Abstract
With the world population aging at a fast rate, ambient assisted living systems focused on elderly people gather more attention. Human activity recognition (HAR) is a component connected to those systems, as it allows identification of the actions performed and their utilization on behavioral analysis. This paper aims to provide a review on recent studies focusing on HAR and abnormal behavior detection specifically for seniors. The frameworks proposed in the literature are presented. The results are also discussed and summarized, along with the datasets and metrics used. The absence of a universal evaluation framework makes direct comparison not feasible, thus an analysis is made trying to divide the literature using a taxonomy. Solutions on the challenges identified are proposed, while discussing future work.
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References
Abdallah ZS, Gaber MM, Srinivasan B, Krishnaswamy S (2015) Adaptive mobile activity recognition system with evolving data streams. Neurocomputing 150(Part A):304–317. https://doi.org/10.1016/j.neucom.2014.09.074
Aguilar PAC, Boudy J, Istrate D, Dorizzi B, Mota JCM (2014) A dynamic evidential network for fall detection. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2013.2283055
Alcalá J, Ureña J, Hernández Á (2015) Activity supervision tool using non-intrusive load monitoring systems. In: IEEE international conference on emerging technologies and factory automation, ETFA, vol 2015–Octob. https://doi.org/10.1109/ETFA.2015.7301622
Alcalá J, Ureña J, Hernández Á, Gualda D (2017) Assessing human activity in elderly people using non-intrusive load monitoring. Sensors 17(2):351. https://doi.org/10.3390/s17020351
Álvarez de la Concepción MÁ, Soria Morillo LM, Álvarez García JA, González-Abril L (2017) Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive Mob Comput 34:3–13. https://doi.org/10.1016/j.pmcj.2016.05.002
Amiribesheli M, Bouchachia A (2015) Smart homes design for people with dementia. In: Proceedings—2015 international conference on intelligent environments, IE 2015, pp 156–159. https://doi.org/10.1109/IE.2015.33
Aran O, Sanchez-Cortes D, Do MT, Gatica-Perez D (2016) Anomaly detection in elderly daily behavior in ambient sensing environments. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9997 LNCS, pp 51–67. https://doi.org/10.1007/978-3-319-46843-3_4
Arifoglu D, Bouchachia A (2017) Activity recognition and abnormal behaviour detection with recurrent neural networks. Procedia Comput Sci 110:86–93. https://doi.org/10.1016/j.procs.2017.06.121
Baldinger J-L, Boudy J, Dorizzi B, Levrey J-P, Andreao R, Perpère C, Delavault F, Rocaries F, Dietrich C, Lacombe A (2004) Tele-surveillance system for patient at home : the MEDIVILLE System. In: Springer, Berlin. https://doi.org/10.1007/978-3-540-27817-7_59
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Capela NA, Lemaire ED, Baddour N, Rudolf M, Goljar N, Burger H (2016) Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants. J Neuroeng Rehabil 13(1):5. https://doi.org/10.1186/s12984-016-0114-0
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58. https://doi.org/10.1145/1541880.1541882
Chavarriaga R, Sagha H, Millán JD (2011) Ensemble creation and reconfiguration for activity recognition: an information theoretic approach. In: Conference proceedings—IEEE international conference on systems, man and cybernetics, pp 2761–2766. https://doi.org/10.1109/ICSMC.2011.6084090
Cho K, Van Merrienboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation : encoder—decoder approaches. Ssst-2014, pp 103–111. https://doi.org/10.3115/v1/W14-4012
Damaševicius R, Vasiljevas M, Šalkevicius J, Wozniak M (2016) Human activity recognition in AAL environments using random projections. Comput Math Methods Med 2016:1–17. https://doi.org/10.1155/2016/4073584
Del Rosario MB, Wang K, Wang J, Liu Y, Brodie M, Delbaere K, Lovell NH, Lord SR, Redmond SJ (2014) A comparison of activity classification in younger and older cohorts using a smartphone. Physiol Meas 35(11):2269–2286. https://doi.org/10.1088/0967-3334/35/11/2269
