Abstract
Human Activity Recognition (HAR) integrates ambient assisted living (AAL), leading to smart home automation for monitoring activities, healthcare, fall detection, etc. Various researchers have proposed a single-resident HAR system for ambient-sensor based smart home data, which is simple, and single-resident is not always the case. Multi-resident recognition is slightly complex and time-consuming. The researchers have made several efforts to generate benchmark datasets, such as CASAS, ARAS, vanKasteren, etc., for baseline comparison and performance analysis. However, these datasets have certain limitations, such as data association, annotation scarcity, computational cost, and even with data collection itself. This paper profoundly analyzed these limitations and manually clustered the activity labels to record the improvement in the performance of the system in terms of both recognition rate and computational time on the ARAS dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., De Munari, I.: IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Things J. 6(5), 8553–8562 (2019). https://doi.org/10.1109/jiot.2019.2920283
Sharma, V., Gupta, M., Pandey, A.K., Mishra, D., Kumar, A.: A review of deep learning-based human activity recognition on benchmark video datasets. Appl. Artif. Intell. 36(1), 2093705 (2022). https://doi.org/10.1080/08839514.2022.2093705
Almeida, A., Mulero, R., Rametta, P., Urošević, V., Andrić, M., Patrono, L.: A critical analysis of an IoT-aware AAL system for elderly monitoring. Futur. Gener. Comput. Syst. 97, 598–619 (2019). https://doi.org/10.1016/j.future.2019.03.019
Gupta, N., Gupta, S.K., Pathak, R.K., Jain, V., Rashidi, P., Suri, J.S.: Human activity recognition in artificial intelligence framework: a narrative review. Artif. Intell. Rev. 55(6), 4755–4808 (2022)
Anikwe, C.V., et al.: Mobile and wearable sensors for data-driven health monitoring system: state-of-the-art and future prospect. Expert Syst. Appl. 202, 117362 (2022). https://doi.org/10.1016/j.eswa.2022.117362
Babangida, L., Perumal, T., Mustapha, N., Yaakob, R.: Internet of things (IoT) based activity recognition strategies in smart homes: a review. IEEE Sens. J. 22(9), 8327–8336 (2022). https://doi.org/10.1109/jsen.2022.3161797
Alemdar, H., Durmaz Incel, O., Ertan, H., Ersoy, C.: ARAS human activity datasets in multiple homes with multiple residents. In: Proceedings of the ICTs for Improving Patients Rehabilitation Research Techniques (2013). https://doi.org/10.4108/icst.pervasivehealth.2013.252120
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., Liu, Y.: Deep learning for sensor-based human activity recognition. ACM Comput. Surv. 54(4), 1–40 (2021). https://doi.org/10.1145/3447744
Shiri, F.M., Perumal, T., Mustapha, N., Mohamed, R., Ahmadon, M.A.B., Yamaguchi, S.: A survey on multi-resident activity recognition in smart environments. arXiv preprint: arXiv:2304.12304 (2023)
Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2013). https://doi.org/10.1109/mc.2012.328
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Human activity recognition from wireless sensor network data: benchmark and software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4. Atlantis Press, Amsterdam (2011). https://doi.org/10.2991/978-94-91216-05-3_8
De-La-Hoz-Franco, E., Bernal Monroy, E., Ariza-Colpas, P., Mendoza-Palechor, F., Espinilla, M.: UJA human activity recognition multi-occupancy dataset. In: Proceedings of the 54th Hawaii International Conference on System Sciences (2021). https://doi.org/10.24251/hicss.2021.236
Ramos, R.G., Domingo, J.D., Zalama, E., Gómez-García-Bermejo, J., López, J.: SDHAR-HOME: a sensor dataset for human activity recognition at home. Sensors 22(21), 8109 (2022). https://doi.org/10.3390/s22218109
Arrotta, L., Bettini, C., Civitarese, G.: The MARBLE dataset: multi-inhabitant activities of daily living combining wearable and environmental sensors data. In: Hara, T., Yamaguchi, H. (eds.) MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 419, pp. 451–468. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94822-1_25
Chen, D., Yongchareon, S., Lai, E.M.-K., Yu, J., Sheng, Q.Z., Li, Y.: Transformer with bidirectional GRU for nonintrusive, sensor-based activity recognition in a multiresident environment. IEEE Internet Things J. 9(23), 23716–23727 (2022). https://doi.org/10.1109/jiot.2022.3190307
Lentzas, A., Dalagdi, E., Vrakas, D.: Multilabel classification methods for human activity recognition: a comparison of algorithms. Sensors 22(6), 2353 (2022). https://doi.org/10.3390/s22062353
Natani, A., Sharma, A., Peruma, T., Sukhavasi, S.: Deep learning for multi-resident activity recognition in ambient sensing smart homes. In: 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) (2019). https://doi.org/10.1109/gcce46687.2019.9015212
Jethanandani, M., Sharma, A., Perumal, T., Chang, J.-R.: Multi-label classification based ensemble learning for human activity recognition in smart home. Internet Things 12, 100324 (2020). https://doi.org/10.1016/j.iot.2020.100324
Natani, A., Sharma, A., Perumal, T.: Sequential neural networks for multi-resident activity recognition in ambient sensing smart homes. Appl. Intell. 51(8), 6014–6028 (2021). https://doi.org/10.1007/s10489-020-02134-z
Ramanujam, E., Perumal, T.: MLMO-HSM: multi-label multi-output hybrid sequential model for multi-resident smart home activity recognition. J. Ambient. Intell. Humaniz. Comput. 14(3), 2313–2325 (2023)
Perumal, T., Ramanujam, E., Suman, S., Sharma, A., Singhal, H.: Internet of things centric-based multiactivity recognition in smart home environment. IEEE Internet Things J. 10(2), 1724–1732 (2023). https://doi.org/10.1109/jiot.2022.3209970
The multilayer perceptron. Neural Computing - An Introduction (1990). https://doi.org/10.1201/9781420050431.ch4
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014). https://doi.org/10.3115/v1/d14-1179
O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint: arXiv:1511.08458 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ramanujam, E., Kalimuthu, S., Harshavardhan, B.V., Perumal, T. (2024). Improvement in Multi-resident Activity Recognition System in a Smart Home Using Activity Clustering. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-45878-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45877-4
Online ISBN: 978-3-031-45878-1
eBook Packages: Computer ScienceComputer Science (R0)
