Abstract
Human Activity Recognition (HAR) plays an important role in smart home assisted living system which is one among the growing research area in smart computing. In this modern era, Smart home assisted living is highly recommended for elderly people to monitor and assist in taking care of themselves. HAR is applied in various ambiences to recognize single activity and group activity as well. This chapter focuses on single activity recognition system with respect to variety of sensors used in smart homes, activity recognition methods and wide range of communication systems that helps to ease the living style of elderly people in healthy environment which can be linked to the advancement of IoT technology in smart building. This chapter reviews many applications with variety of sensors, real time smart home projects, and smart home assisted living systems including activity recognition methods and communication systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achumba, I. E., Bersch, S., Khusainov, R., Azzi, D., & Kamalu, U. (2012). On time series sensor data segmentation for fall and activity classification. In: Proceedings of the 14th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom), Beijing, China, 2012.
Alirezaie, M., Renoux, J., Köckemann, U., Kristoffersson, A., Karlsson, L., Blomqvist, E., Tsiftes, N., Voigt, T., & Loutfi, A. (2017). An ontology-based context-aware system for smart homes: E-care@home. Sensors.
Aran, O., Sanchez-Cortes, D., Do, M.-T., & Gatica-Perez, D. (2016). Anomaly detection in elderly daily behavior in ambient sensing environments. In Proceedings of the 7th International workshop on human behavior understanding, ACM Multimedia.
Barsocchi, P., Cassará, P., Giorgi, D., Moroni, D., & Pascali, M. A. (2018). Computational topology to monitor human occupancy. International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Kos Island, Greece.
Bertomeu-Motos, A., Delegido, I., Ezquerro, S., Lledó, L. D., Catalan, J. M., & Garcia-Aracil, N. (2017). Upper-limb motion analysis in daily activities using wireless inertial sensors. Converging clinical and engineering research on neurorehabilitation II, Biosystems & Biorobotics 15.
Chahuara, P., Fleury, A., Portet, F., & Vacher, M. (2016). On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic smart homes. Journal of Ambient Intelligence and Smart Environments.
Chen, L., Hoey, J., Nugent, C. D., & Cook, D. J. Y. Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6), 790–808.
Chernbumroong, S., Cang, S., Atkins, A., & Yu, H. (2013). Elderly activities recognition and classification for applications in assisted living. Science direct-expert systems with applications., 40, 1662–1674.
Cook, D. J., Crandall, A. S., Thomas, B. S., & Krishnan, N. C. (2013). CASAS: A smart home in a box. Computer, 46, 62–69.
Debes, C., Merentitis, A., Sukhanov, S., Niessen, M., Frangiadakis, N., & Bauer, A. (2016). Monitoring activities of daily living in smart homes: Understanding human behavior. IEEE Signal Processing Magazine, 33(2), 81–94.
Demir, E., Köseoğlu, E., Sokullu, R., Şeker, B. (2017). Smart home assistant for ambient assisted living of elderly people with Dementia. International workshop on IoT, M2M and Healthcare.
Diaz, K. M., Krupka, D. J., & Chang, M. J. (2015). Fitbit: An accurate and reliable device for wireless physical activity tracking. International Journal of Cardiology, 185, 138.
Elhoushi, M., Georgy, J., Noureldin, A., & Korenberg, M. J. (2017). A survey on approaches of motion mode recognition using sensors. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1662–1686.
Fauzi, C., Sulistyo, S., & Widyawan. (2018). A survey of group activity recognition in smart building. In 2018 International Conference on Signals and Systems (ICSigSys), Bali.
Fleury, A., Vacher, M., & Noury, N. (2010). SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results. IEEE Transactions on Information Technology in Biomedicine, 14, 274.
Frejlichowski, D., Katarzyna, G.’s., Forczmanski, P., & Hofman, R. (2014). SmartMonitor- an intelligent security system for the protection of individuals and small properties with the possibility of home automation. Sensors, 14, 9922.
Guo, M., & Wang, Z. (2018). Segmentation and recognition of human motion sequences using wearable inertial sensors. Multimedia Tools and Applications, 77, 21201–21220.
Hsu, Y.-L., Chou, P.-H., Chang, H.-C., Lin, S.-L., Yang, S.-C., Su, H.-Y., Chang, C.-C., Cheng, Y.-S., & Kuo, Y.-C. (2017). Design and implementation of a smart home system using multisensor data fusion technology. Senors, 17, 1631.
Get Your Own Smart Home, CASAS http://smarthome.ailab.eecs.wsu.edu
CURE, Center for Usability Research & Engineering, Austria, Available Online: http://www.fp7-hermes.eu/
GERHOME http://www.virtualworldlets.net/Resources/Hosted/Resource.php?Name=Gerhome
GER’HOME project. Francois Bremond, http://www-sop.inria.fr/members/Francois.Bremond/topicsText/gerhomeProject.html
Hu, Q., & Li, F. (2013). Hardware design of smart home energy management system with dynamic price response. IEEE Transactions on Smart Grid, 4, 1878–1887.
Ismail, W. N., & Hassan, M. M. (2017). Mining productive-associated periodic-frequent patterns in body sensor data for smart home care. Sensors.
Jung, Y. (2017). Hybrid-aware model for senior wellness service in smart home. Sensors (Basel), 17(5), 1182.
Kavitha, R., & Binu, S. (2018). Activity recognition using machine learning techniques for smart home assisted living. Dissertation, Christ University.
Kolovou, L. T, & Lymberopoulos, D. (2011). The concept of interoperability for AAL systems. Wireless Technologies for Ambient Assisted Living and Healthcare-Systems and Applications, Medical Information Science Reference.