Do TM, Loke SW, Liu F (2013) HealthyLife: an activity recognition system with smartphone using logic-based stream reasoning. In: International conference on mobile and ubiquitous systems: computing, networking, and services, pp 188–199. https://doi.org/10.1007/978-3-642-40238-8_16
Durand VM, Barlow DH (2003) Essentials of abnormal psychology, 3rd edn. In: Essentials of abnormal psychology, 3rd edn. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=psyc4&NEWS=N&AN=2003-06650-000. Accessed 20 Jan 2018
Fan X, Xie Q, Li X, Huang H, Wang J, Chen S, Xie C, Chen J (2017) Activity recognition as a service for smart home: ambient assisted living application via sensing home. IEEE Int Conf AI Mob Serv (AIMS) 2017:54–61. https://doi.org/10.1109/AIMS.2017.29
Forman G, Scholz M (2009) Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. HP Labs 12(1):49–57. https://doi.org/10.1145/1882471.1882479
Garcia-Valverde T, Garcia-Sola A, Botia JA (2010) Improving RFID’s location based services by means of hidden Markov models. In: Frontiers in artificial intelligence and applications, vol 215, pp 1045–1046. https://doi.org/10.3233/978-1-60750-606-5-1045
Gonzalez-Abril L, Cuberos FJ, Velasco F, Ortega JA (2009) Ameva: an autonomous discretization algorithm. Expert Syst Appl 36(3 PART 1):5327–5332. https://doi.org/10.1016/j.eswa.2008.06.063
Hall M (1999) Correlation-based feature selection for machine learning. Doctoral dissertation, University of Waikato, Deptartment of Computer Science
Hochreiter S, Urgen Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hong X, Nugent C, Mulvenna M, McClean S, Scotney B, Devlin S (2009) Evidential fusion of sensor data for activity recognition in smart homes. Pervasive Mob Comput. https://doi.org/10.1016/j.pmcj.2008.05.002
Hu G, Qiu X, Meng L (2016) RTagCare: deep human activity recognition powered by passive computational RFID sensors. In: 18th Asia-Pacific network operations and management symposium, APNOMS 2016: management of softwarized infrastructure—proceedings. https://doi.org/10.1109/APNOMS.2016.7737258
Hu G, Qiu X, Meng L (2017a) Human activity recognition based on hidden Markov models using computational RFID. In: 2017 4th international conference on systems and informatics (ICSAI). Hangzhou, China, pp 813–818
Hu R, Pham H, Buluschek P, Gatica-perez D (2017b) Elderly people living alone : detecting home visits with ambient and wearable sensing. https://doi.org/10.1145/3132635.3132649
Huynh T, Schiele B (2005) Analyzing features for activity recognition. In: Proceedings of the 2005 joint conference on smart objects and ambient intelligence innovative context-aware services: usages and technologies—sOc-EUSAI’05. https://doi.org/10.1145/1107548.1107591
Johansson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14(2):201–211. https://doi.org/10.3758/BF03212378
Kang J, Kim J, Lee S, Sohn M (2018) Transition activity recognition using fuzzy logic and overlapped sliding window-based convolutional neural networks. J Supercomput. https://doi.org/10.1007/s11227-018-2470-y
Kelly J, Knottenbelt W (2015) The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data. https://doi.org/10.1038/sdata.2015.7
Kornowski R, Zeeli D, Averbuch M, Finkelstein A, Schwartz D, Moshkovitz M, Weinreb B, Hershkovitz R, Eyal D, Miller M, Levo Y, Pines A (1995) Intensive home-care surveillance prevents hospitalization and improves morbidity rates among elderly patients with severe congestive heart failure. Am Heart J 129(4):762–766. https://doi.org/10.1016/0002-8703(95)90327-5
Kumari P, Mathew L, Syal P (2017) Increasing trend of wearables and multimodal interface for human activity monitoring: a review. Biosens Bioelectron. https://doi.org/10.1016/j.bios.2016.12.001
Labrador MA, Lara OD (2013) Human activity recognition using wearable sensors and smartphones. Computer and Information Science Series. Chapman & Hall/CRC, Boca Raton, FL. https://doi.org/10.1201/b16098
Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209. https://doi.org/10.1109/SURV.2012.110112.00192
Mainetti L, Patrono L, Rametta P (2015) Capturing behavioral changes of elderly people through unobtruisive sensing technologies, pp 1–3
Meng L, Miao C, Leung C (2017) Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing. Multimed Tools Appl 76(8):10779–10799. https://doi.org/10.1007/s11042-016-3267-8
