{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:18:13Z","timestamp":1759483093913},"reference-count":45,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The interest towards robots for elderly care has been growing in the last years. Systems aiming to integrate robot interactive components and the user\u2019s 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.<\/jats:p>","DOI":"10.1515\/pjbr-2018-0011","type":"journal-article","created":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T22:57:10Z","timestamp":1531954630000},"page":"155-167","source":"Crossref","is-referenced-by-count":5,"title":["Environment aware ADL recognition system based on decision tree and activity frame"],"prefix":"10.1515","volume":"9","author":[{"given":"Nicholas","family":"Melo","sequence":"first","affiliation":[{"name":"College of Engineering, Chubu University , Kasugai , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaeryoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Robotic Science and Technology, College of Engineering, Chubu University , Kasugai , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2018,7,18]]},"reference":[{"key":"2022042712092642069_j_pjbr-2018-0011_ref_001_w2aab3b7c11b1b6b1ab1ab1Aa","unstructured":"[1] H. M. Van der Loos, D. J. Reinkensmeyer, E. Guglielmelli, Rehabilitation and Health Care Cobotics, In: Springer Handbook of Robotics, Springer, 2016, 1685-172810.1007\/978-3-319-32552-1_64"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_002_w2aab3b7c11b1b6b1ab1ab2Aa","unstructured":"[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:1708.06276"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_003_w2aab3b7c11b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] D. Monekosso, F. Florez-Revuelta, P. Remagnino, Ambient assisted living, IEEE Intelligent Systems, 2015, 30(4), 2-610.1109\/MIS.2015.63","DOI":"10.1109\/MIS.2015.63"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_004_w2aab3b7c11b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[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, 593-60710.1016\/j.rser.2013.02.032","DOI":"10.1016\/j.rser.2013.02.032"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_005_w2aab3b7c11b1b6b1ab1ab5Aa","unstructured":"[5] B. Hamed, Design & implementation of smart house control using labview, International Journal of Soft Computing and Engineering (IJSCE), 2012, 1(6), 98-106"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_006_w2aab3b7c11b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[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), 53-7310.1016\/j.pmcj.2006.12.001","DOI":"10.1016\/j.pmcj.2006.12.001"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_007_w2aab3b7c11b1b6b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] A. Peri\u0161i\u0107, M. Lazi\u0107, B. Peri\u0161i\u0107, R. Obradovi\u0107, 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, 56-5910.1109\/CEWS.2015.7867155","DOI":"10.1109\/CEWS.2015.7867155"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_008_w2aab3b7c11b1b6b1ab1ab8Aa","unstructured":"[8] G. Demiris, B. K. Hensel, et al., Technologies for an aging society: a systematic review of \u201csmart home\u201d applications, Yearbook of Medical Informatics, 2008, 3, 33-4010.1055\/s-0038-1638580"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_009_w2aab3b7c11b1b6b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[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), 228-25010.1109\/TNSRE.2004.82842315218937","DOI":"10.1109\/TNSRE.2004.828423"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_010_w2aab3b7c11b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[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), 1302-131310.1109\/TSMC.2013.2252338426929225530925","DOI":"10.1109\/TSMC.2013.2252338"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_011_w2aab3b7c11b1b6b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[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), 228-25010.1109\/TNSRE.2004.828423","DOI":"10.1109\/TNSRE.2004.828423"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_012_w2aab3b7c11b1b6b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] D. Cook, K. D. Feuz, N. C. Krishnan, Transfer learning for activity recognition: A survey, Knowledge and Information Systems, 2013, 36(3), 537-55610.1007\/s10115-013-0665-3376802724039326","DOI":"10.1007\/s10115-013-0665-3"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_013_w2aab3b7c11b1b6b1ab1ac13Aa","unstructured":"[13] J. Wang, Y. Chen, S. Hao, X. Peng, L. Hu, Deep learning for