Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-14492-0.pdf
Reference54 articles.
1. Agarwal P, Alam M (2020) A lightweight deep learning model for human activity recognition on edge devices. Procedia Comput Sci 167:2364–2373. https://doi.org/10.1016/j.procs.2020.03.289
2. Bojan Kolosnjaji CE (2015) Neural network-based user-independent physical activity recognition for Mobile devices. 378–386 https://doi.org/10.1007/978-3-319-24834-9
3. Cao Y, Geddes TA, Yang JYH, Yang P (2020) Ensemble deep learning in bioinformatics. Nat Mach Intell 2:500–508. https://doi.org/10.1038/s42256-020-0217-y
4. Chen WH, Betancourt Baca CA, Tou CH (2017) LSTM-RNNs combined with scene information for human activity recognition. 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom 2017 2017-Decem:1–6. https://doi.org/10.1109/HealthCom.2017.8210846
5. de Vita A, Pau D, di Benedetto L, Licciardo GD (2021) Highly-accurate binary tiny neural network for low-power human activity recognition. Microprocess Microsyst 87:104371. https://doi.org/10.1016/j.micpro.2021.104371
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