our system for three hours each, demonstrating TAO's ability to capture semantically meaningful contexts in the real world. Finally, to showcase the usefulness of contexts, we prototype wellness applications that assess productivity and stress and show that the wellness metrics calculated using contexts provided by TAO are much closer to the ground truth (on average within 1.1%), as compared to the baseline approach (on average within 30%).

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Cited By

Das Swain VGao LMondal AAbowd GDe Choudhury M(2024)Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information WorkersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642716(1-30)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642716

Index Terms

TAO: Context Detection from Daily Activity Patterns Using Temporal Analysis and Ontology
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
    2. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Ambient intelligence
      2. Ubiquitous computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 3

September 2023

1734 pages

EISSN:2474-9567

DOI:10.1145/3626192

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Published: 27 September 2023

Published in IMWUT Volume 7, Issue 3

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Das Swain VGao LMondal AAbowd GDe Choudhury M(2024)Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information WorkersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642716(1-30)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642716

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