sensor-based activity recognition: A survey, arXiv preprint arXiv:1707.03502"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_014_w2aab3b7c11b1b6b1ab1ac14Aa","unstructured":"[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, 1-6"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_015_w2aab3b7c11b1b6b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[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), 54-6210.9781\/ijimai.2017.447","DOI":"10.9781\/ijimai.2017.447"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_016_w2aab3b7c11b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[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), 1274-128610.1109\/TITB.2012.221478622922730","DOI":"10.1109\/TITB.2012.2214786"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_017_w2aab3b7c11b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[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, 1-1010.1145\/1631040.1631042","DOI":"10.1145\/1631040.1631042"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_018_w2aab3b7c11b1b6b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[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, 460-46710.1007\/978-3-642-21219-2_58","DOI":"10.1007\/978-3-642-21219-2_58"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_019_w2aab3b7c11b1b6b1ab1ac19Aa","unstructured":"[19] L. Bao, S. Intille, Activity recognition from user-annotated acceleration data, Pervasive Computing, 2004, 3001, 1-1710.1007\/978-3-540-24646-6_1"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_020_w2aab3b7c11b1b6b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[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, 5041-504410.1109\/ICSMC.2009.5346042","DOI":"10.1109\/ICSMC.2009.5346042"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_021_w2aab3b7c11b1b6b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] G. Demiris, M. J. Rantz, M. A. Aud, K. D. Marek, H. W. Tyrer, M. Skubic, A. A. Hussam, Older adults\u2019 attitudes towards and perceptions of \u2018smart home\u2019technologies: a pilot study, Medical Informatics and the Internet in Medicine, 2004, 29(2), 87-9410.1080\/1463923041000168438715370989","DOI":"10.1080\/14639230410001684387"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_022_w2aab3b7c11b1b6b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] R. Fensli, P. Pedersen, T. Gundersen, O. Hejlesen, Sensor acceptance model - measuring patient acceptance of wearable sensors, Methods Inf Med, 2008, 47, 89-9510.3414\/ME9106","DOI":"10.3414\/ME9106"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_023_w2aab3b7c11b1b6b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] D. Sprute, M. K\u00f6nig, On-chip activity recognition in a smart home, In: In: 12th International Conference on Intelligent Environments(IE), 2016, 95-10210.1109\/IE.2016.23","DOI":"10.1109\/IE.2016.23"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_024_w2aab3b7c11b1b6b1ab1ac24Aa","doi-asserted-by":"crossref","unstructured":"[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), 183-19810.1007\/s10514-006-9012-9","DOI":"10.1007\/s10514-006-9012-9"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_025_w2aab3b7c11b1b6b1ab1ac25Aa","doi-asserted-by":"crossref","unstructured":"[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, 64-6810.1109\/CBD.2013.19","DOI":"10.1109\/CBD.2013.19"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_026_w2aab3b7c11b1b6b1ab1ac26Aa","unstructured":"[26] M. Prossegger, A. Bouchachia, Multi-resident activity recognition using incremental decision trees, In: Adaptive and Intelligent Systems, Springer, 2014, 182-19110.1007\/978-3-319-11298-5_19"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_027_w2aab3b7c11b1b6b1ab1ac27Aa","unstructured":"[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, 48-51"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_028_w2aab3b7c11b1b6b1ab1ac28Aa","doi-asserted-by":"crossref","unstructured":"[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, 211-21510.1109\/FITME.2010.5654824","DOI":"10.1109\/FITME.2010.5654824"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_029_w2aab3b7c11b1b6b1ab1ac29Aa","unstructured":"[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, 158-17510.1007\/978-3-540-24646-6_10"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_030_w2aab3b7c11b1b6b1ab1ac30Aa","doi-asserted-by":"crossref","unstructured":"[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, 