Mighali V, Patrono L, Stefanizzi ML, Solic P, Rodrigues JPC (2017) A smart remote elderly monitoring system based on IoT technologies, pp 43–48
Mukhopadhyay SC (2014) Wearable sensors for human activity monitoring: a review. IEEE Sens J 15(3):1321–1330. https://doi.org/10.1109/JSEN.2014.2370945
Nalmpantis C, Vrakas D (2018) Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9613-7
Nef T, Urwyler P, Büchler M, Tarnanas I, Stucki R, Cazzoli D, Müri R, Mosimann U (2015) Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data. Sensors (Switzerland) 15(5):11725–11740. https://doi.org/10.3390/s150511725
Ojetola O, Gaura E, Brusey J (2015) Data set for fall events and daily activities from inertial sensors. In: Proceedings of the 6th ACM multimedia systems conference on—MMSys’15, pp 243–248. https://doi.org/10.1145/2713168.2713198
Paul SS, Tiedemann A, Hassett LM, Ramsay E, Kirkham C, Chagpar S, Sherrington C (2015) Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults. BMJ Open Sport Exerc Med. https://doi.org/10.1136/bmjsem-2015-000013
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56(3):303–308. https://doi.org/10.1001/archneur.56.3.303
Ramasso E, Rombaut M, Pellerin D (2006) A temporal belief filter improving human action recognition in videos. In: 2006 IEEE international conference on acoustics speech and signal processing proceedings. https://doi.org/10.1109/ICASSP.2006.1660299
Rashidi P, Mihailidis A (2013) A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inform 17(3):579–590. https://doi.org/10.1109/JBHI.2012.2234129
Riboni D, Bettini C, Civitarese G, Janjua ZH, Bulgari V (2015a). From lab to life: fine-grained behavior monitoring in the elderly’s home. In: 2015 IEEE international conference on pervasive computing and communication workshops, percom workshops 2015, pp 342–347. https://doi.org/10.1109/PERCOMW.2015.7134060
Riboni D, Bettini C, Civitarese G, Janjua ZH, Helaoui R (2015b) Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment (Mci), pp 149–154. http://arxiv.org/abs/1501.05581
Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1–2):23–69. https://doi.org/10.1023/A:1025667309714
Ronao CA, Cho S-B (2016) Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst Appl 59:235–244. https://doi.org/10.1016/j.eswa.2016.04.032
Rosenhan D, Seligman M (1984) Abnormal psychology. W W Norton & Co Ltd., New York
Ruan W (2016) Unobtrusive human localization and activity recognition for supporting independent living of the elderly. In: 2016 IEEE international conference on pervasive computing and communication workshops, PerCom workshops 2016, pp 16–18. https://doi.org/10.1109/PERCOMW.2016.7457085
Sagha H, Digumarti ST, Millán JDR, Chavarriaga R, Calatroni A, Roggen D, Tröster G (2011) Benchmarking classification techniques using the opportunity human activity dataset. In: Conference proceedings—IEEE international conference on systems, man and cybernetics, pp 36–40. https://doi.org/10.1109/ICSMC.2011.6083628
Sansrimahachai W, Toahchoodee M (2017) Mobile-phone based immobility tracking system for elderly care. In: IEEE region 10 annual international conference, proceedings/TENCON, pp 3550–3553. https://doi.org/10.1109/TENCON.2016.7848718
Santiago J, Cotto E, Jaimes LG, Vergara-Laurens I (2017) Fall detection system for the elderly. In: 2017 IEEE 7th annual computing and communication workshop and conference, CCWC 2017, pp 1–4. https://doi.org/10.1109/CCWC.2017.7868363
Sebestyen G, Stoica I, Hangan A (2016) Human activity recognition and monitoring for elderly people. In: 2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP), pp 341–347. https://doi.org/10.1109/ICCP.2016.7737171
Shinmoto Torres RL, Ranasinghe DC, Shi Q, Sample AP (2013) Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In: 2013 IEEE international conference on RFID, RFID 2013. https://doi.org/10.1109/RFID.2013.6548154
Shoaib M, Scholten H, Havinga PJM (2013) Towards physical activity recognition using smartphone sensors. In: 2013 IEEE 10th international conference on ubiquitous intelligence and computing and 2013 IEEE 10th international conference on autonomic and trusted computing, pp 80–87. https://doi.org/10.1109/UIC-ATC.2013.43
