Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2971648.2971691acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

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

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.

References

[1]
Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, and Peter F. Patel-Schneider. 2010. The Description Logic Handbook: Theory, Implementation and Applications (2nd ed.). Cambridge University Press, New York, NY, USA.
[2]
Ling Bao and Stephen S. Intille. 2004. Activity Recognition from User-Annotated Acceleration Data. In Pervasive Computing: Second International Conference, PERVASIVE 2004, Linz/Vienna, Austria, April 21-23, 2004. Proceedings. Springer, Berlin, Heidelberg, 1--17.
[3]
Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Computing Surveys (CSUR) 46, 3 (2014), 33:1--33:33.
[4]
Alberto Calatroni, Daniel Roggen, and Gerhard Tröster. 2011. Collection and curation of a large reference dataset for activity recognition. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on. IEEE, Anchorage, Alaska, USA, 30--35.
[5]
Melisachew Chekol, Jakob Huber, Christian Meilicke, and Heiner Stuckenschmidt. 2016. Markov Logic Networks with Numerical Constraints. In 22st European Conference on Artificial Intelligence (ECAI2016). IOS Press, Amsterdam, The Netherlands, 1--9.
[6]
Liming Chen and Chris Nugent. 2009. Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems 5, 4 (2009), 410--430.
[7]
Diane J. Cook, Aaron S. Crandall, Brian L. Thomas, and Narayanan C. Krishnan. 2013a. CASAS: A Smart Home in a Box. Computer 46, 7 (2013), 62--69.
[8]
Diane J. Cook, Kyle D. Feuz, and Narayanan Chatapuram Krishnan. 2013b. Transfer learning for activity recognition: A survey. Knowledge and Information Systems 36, 3 (2013), 537--556.
[9]
Nigel Davies, Daniel P. Siewiorek, and Rahul Sukthankar. 2008. Activity-Based Computing. IEEE Pervasive Computing 7, 2 (2008), 20--21.
[10]
Prafulla Dawadi, Diane J. Cook, and Maureen Schmitter-Edgecombe. 2013. Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43, 6 (2013), 1302--1313.
[11]
Christopher W. Geib and Robert P. Goldman. 2009. A probabilistic plan recognition algorithm based on plan tree grammars. Artificial Intelligence 173, 11 (2009), 1101--1132.
[12]
Bernardo Cuenca Grau, Ian Horrocks, Boris Motik, Bijan Parsia, Peter F. Patel-Schneider, and Ulrike Sattler. 2008. OWL 2: The next step for OWL. Journal of Web Semantics 6, 4 (2008), 309--322.
[13]
Tao Gu, Zhanqing Wu, XianPing Tao, Hung Keng Pung, and Jian Lu. 2009. epSICAR: An Emerging Patterns based Approach to Sequential, Interleaved and Concurrent Activity Recognition. In Proceedings of the Seventh Annual IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, Washington, D.C., 1--9.
[14]
Rim Helaoui, Daniele Riboni, and Heiner Stuckenschmidt. 2013. A Probabilistic Ontological Framework for the Recognition of Multilevel Human Activities. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, New York, NY, USA, 345--354.
[15]
Xin Hong, Chris D. Nugent, Maurice D. Mulvenna, Suzanne Martin, Steven Devlin, and Jonathan G. Wallace. 2012. Dynamic similarity-based activity detection and recognition within smart homes. International Journal of Pervasive Computing and Communications 8, 3 (2012), 264--278.
[16]
Jakob Huber, Christian Meilicke, and Heiner Stuckenschmidt. 2014. Applying markov logic for debugging probabilistic temporal knowledge bases. In AKBC 2014: 4th Workshop on Automated Knowledge Base Construction AKBC 2014 at NIPS 2014 in Montreal, Canada, December 13, 2014. ACM, New York, NY, USA, 1--6.
[17]
Jonathan Lester, Tanzeem Choudhury, Nicky Kern, Gaetano Borriello, and Blake Hannaford. 2005. A Hybrid Discriminative/Generative Approach for Modeling Human Activities. In Proceedings of the 19th international joint conference on Artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 766--772.
[18]
Seng W. Loke. 2004. Representing and reasoning with situations for context-aware pervasive computing: A logic programming perspective. The Knowledge Engineering Review 19, 3 (2004), 213--233.
[19]
Paul Lukowicz, Jamie A. Ward, Holger Junker, Mathias Stäger, Gerhard Tröster, Amin Atrash, and Thad Starner. 2004. Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. In Pervasive Computing: Second International Conference, PERVASIVE 2004, Linz/Vienna, Austria, April 21-23, 2004. Proceedings. Springer, Berlin, Heidelberg, 18--32.
[20]
Georgios Meditskos, Efstratios Kontopoulos, and Ioannis Kompatsiaris. 2014. Knowledge-Driven Activity Recognition and Segmentation Using Context Connections. In The Semantic Web -- ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II. Springer, Cham, 260--275.
[21]
Georgios Meditskos, Efstratios Kontopoulos, and Ioannis Kompatsiaris. 2015. ReDef: Context-aware Recognition of Interleaved Activities using OWL 2 and Defeasible Reasoning. In Joint Proceedings of SSN-TC and OrdRing 2015 (CEUR Workshop Proceedings), Vol. 1488. CEUR-WS.org, Bethlehem, Pennsylvania, United States, 31--42.
[22]
George Okeyo, Liming Chen, Hui Wang, and Roy Sterritt. 2014. Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing 10, Part B (2014), 155--172.
[23]
Paulito Palmes, Hung Keng Pung, Tao Gu, Wenwei Xue, and Shaxun Chen. 2010. Object relevance weight pattern mining for activity recognition and segmentation. Pervasive and Mobile Computing 6, 1 (2010), 43 -- 57.
[24]
Carolyn Parsey and Maureen Schmitter-Edgecombe. 2013. Applications of technology in neuropsychological assessment. The Clinical Neuropsychologist 27, 8 (2013), 1328--1361.
[25]
Attila Reiss and Didier Stricker. 2012. Creating and benchmarking a new dataset for physical activity monitoring. In Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments. ACM, New York, NY, USA, 40:1--40:8.
[26]
Daniele Riboni and Claudio Bettini. 2011a. COSAR: Hybrid reasoning for context-aware activity recognition. Personal and Ubiquitous Computing 15, 3 (2011), 271--289.
[27]
Daniele Riboni and Claudio Bettini. 2011b. OWL 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7, 3 (2011), 379--395.
[28]
Daniele Riboni, Claudio Bettini, Gabriele Civitarese, Zaffar Haider Janjua, and Viola Bulgari. 2015. From Lab to Life: Fine-grained Behavior Monitoring in the Elderly's Home. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference on. IEEE Computer Society, Washington, D.C., 342--347.
[29]
Daniele Riboni, Claudio Bettini, Gabriele Civitarese, Zaffar Haider Janjua, and Rim Helaoui. 2016. SmartFABER: Recognizing Fine-grained Abnormal Behaviors for Early Detection of Mild Cognitive Impairment. Artificial Intelligence in Medicine 67 (2016), 57--74.
[30]
Matthew Richardson and Pedro Domingos. 2006. Markov logic networks. Machine learning 62, 1 (2006), 107--136.
[31]
Geetika Singla, Diane J Cook, and Maureen Schmitter-Edgecombe. 2009. Tracking activities in complex settings using smart environment technologies. International journal of biosciences, psychiatry, and technology (IJBSPT) 1, 1 (2009), 25--35.
[32]
Timo Sztyler and Heiner Stuckenschmidt. 2016. On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition. In 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, Washington, D.C., 1--9.
[33]
X. H. Wang, T. Gu, D. Q. Zhang, and H. K. Pung. 2004. Ontology Based Context Modeling and Reasoning using OWL. In Proceedings of Second IEEE Annual Conference on Pervasive Computing and Communications Workshops. IEEE Computer Society, Washington, D.C., 18--22.
[34]
Danny Wyatt, Matthai Philipose, and Tanzeem Choudhury. 2005. Unsupervised Activity Recognition Using Automatically Mined Common Sense. In Proceedings of the 20th National Conference on Artificial Intelligence, Vol. 1. AAAI Press, California, USA, 21--27.
[35]
Juan Ye, Simon Dobson, and Susan McKeever. 2012. Situation identification techniques in pervasive computing: A review. Pervasive and Mobile Computing 8, 1 (2012), 36--66.
[36]
Juan Ye and Graeme Stevenson. 2013. Semantics-Driven Multi-user Concurrent Activity Recognition. Springer International Publishing, Cham, 204--219.
[37]
Juan Ye, Graeme Stevenson, and Simon Dobson. 2014. USMART: An Unsupervised Semantic Mining Activity Recognition Technique. ACM Transactions on Interactive Intelligent Systems (TiiS) 4, 4 (2014), 16:1--16:27.