1-1210.1145\/2971648.2971691","DOI":"10.1145\/2971648.2971691"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_031_w2aab3b7c11b1b6b1ab1ac31Aa","doi-asserted-by":"crossref","unstructured":"[31] N. C. Krishnan, D. J. Cook, Activity recognition on streaming sensor data, Pervasive and Mobile Computing, 2014, 10, 138-15410.1016\/j.pmcj.2012.07.003397957024729780","DOI":"10.1016\/j.pmcj.2012.07.003"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_032_w2aab3b7c11b1b6b1ab1ac32Aa","doi-asserted-by":"crossref","unstructured":"[32] J. P\u00e4rkk\u00e4, 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), 1211-121510.1109\/TITB.2010.205506020813625","DOI":"10.1109\/TITB.2010.2055060"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_033_w2aab3b7c11b1b6b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"[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, 3337-334010.1109\/ICASSP.2008.4518365","DOI":"10.1109\/ICASSP.2008.4518365"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_034_w2aab3b7c11b1b6b1ab1ac34Aa","doi-asserted-by":"crossref","unstructured":"[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), 115-13010.1016\/j.pmcj.2010.12.001","DOI":"10.1016\/j.pmcj.2010.12.001"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_035_w2aab3b7c11b1b6b1ab1ac35Aa","unstructured":"[35] T. Hastie, R. Tibshirani, J. Friedman, Overview of supervised learning, In: The elements of Statistical Learning, Springer, 2009, 9-4110.1007\/978-0-387-84858-7_2"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_036_w2aab3b7c11b1b6b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[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), 323-34810.1037\/a0016973292798219968396","DOI":"10.1037\/a0016973"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_037_w2aab3b7c11b1b6b1ab1ac37Aa","doi-asserted-by":"crossref","unstructured":"[37] D. G. Denison, B. K. Mallick, A. F. Smith, A bayesian cart algorithm, Biometrika, 1998, 85(2), 363-37710.1093\/biomet\/85.2.363","DOI":"10.1093\/biomet\/85.2.363"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_038_w2aab3b7c11b1b6b1ab1ac38Aa","unstructured":"[38] J. R. Quinlan, C4. 5: Programs for Machine Learning, Elsevier, 2014"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_039_w2aab3b7c11b1b6b1ab1ac39Aa","doi-asserted-by":"crossref","unstructured":"[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), 65-7410.1016\/j.eswa.2005.01.006","DOI":"10.1016\/j.eswa.2005.01.006"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_040_w2aab3b7c11b1b6b1ab1ac40Aa","unstructured":"[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:1309.0238"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_041_w2aab3b7c11b1b6b1ab1ac41Aa","doi-asserted-by":"crossref","unstructured":"[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), 29-4210.1007\/s11227-009-0335-0","DOI":"10.1007\/s11227-009-0335-0"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_042_w2aab3b7c11b1b6b1ab1ac42Aa","doi-asserted-by":"crossref","unstructured":"[42] D. J. Cook, M. Schmitter-Edgecombe, Assessing the quality of activities in a smart environment, Methods of Information in Medicine, 2009, 48(5), 480-48510.3414\/ME0592275986319448886","DOI":"10.3414\/ME0592"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_043_w2aab3b7c11b1b6b1ab1ac43Aa","unstructured":"[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, 232-23510.4108\/icst.pervasivehealth.2013.252120"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_044_w2aab3b7c11b1b6b1ab1ac44Aa","unstructured":"[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), 25-35"},{"key":"2022042712092642069_j_pjbr-2018-0011_ref_045_w2aab3b7c11b1b6b1ab1ac45Aa","doi-asserted-by":"crossref","unstructured":"[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), 11953-1197110.3390\/s150511953448197326007738","DOI":"10.3390\/s150511953"}],"container-title":["Paladyn, Journal of Behavioral Robotics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/pjbr.2018.9.issue-1\/pjbr-2018-0011\/pjbr-2018-0011.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/pjbr-2018-0011\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/pjbr-2018-0011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:31:23Z","timestamp":1651080683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/pjbr-2018-0011\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,1]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,7,25]]},"published-print":{"date-parts":[[2018,7,1]]}},"alternative-id":["10.1515\/pjbr-2018-0011"],"URL":"https:\/\/doi.org\/10.1515\/pjbr-2018-0011","relation":{},"ISSN":["2081-4836"],"issn-type":[{"value":"2081-4836","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,1]]}}}