A pytorch code about ETRI2023, ~~~~~~~~.
To train or inference our models, please clone this repository.😀
This project was researched by Minseong Kweon, Jaehyeong Park, Kyunghyun Kim
Feel free to contact us if you have any questions,
📬 wou1202@pusan.ac.kr
📬 ianpark318@pusan.ac.kr
📬 klps44@pusan.ac.kr
Convert_RP.pyconverts time series datas of 3-axis accelerometer to RP.MFCC_convert.ipynbconverts time series datas of 3-axis accelerometer to RP.
$ ./run_mfcc.sh
$ ./run_rp.sh
train_mfcc.pytrains mfcc imagestrain_rptrains rp images
$ ./infer.sh
infer.pytests our model (inference)
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[2] Jaeyoung Chang, et al, “Development of Real-time Video Surveillance System Using the Intelligent Behavior Recognition Technique”, The Journal of The Institute of Internet, Broadcasting and Communication, vol. 19, no. 2, pp. 161-168, 2020.
[3] Nedorubova, A., Kadyrova, A., Khlyupin, A., “Human activity recognition using continuous wavelet transform and convolutional neural network”, doi: https://doi.org/10.48550/arXiv.2106.12666, 2021.
[4] Chen, Y., Xue, Y., “A deep learning approach to human activity recognition based on single accelerometer”, In 2015 IEEE international conference onsystems, man, and cybernetics, pp. 1488-1492, 2015.
[5] He, Z., He, Z., “Accelerometer-based Gesture Recognition Using MFCC and HMM”, In 2018 IEEE 4th International Conference on Computer and Communications (ICCC), pp. 1435-1439, 2018.
[6] Seungeun Chung, et al., “Real-world multimodallifelog dataset for human behavior study”, ETRI Journal, vol. 43, no. 6, 2021.
[7] Jianjie, L., Kai-Yu, Tong, “Robust Single Accelerometer-Based Activity Recognition Using Modified Recurrence Plot”, IEEE Sensors Journal, vol. 19, no. 15, 2019.