Skeleton joint trajectories based human activity recognition using deep RNN
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/s11042-023-15024-6.pdf
Reference42 articles.
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2. Ahmad T, Jin L, Lin L, Tang G (2021) Skeleton-based action recognition using sparse spatio-temporal gcn with edge effective resistance. Neurocomputing 423:389–398. https://doi.org/10.1016/j.neucom.2020.10.096
3. Ahmed N, Rafiq JI, Islam MR (2020) Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors 20(1):317. https://doi.org/10.3390/s20010317
4. Al-Faris M, Chiverton JP, Yang Y, Ndzi D (2020) Multi-view region-adaptive multi-temporal dmm and rgb action recognition. Pattern Anal Appl 23 (4):1587–1602. https://doi.org/10.1007/s10044-020-00886-5
5. Andrade-Ambriz YA, Ledesma S, Ibarra-Manzano M-A, Oros-Flores MI, Almanza-Ojeda D-L (2022) Human activity recognition using temporal convolutional neural network architecture. Expert Syst Appl 191:116287. https://doi.org/10.1016/j.eswa.2021.116287
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