Smart Home Activity Recognition For Ambient Assisted Living(AAL)

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Published Sep 8, 2021
Kavitha R Sakina Naaz


With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer’s Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual’s well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. AAL is already being used to observe the wellness of the elderly population across the globe. This paper gives an insight into human activity recognition in a smart home living environment. A smart home activity recognition model is proposed and implemented using four machine learning algorithms. The proposed model is applied to six different publicly available datasets and the accuracy was estimated using different measures. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the datasets.

How to Cite

R, K., & Naaz, S. (2021). Smart Home Activity Recognition For Ambient Assisted Living(AAL). SPAST Abstracts, 1(01). Retrieved from
Abstract 10 |

Article Details


Activity Recognition, Health care., Elder care, Machine Learning, Smart Home

[1]. Telecommunication Engineering Centre Department of Telecommunications Ministry of Communications Government of India, Technical Report M2M/IoT Enablement in Smart Homes, first ed., India, 2017
[2] P. Rashidi, A. Mihailidis, “A survey on ambient-assisted living tools for older adults”, IEEE journal of biomedical and health informatics, Vol. 17, Issue 3, pp. 579-590, 2013.
[3] W.J.J. Yeung, A.K.L. Cheung, “Living alone: One-person households in Asia”, Demographic research, Vol. 32, pp. 1099-1112, 2015.
[4] Rema Nagarajan, The Times of India, India. Updated 2014, [Online]. Available:
[5] V. Jakkula, D.J. Cook, “Anomaly detection using temporal data mining in a smart home environment”, Methods of information in medicine, Vo. 47, Issue 1, pp. 70-75, 2008.
[6] CASAS, Available:
[7] Van Kasteren T.L.M., Englebienne G., Kröse B.J.A. Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. Atlantis Ambient and Pervasive Intelligence, Atlantis Pres, Vol. 4, 2011.
[8] Debes C., Merentitis A., Sukhanov S., Niessen M., Frangiadakis N., and Bauer A., "Monitoring Activities Of Daily Living In Smart Homes: Understanding Human Behaviour," IEEE Signal Processing Magazine, vol. 33, p. 81, 2016.
[9] Skocir P., Krivic P., Tomeljak M., Kusek M., and Jezic G., "Activity detection in smart home environment," Procedia Computer Science, vol. 96, p.672, 2016.
[10] Zdravevska A., Dimitrievski A., Lameski P., Zdravevski E. and Trajkovik V., "Cloud-Based Recognition Of Complex Activities For Ambient Assisted Living In Smart Homes With Non-Invasive Sensors," in Proceedings of the IEEE EUROCON 2017 -17th International Conference on Smart Technologies, Ohrid, 2017
[11] Zdravevski E., Lameski P., Trajkovik V., Kulakov A., Chorbev I., Goleva R., Pombo N. and Garcia N., "Improving Activity Recognition Accuracy In Ambient-Assisted Living Systems by Automated Feature Engineering," IEEE Access, vol. 5, p. 5262, 2017.
[12] Shahi A., Woodford B. J., Lin H. "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, 2017.
[13] Paolo Barsocchi, Pietro Cassará, Daniela Giorgi, Davide Moroni and Maria Antonietta Pascali, "Computational Topology to Monitor Human Occupancy," in Proceedings of International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Kos Island, Greece, 2018
[14] Binh Nguyen, Yves Coelho, Teodiano Bastos, Sridhar Krishnan, Trends in human activity recognition with focus on machine learning and power requirements, Machine Learning with Applications, Volume 5, 2021
[15] Kemilly Dearo Garcia, Cláudio Rebelo de Sá, Mannes Poel, Tiago Carvalho, João Mendes-Moreira, João M.P. Cardoso, André C.P.L.F. de Carvalho, Joost N. Kok, An ensemble of autonomous auto-encoders for human activity recognition, Neurocomputing, Volume 439, 2021
[16] T. T. Alemayoh, J. H. Lee, and S. Okamoto, “New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition,” Sensors, vol. 21, no. 8, p. 2814, Apr. 2021.
[17] R. G. Ramos, J. D. Domingo, E. Zalama, and J. Gómez-García-Bermejo, “Daily Human Activity Recognition Using Non-Intrusive Sensors,” Sensors, vol. 21, no. 16, p. 5270, Aug. 2021.
[18] Kavitha, R., Binu, S. Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living. Procedia Comput. Sci. 165, 314–321, 2019.
[19] Shodhganga, Available: et al., Electronic Nose and Its Applications: A Survey, International Journal of Automation and Computing, DOI: 10.1007/s11633-019-1212-9.
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