Improvement in Multi-resident Activity Recognition System in a Smart Home Using Activity Clustering
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45878-1_22
Reference25 articles.
1. 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
2. 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
3. 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
4. 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)
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