Human Activity Recognition: Trends and Challenges

Author:

Thakur DipanwitaORCID,Pal ArindamORCID

Publisher

Springer Nature Switzerland

Reference95 articles.

1. Adams, R. P. & MacKay, D. J. C. (2007). Bayesian online changepoint detection. https://doi.org/10.48550/arXiv.0710.3742

2. Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339–367. https://doi.org/10.1007/s10115-016-0987-z

3. Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013). A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

4. Arlot, S., Celisse, A., & Harchaoui, Z. (2019). A kernel multiple change-point algorithm via model selection. Journal of Machine Learning Research, 20(162), 1–56. http://jmlr.org/papers/v20/16-155.html.

5. Baloch, Z., Shaikh, F. K., & Unar, M. A. (2022). CNN-LSTM-based late sensor fusion for human activity recognition in big data networks. Wireless Communications and Mobile Computing, 2022, 3434100. https://doi.org/10.1155/2022/3434100.

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