Simon C, Weber P (2009) Evidential networks for reliability analysis and performance evaluation of systems with imprecise knowledge. IEEE Trans Reliab. https://doi.org/10.1109/TR.2008.2011868
Singh A, Misra N (2009) Loneliness, depression and sociability in old age. Ind Psychiatry J 18(1):51. https://doi.org/10.4103/0972-6748.57861
Steenkeste F, Banerjee S, Courturier P (2005) Telesurveillance of geriatric patients in a hospital using passive infra-red sensors. J Inf Technol Healthcare 3:89–100
Sztyler T, Stuckenschmidt H (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 2016. https://doi.org/10.1109/PERCOM.2016.7456521
Sztyler T, Stuckenschmidt H, Petrich W (2017) Position-aware activity recognition with wearable devices. Pervasive Mob Comput 38:281–295. https://doi.org/10.1016/j.pmcj.2017.01.008
Thies W, Bleiler L (2013) 2013 Alzheimer’s disease facts and figures. Alzheimer’s Dement J Alzheimer’s Assoc 9(2):208–245. https://doi.org/10.1016/j.jalz.2013.02.003
Tran T, Sutton C, Cocci R, Nie Y, Diao Y, Shenoy P (2009) Probabilistic inference over RFID streams in mobile environments. In: Proceedings—international conference on data engineering, pp 1096–1107. https://doi.org/10.1109/ICDE.2009.33
United Nations, Department of Economic and Social Affairs PD (2015) World Population Ageing 2015 (Report ST/ESA/SER.A/390)
United Nations, Department of Economic and Social Affairs PD (2017) World population prospects the 2017 revision key findings and advance tables. https://doi.org/10.1017/CBO9781107415324.004
Van Kasteren TLM, Englebienne G, Kröse BJA (2010) Human activity recognition from wireless sensor network data : benchmark and software. Activity Recognit Pervasive Intell Environ. https://doi.org/10.2991/978-94-91216-05-3_8
Weiss GM, Lockhart JW (2012) The impact of personalization on smartphone-based activity recognition. In: AAAI workshop on activity context representation: techniques and languages (October 2016), pp 98–104. http://www.aaai.org/ocs/index.php/WS/AAAIW12/paper/download/5203/5564. Accessed 20 Jan 2018
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Annals of physics, vol 54. https://doi.org/10.1002/1521-3773(20010316)40:6%3c9823::AID-ANIE9823%3e3.3.CO;2-C
World Health Organization (2015) World report on ageing and health 2015. Luxembourg, pp 1–260
World Health Organization (2017) Mental health of older adults: fact Sheet
Yao L, Sheng QZ, Li X, Gu T, Tan M, Wang X, Zou W (2017) Compressive representation for device-free activity recognition with passive RFID signal strength. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2017.2706282
Yusuf B, Woo J, Botzheim J, Kubota N, Tudjarov B (2017) Robot partner technology based on information support system for elderly people and their family. In: Proceedings—2016 3rd international conference on computing measurement control and sensor network, CMCSN 2016. https://doi.org/10.1109/CMCSN.2016.35
Zambrana C, Rafael-Palou X, Vargiu E (2016) Sleeping recognition to assist elderly people at home. Artif Intell Res. https://doi.org/10.5430/air.v5n2p64
Zhang M, Sawchuk AA (2012) USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM conference on ubiquitous computing—UbiComp’12, p 1036. https://doi.org/10.1145/2370216.2370438
Zimmermann J-P, Evans M, Lineham T, Griggs J, Surveys G, Harding L, Evans C, Roberts P (2012) Household electricity survey: a study of domestic electrical product usage. Intertek, p 600. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/208097/10043_R66141HouseholdElectricitySurveyFinalReportissue4.pdf. Accessed 20 Jan 2018
Acknowledgements
This work has been funded by the ΕΣΠΑ (2014–2020) Erevno—Dimiourgo—Kainotomo 2018/EPAnEK Program ‘Energy Controlling Voice Enabled Intelligent Smart Home Ecosystem’, General Secretariat for Research and Technology, Ministry of Education, Research and Religious Affairs.
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Lentzas, A., Vrakas, D. Non-intrusive human activity recognition and abnormal behavior detection on elderly people: a review. Artif Intell Rev 53, 1975–2021 (2020). https://doi.org/10.1007/s10462-019-09724-5
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DOI: https://doi.org/10.1007/s10462-019-09724-5