research-article

Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

Authors:

Daniele Riboni,

Gabriele Civitarese,

Heiner StuckenschmidtAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 1 - 12

https://doi.org/10.1145/2971648.2971691

Published: 12 September 2016 Publication History

Abstract

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals' privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.

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Cited By

Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Yan SLin KWang H(2024)Toward Lightweight End-to-End Semantic Learning of Real-Time Human Activity Recognition for Enabling Ambient IntelligenceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338679436:11(7157-7170)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3386794
Kim HSon HLee D(2024)Causality-Aware Pattern Mining Scheme for Group Activity Recognition2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00126(908-917)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00126
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Index Terms

Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Theory of computation
  1. Logic

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cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2016

1288 pages

ISBN:9781450344616

DOI:10.1145/2971648

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 ACM.

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Published: 12 September 2016

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UbiComp '16

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Yan SLin KWang H(2024)Toward Lightweight End-to-End Semantic Learning of Real-Time Human Activity Recognition for Enabling Ambient IntelligenceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338679436:11(7157-7170)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3386794
Kim HSon HLee D(2024)Causality-Aware Pattern Mining Scheme for Group Activity Recognition2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00126(908-917)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00126
Boovaraghavan SPatidar PAgarwal Y(2023)TAOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108967:3(1-32)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610896
Lee BMohan PChaspari TRyan Ahn C(2023)Assessing Daily Activity Routines Using an Unsupervised Approach in a Smart Home EnvironmentJournal of Computing in Civil Engineering10.1061/JCCEE5.CPENG-489537:1Online publication date: Jan-2023
https://doi.org/10.1061/JCCEE5.CPENG-4895
Buoncompagni LKareem SMastrogiovanni F(2022)Human Activity Recognition Models in Ontology NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2021.307353952:6(5587-5606)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2021.3073539
Kim HDongman (2022)AR-T: Temporal Relation Embedded Transformer for the Real World Activity RecognitionMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_40(617-633)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_40
Niemann FLüdtke SBartelt Cten Hompel M(2021)Context-Aware Human Activity Recognition in Industrial ProcessesSensors10.3390/s2201013422:1(134)Online publication date: 25-Dec-2021
https://doi.org/10.3390/s22010134
Stavropoulos TMeditskos GLazarou IMpaltadoros LPapagiannopoulos STsolaki MKompatsiaris I(2021)Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple SclerosisSensors10.3390/s2118623021:18(6230)Online publication date: 17-Sep-2021
https://doi.org/10.3390/s21186230
Michalakis KChristodoulou YCaridakis GVoutos YMylonas P(2021)A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural SpacesApplied Sciences10.3390/app1113577011:13(5770)Online publication date: 22-Jun-2021
https://doi.org/10.3390/app11135770
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