LSTM-Based Deep Learning Architecture for Recognition of Human Activities

Author:

Kallepally Pooja,Rajesh M.

Publisher

Springer Nature Singapore

Reference14 articles.

1. Bülbül, E., Çetin, A., & Doğru, I. A. (2018). Human activity recognition using smartphones “Gazi”. University Ankara, Turkey, IEEE.

2. Wijekoon, A., Wiratunga, N., & Cooper, K. (2019). MEx: Multi-modal exercises dataset for human activity recognition. arXiv:1908.08992v1[cs.CV]. Aug 13, 2019.

3. Dehghani, A., Glatard, T., & Shihab, E. (2018). Subject cross-validation in human activity recognition, Conference, July 17, 2018, Washington, DC, USA.

4. Baby, A. A., Vinod, B., Chinni, C., & Mitra, K. (2018). Dynamic vision sensors for human activity recognition. arXiv:1803.04667v1[cs.CV]. 13 Mar 2018.

5. Ghazal, S., & Khan, U. S. (2018). Human posture classification using skeleton information. In 2018 international conference on Computing, Mathematics and Engineering Technologies—iCoMET 2018.

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