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Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch

Authors:

Shruthi K. Hiremath,

Yasutaka Nishimura,

Sonia Chernova,

Thomas PlötzAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 3

Article No.: 119, Pages 1 - 27

https://doi.org/10.1145/3550294

Published: 07 September 2022 Publication History

Abstract

Smart Homes have come a long way: From research laboratories in the early days, through (almost) neglect, to their recent revival in real-world environments enabled through the existence of commodity devices and robust, standardized software frameworks. With such availability, human activity recognition (HAR) in smart homes has become attractive for many real-world applications, especially in the domain of Ambient Assisted Living. Yet, getting started with an activity recognition system in specific smart homes, which are highly specialized spaces inhabited by individuals with idiosyncratic behaviors and habits, is a non-trivial endeavor. We present an approach for bootstrapping HAR systems for individual smart homes from scratch. At the beginning of the life cycle of a smart home, our system passively observes activities and derives rich representations for sensor data-action units-which are then aggregated into activity models through motif learning with minimal supervision. The resulting HAR system is then capable of recognizing relevant, most frequent activities in a smart home. We demonstrate the effectiveness of our bootstrapping procedure through experimental evaluations on CASAS datasets that show the practical value of our approach.

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Index Terms

Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile 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 6, Issue 3

September 2022

1612 pages

EISSN:2474-9567

DOI:10.1145/3563014

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Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 07 September 2022

Published in IMWUT Volume 6, Issue 3

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Cited By

Wang CFeng YZhong LZhu SZhang CZheng SLiang CWang YHe CYu CShi Y(2024)UbiPhysioProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435528:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643552
Xia KLi WGan SLu S(2024)TS2ACTProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314457:4(1-22)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631445
Dawson JPhanich KWiese J(2024)Reenvisioning Patient Education with Smart Hospital Patient RoomsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314197:4(1-23)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631419
Arrotta LCivitarese GBettini C(2024)Semantic LossProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314077:4(1-29)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631407
Wang JCui WZhu TNing HLiu Z(2024)An Improved Masking Strategy for Self- Supervised Masked Reconstruction in Human Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2024.339075524:11(18699-18709)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JSEN.2024.3390755
Takeda NLegaspi RNishimura YIkeda KPlötz TChernova S(2024)A Synergistic Large Language Model and Supervised Learning Approach to Zero-Shot and Continual Activity Recognition in Smart Homes2024 9th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA61153.2024.10607364(113-122)Online publication date: 16-Mar-2024
https://doi.org/10.1109/ICBDA61153.2024.10607364
Arrotta LCivitarese GBettini C(2024)SelfAct: Personalized Activity Recognition Based on Self-Supervised and Active LearningMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63989-0_19(375-391)Online publication date: 19-Jul-2024
https://doi.org/10.1007/978-3-031-63989-0_19
Konak Ovan de Water RDöring VFiedler TLiebe LMasopust LPostnov KSauerwald FTreykorn FWischmann AGjoreski HLuštrek MArnrich B(2023)HARE: Unifying the Human Activity Recognition Engineering WorkflowSensors10.3390/s2323957123:23(9571)Online publication date: 2-Dec-2023
https://doi.org/10.3390/s23239571
Hiremath SPlötz T(2023)The Lifespan of Human Activity Recognition Systems for Smart HomesSensors10.3390/s2318772923:18(7729)Online publication date: 7-Sep-2023
https://doi.org/10.3390/s23187729
Diraco GRescio GSiciliano PLeone A(2023)Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained ProcessingSensors10.3390/s2311528123:11(5281)Online publication date: 2-Jun-2023
https://doi.org/10.3390/s23115281

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