Cited By

View all
  • (2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
  • (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
  • (2023)TAOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108967:3(1-32)Online publication date: 27-Sep-2023
  • Show More Cited By

Index Terms

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

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      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
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 September 2016

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. activity recognition
      2. ontological reasoning
      3. probabilistic reasoning

      Qualifiers

      • Research-article

      Conference

      UbiComp '16

      Acceptance Rates

      UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;
      Overall Acceptance Rate 764 of 2,912 submissions, 26%

      Upcoming Conference

      UbiComp '24

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)21
      • Downloads (Last 6 weeks)4
      Reflects downloads up to 23 Sep 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
      • (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
      • (2023)TAOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108967:3(1-32)Online publication date: 27-Sep-2023
      • (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
      • (2022)Human Activity Recognition Models in Ontology NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2021.307353952:6(5587-5606)Online publication date: Jun-2022
      • (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
      • (2021)Context-Aware Human Activity Recognition in Industrial ProcessesSensors10.3390/s2201013422:1(134)Online publication date: 25-Dec-2021
      • (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
      • (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
      • (2021)Precise Correlation Extraction for IoT Fault Detection With Concurrent ActivitiesACM Transactions on Embedded Computing Systems10.1145/347702520:5s(1-21)Online publication date: 22-Sep-2021
      • Show More Cited By

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media