Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor


Covered by SCOPUS


CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2018: 120.52

Open Access
Online
ISSN
2081-4836
See all formats and pricing
More options …

Environment aware ADL recognition system based on decision tree and activity frame

Nicholas Melo / Jaeryoung Lee
  • Corresponding author
  • Department of Robotic Science and Technology, College of Engineering, Chubu University, Kasugai, Japan
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-07-18 | DOI: https://doi.org/10.1515/pjbr-2018-0011

Abstract

The interest towards robots for elderly care has been growing in the last years. Systems aiming to integrate robot interactive components and the user’s activity recognition system are increasing as well. This work presents an activity aware intelligent system that supports user in his/her daily life tasks. The proposed system aims to integrate three important aspects into a smart house application (environment monitoring, user activity recognition and user friendly interaction). The information gathered from sensors across the environment is structured as the state of the environment in a compacted form called activity frame. This specific frame is used by a predictor (based on the decision tree method), in order to recognize the activities that have been performed by the user inside his/her domestic environment. The recognized activity is used by an user-interactive component, which uses the predicted behavior as a guideline for its interaction planner. The presented activity recognition system was tested with the data provided by different smart home projects, and the recognition rate for the proposed predictor has high recognition rate compared to other similar ones. The architecture described by the sensory network allows the system to be easily implemented in real time in a smart house context.

Keywords: activity recognition; ADL; smart house; activity frame; decision tree; elderly care

References

  • [1] H. M. Van der Loos, D. J. Reinkensmeyer, E. Guglielmelli, Rehabilitation and Health Care Cobotics, In: Springer Handbook of Robotics, Springer, 2016, 1685-1728Google Scholar

  • [2] B. Bruno, N. Y. Chong, H. Kamide, S. Kanoria, J. Lee, Y. Lim, et al., The caresses eu-japan project: making assistive robots culturally competent, arXiv preprint arXiv:1708.06276Google Scholar

  • [3] D. Monekosso, F. Florez-Revuelta, P. Remagnino, Ambient assisted living, IEEE Intelligent Systems, 2015, 30(4), 2-6Web of ScienceGoogle Scholar

  • [4] A. GhaffarianHoseini, N. D. Dahlan, U. Berardi, A. GhaffarianHoseini, N. Makaremi, The essence of future smart houses: From embedding ICT to adapting to sustainability principles, Renewable and Sustainable Energy Reviews, 2013, 24, 593-607Google Scholar

  • [5] B. Hamed, Design & implementation of smart house control using labview, International Journal of Soft Computing and Engineering (IJSCE), 2012, 1(6), 98-106Google Scholar

  • [6] D. J. Cook, S. K. Das, How smart are our environments? an updated look at the state of the art, Pervasive and Mobile Computing, 2007, 3(2), 53-73Google Scholar

  • [7] A. Perišić, M. Lazić, B. Perišić, R. Obradović, A smart house environment - the system of systems approach to model driven simulation of building (house) attributes, In: 2015 IEEE 1st International Workshop on Consumer Electronics (CE WS), 2015, 56-59Google Scholar

  • [8] G. Demiris, B. K. Hensel, et al., Technologies for an aging society: a systematic review of “smart home” applications, Yearbook of Medical Informatics, 2008, 3, 33-40Google Scholar

  • [9] D. H. Stefanov, Z. Bien, W.-C. Bang, The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2004, 12(2), 228-250CrossrefGoogle Scholar

  • [10] P. N. Dawadi, D. J. Cook, M. Schmitter-Edgecombe, Automated cognitive health assessment using smart home monitoring of complex tasks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43(6), 1302-1313Google Scholar

  • [11] D. H. Stefanov, Z. Bien, W.-C. Bang, The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2004, 12(2), 228-250CrossrefGoogle Scholar

  • [12] D. Cook, K. D. Feuz, N. C. Krishnan, Transfer learning for activity recognition: A survey, Knowledge and Information Systems, 2013, 36(3), 537-556Google Scholar

  • [13] J. Wang, Y. Chen, S. Hao, X. Peng, L. Hu, Deep learning for sensor-based activity recognition: A survey, arXiv preprint arXiv:1707.03502Google Scholar

  • [14] R. Bodor, B. Jackson, N. Papanikolopoulos, Vision-based human tracking and activity recognition, In: Proc. of the 11th Mediterranean Conference on Control and Automation, Citeseer, 2003, 1, 1-6Google Scholar

  • [15] A. Jalal, S. Kamal, D. Kim, A depth video-based human detection and activity recognition using multi-features and embedded hidden Markov models for health care monitoring systems, International Journal of Interactive Multimedia & Artificial Intelligence, 2017, 4(4), 54-62CrossrefGoogle Scholar

  • [16] M. Yu, A. Rhuma, S. M. Naqvi, L. Wang, J. Chambers, A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment, IEEE Transactions on Information Technology in Biomedicine, 2012, 16(6), 1274-1286CrossrefWeb of ScienceGoogle Scholar

  • [17] J. Yang, Toward physical activity diary: motion recognition using simple acceleration features with mobile phones, In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, ACM, 2009, 1-10Google Scholar

  • [18] Y.-S. Lee, S.-B. Cho, Activity recognition using hierarchical hidden Markov models on a smartphone with 3d accelerometer, In: International Conference on Hybrid Artificial Intelligence Systems, Springer, 2011, 460-467Google Scholar

  • [19] L. Bao, S. Intille, Activity recognition from user-annotated acceleration data, Pervasive Computing, 2004, 3001, 1-17Google Scholar

  • [20] Z. He, L. Jin, Activity recognition from acceleration data based on discrete consine transform and svm, In: IEEE International Conference on Systems, Man and Cybernetics (SMC 2009), 2009, 5041-5044Google Scholar