Krose B, Van Kasteren T, Gibson C, & Van den Dool. (2008). CARE: Context awareness in residences for elderly. In: Proceeding of the 6th International Conference of the International Society for Geron Technology, Paisa.
Laguna, J. O., Olaya, A. G., & Borrajo, D. (2011). A dynamic sliding window approach for activity recognition. User modeling adaption and personalization (Vol. 6787, p. 219). Berlin/Heidelberg: Springer.
Lara, O. D., & Labrador, M. A. (2013). A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials, (15), 1192.
Lee, M., & Gatton, T. M. (2010). Wireless health data exchange for home healthcare monitoring system. Sensors, 10, 3243.
Liu, Y., Ouyang, D., Liu, Y., & Chen, R. (2017). A novel approach based on time cluster for activity recognition of daily living in smart homes symmetry. A MDPI Journal.
Lotfi, A., Langensiepen, C., Moreno, P. A., G’omez, E. J., & Chernbumroong, S. (2017). An ambient assisted living technology platform for informal carers of the elderly (LNICST 181). Springer.
Lymberopoulos, D., Bamis, A., & Savvides, A. (2011). Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. Universal Access in the Information Society, 10, 125.
Machot, F. A., & Mayr, H. C. (2016). Improving human activity recognition by smart windowing and spatio-temporal feature analysis. In: PETRA’16 proceedings of the 9th ACM international conference on PErvasive technologies related to assistive environment.
Machot, F. A., Ranasinghe, S., Plattner, J., & Jnoub, N. (2018). Human activity recognition based on real life scenarios. In 2018 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), Athens (pp. 3–8).
Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., & Deen, M. J. (2017). Smart homes for elderly healthcare—Recent advances and research challenges. Sensors.
Mendes, S., Queiroz, J., & Leitão, P. (2017). Data driven multi-agent m-health system to characterize the daily activities of elderly people. In 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), Lisbon.
Mocanu, I., Cramariuc, B., Balan, O., & Moldoveanu, A. (2017). A framework for activity recognition through deep learning. In: ICIAP 2017, Part II, LNCS 10485.
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.
Nguyen, T., & Minh-Thai, N. T.-N. (2015). An approach for developing intelligent systems in smart home environment. Cham: Springer.
Oyeleke, R. O., Yu, C.-Y., & Chang, C. (2018). Situ-centric reinforcement learning for recommendation of tasks in activities of daily living in smart homes (pp. 317–322). https://doi.org/10.1109/COMPSAC.2018.10250.
Park, J., Jang, K., & Yang, S. (2018). Deep neural networks for activity recognition with multi-sensor data in a smart home. In: 2018 IEEE 4th world forum on Internet of Things (WF-IoT), Singapore (pp. 155–160).
Ransing, R. S., & Rajput, M. (2015). International conference on Nascent technologies in the engineering field. IEEE.
Rosati, S., Balestra, G., & Knaflitz, M. (2018). Comparison of different sets of features for human activity recognition by wearable sensors. Sensors, 18, 4189.
Shahi, A., Woodford, B. J., & Lin, H. (2017). Dynamic real-time segmentation and recognition of activities using a multi-feature windowing approach. In: Proceeding of the PAKDD 2017 trends and applications in knowledge discovery and data mining.
Skocir, P., Krivic, P., Tomeljak, M., Kusek, M., & Jezic, G. (2016). Activity detection in smart home environment. In 20th international conference on knowledge based and intelligent information and engineering systems. Procedia Computer Science.
Tapia, E. M., Intille, S. S., & Larson, K. (2004a). Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of the international conference on pervasive computing. Springer-LNCS.
Tapia, E. M., Intille, S. S., Larson, K. (2004b). Activity recognition in the home using simple and ubiquitous sensors. International Conference on Pervasive Computing, Springer-LNCS.
Theodoridis, S., & Koutroumbas, K. (2009). Chapter 3. In Pattern recognition (4th ed., pp. 77–82). London: Elsevier.
Van Kasteren, T., Noulas, A., Englebienne, G., & Kröse, B. (2008). Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on ubiquitous computing, Seoul, South Korea.
Virone, G., Alwan, M., Dalal, S., Kell, S. W., Turner, B., & Stankovic, J. A. (2008). Behavioral patterns of older adults in assisted living. IEEE Transactions on Information Technology in Biomedicine, 12, 387.
Wang, W., & Miao, C. (2008). Activity recognition in new smart home environments. In PETRA 2008 Athens, Greece.
Witten, I. H., & Frank, E. (2005). Chapters 2–6. In Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco.
Wu, C. (2019). Nonparametric activity recognition system in smart homes based on heterogeneous sensor data. IEEE Transactions on Automation Science and Engineering, 16(2), 678–690.
Zdravevska, A., Dimitrievski, A., Lameski, P., Zdravevski, E., & Trajkovik, V. (2017). Cloud-based recognition of complex activities for ambient assisted living in smart homes with non-invasive sensors. In IEEE EUROCON 2017 -17th International Conference on Smart Technologies, Ohrid.
Zdravevski, E., Lameski, P., Trajkovik, V., Kulakov, A., Chorbev, I., Goleva, R., Pombo, N., & And Garcia, N. (2017). Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access, 5, 5262–5280.
Zolfaghari, S., & Keyvanpour, M. R. (2016). SARF: Smart activity recognition framework in ambient assisted living. In 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk (pp. 1435–1443).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nizar Banu, P.K., Kavitha, R. (2020). Single Activity Recognition System: A Review. In: Alam, M., Shakil, K., Khan, S. (eds) Internet of Things (IoT). Springer, Cham. https://doi.org/10.1007/978-3-030-37468-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-37468-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37467-9
Online ISBN: 978-3-030-37468-6
eBook Packages: Computer ScienceComputer Science (R0)