Klasifikasi Human Activity Recognition Menggunakan Metode CNN

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Adil Abdul Hakim

Abstract

Manusia tidak bisa dilepaskan dari kegiatan kesehariannya yang mana itu merupakan bagian dari aktivitas kehidupan keseharian. Human Activity Recognition atau biasa dikenal pengenalan aktivitas manusia saat ini sudah dapat diteliti seiring dengan pesatnya kemajuan di bidang dunia Teknologi yang berkembang saat ini, yang mana lebih banyak dikenal dengan salah satu bagian dari Artificial Intelligence. Aktivitas fisik manusia adalah keadaan tubuh seperti tidur, berjalan, berbaring, makan, jogging dan berdiri.

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How to Cite
[1]
A. A. Hakim, “Klasifikasi Human Activity Recognition Menggunakan Metode CNN”, JR, vol. 3, no. 2, Jan. 2024.
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Articles

References

W. Ahmad, B. M. Kazmi, and H. Ali, “Human Activity Recognition using Multi-Head CNN followed by LSTM,” arXiv, pp. 0–5, 2020.

H. Nematallah and S. Rajan, “Comparative study of time series-based human activity recognition using convolutional neural networks,” I2MTC 2020 - Int. Instrum. Meas. Technol. Conf. Proc., pp. 1–6, 2020, doi: 10.1109/I2MTC43012.2020.9128582.

F. Cruciani et al., “Feature learning for Human Activity Recognition using Convolutional Neural Networks,” CCF Trans. Pervasive Comput. Interact., vol. 2, no. 1, pp. 18–32, 2020, doi: 10.1007/s42486-020-00026-2.

S. M. Lee, S. M. Yoon, and H. Cho, “Human activity recognition from accelerometer data using Convolutional Neural Network,” 2017 IEEE Int. Conf. Big Data Smart Comput. BigComp 2017, pp. 131–134, 2017, doi: 10.1109/BIGCOMP.2017.7881728.

A. Ignatov, “Real-time human activity recognition from accelerometer data using Convolutional Neural Networks,” Appl. Soft Comput. J., vol. 62, pp. 915–922, 2018, doi: 10.1016/j.asoc.2017.09.027.

J. Salminen, S. gyo Jung, J. An, H. Kwak, L. Nielsen, and B. J. Jansen, “Confusion and information triggered by photos in persona profiles,” Int. J. Hum. Comput. Stud., vol. 129, no. July 2018, pp. 1–14, 2019, doi: 10.1016/j.ijhcs.2019.03.005.

K. Wang, J. He, and L. Zhang, “Attention-based convolutional neural network for weakly labeled human activities recognition with wearable sensors,” arXiv, vol. 19, no. 17, pp. 7598–7604, 2019.

K. Xia, J. Huang, and H. Wang, “LSTM-CNN Architecture for Human Activity Recognition,” IEEE Access, vol. 8, pp. 56855–56866, 2020, doi: 10.1109/ACCESS.2020.2982225.

R. A. Putri and N. Rochmawati, “Penerapan Algoritma Support Vector Machine untuk Klasifikasi Motif Citra Batik Solo Berdasarkan Fitur Multi-Autoencoders,” J. Informatics Comput. Sci., vol. 01, pp. 56–63, 2019.

M. M. Hassan, M. Z. Uddin, A. Mohamed, and A. Almogren, “A robust human activity recognition system using smartphone sensors and deep learning,” Futur. Gener. Comput. Syst., vol. 81, pp. 307–313, 2018, doi:10.1016/j.future.2017.11.029