  • [21] G. Demiris, M. J. Rantz, M. A. Aud, K. D. Marek, H. W. Tyrer, M. Skubic, A. A. Hussam, Older adults’ attitudes towards and perceptions of ‘smart home’technologies: a pilot study, Medical Informatics and the Internet in Medicine, 2004, 29(2), 87-94CrossrefGoogle Scholar

  • [22] R. Fensli, P. Pedersen, T. Gundersen, O. Hejlesen, Sensor acceptance model - measuring patient acceptance of wearable sensors, Methods Inf Med, 2008, 47, 89-95Web of ScienceGoogle Scholar

  • [23] D. Sprute, M. König, On-chip activity recognition in a smart home, In: In: 12th International Conference on Intelligent Environments(IE), 2016, 95-102Google Scholar

  • [24] K.-H. Park, Z. Bien, J.-J. Lee, B. K. Kim, J.-T. Lim, J.-O. Kim, et al., Robotic smart house to assist people with movement disabilities, Autonomous Robots, 2007, 22(2), 183-198CrossrefWeb of ScienceGoogle Scholar

  • [25] L. Fan, Z. Wang, H. Wang, Human activity recognition model based on decision tree, In: 2013 International Conference on Advanced Cloud and Big Data (CBD), 2013, 64-68Google Scholar

  • [26] M. Prossegger, A. Bouchachia, Multi-resident activity recognition using incremental decision trees, In: Adaptive and Intelligent Systems, Springer, 2014, 182-191Google Scholar

  • [27] S. Oh,W. Woo, et al., CAMAR: Context-aware mobile augmented reality in smart space, In: Proceedings of the 3rd International Workshop on Ubiquitous Virtual Reality (IWUVR), 2009, 9, 48-51Google Scholar

  • [28] M. Zhou, Z.-l. Nie, Analysis and design of zigbee mac layers protocol, In: 2010 International Conference on Future Information Technology and Management Engineering (FITME), 2010, 2, 211-215Google Scholar

  • [29] E. M. Tapia, S. S. Intille, K. Larson, Activity recognition in the home using simple and ubiquitous sensors, In: International Conference on Pervasive Computing, Springer, 2004, 158-175CrossrefGoogle Scholar

  • [30] D. Riboni, T. Sztyler, G. Civitarese, H. Stuckenschmidt, Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning, In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2016, 1-12Google Scholar

  • [31] N. C. Krishnan, D. J. Cook, Activity recognition on streaming sensor data, Pervasive and Mobile Computing, 2014, 10, 138-154Google Scholar

  • [32] J. Pärkkä, L. Cluitmans, M. Ermes, Personalization algorithm for real-time activity recognition using pda,wireless motion bands, and binary decision tree, IEEE Transactions on Information Technology in Biomedicine, 2010, 14(5), 1211-1215CrossrefWeb of ScienceGoogle Scholar

  • [33] N. C. Krishnan, S. Panchanathan, Analysis of low resolution accelerometer data for continuous human activity recognition, In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), IEEE, 2008, 3337-3340Google Scholar

  • [34] L.Wang, T. Gu, X. Tao, J. Lu, A hierarchical approach to real-time activity recognition in body sensor networks, Pervasive and Mobile Computing, 2012, 8(1), 115-130Google Scholar

  • [35] T. Hastie, R. Tibshirani, J. Friedman, Overview of supervised learning, In: The elements of Statistical Learning, Springer, 2009, 9-41Google Scholar

  • [36] C. Strobl, J. Malley, G. Tutz, An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests, Psychological Methods, 2009, 14(4), 323-348Web of ScienceCrossrefGoogle Scholar

  • [37] D. G. Denison, B. K. Mallick, A. F. Smith, A bayesian cart algorithm, Biometrika, 1998, 85(2), 363-377Google Scholar

  • [38] J. R. Quinlan, C4. 5: Programs for Machine Learning, Elsevier, 2014Google Scholar

  • [39] M. A. Razi, K. Athappilly, A comparative predictive analysis of neural networks (NNS), nonlinear regression and classification and regression tree (CART) models, Expert Systems with Applications, 2005, 29(1), 65-74CrossrefGoogle Scholar

  • [40] L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, et al., Api design for machine learning software: experiences from the scikit-learn project, arXiv preprint arXiv:1309.0238Google Scholar

  • [41] B.-C. Cheng, Y.-A. Tsai, G.-T. Liao, E.-S. Byeon, Hmm machine learning and inference for activities of daily living recognition, The Journal of Supercomputing, 2010, 54(1), 29-42Google Scholar

  • [42] D. J. Cook, M. Schmitter-Edgecombe, Assessing the quality of activities in a smart environment, Methods of Information in Medicine, 2009, 48(5), 480-485Web of ScienceCrossrefGoogle Scholar

  • [43] H. Alemdar, H. Ertan, O. D. Incel, C. Ersoy, Aras human activity datasets in multiple homes with multiple residents, In: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013, 232-235Google Scholar

  • [44] G. Singla, D. J. Cook, M. Schmitter-Edgecombe, Tracking activities in complex settings using smart environment technologies, International Journal of Biosciences, Psychiatry, and Technology (IJBSPT), 2009, 1(1), 25-35Google Scholar

  • [45] S. T. M. Bourobou, Y. Yoo, User activity recognition in smart homes using pattern clustering applied to temporal ann algorithm, Sensors, 2015, 15(5), 11953-11971Web of ScienceGoogle Scholar

About the article

Received: 2017-12-15

Accepted: 2018-05-29

Published Online: 2018-07-18


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 9, Issue 1, Pages 155–167, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2018-0011.

Export Citation

© 2018 Jaeryoung Lee and Nicholas Melo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in