Abstract
Data augmentation is important for training neural networks, especially when there is not enough data to train a network well. However, data augmentation that results in the loss of label information may reduce the performance of the model. Most conventional data augmentation methods have been developed for image- or sound-related tasks, in which case the label information of the augmented data is easily and intuitively verified by human observation. However, in the case of sensor signals, it is difficult to recognize whether there is a change in the label information of the augmented data. We propose a systematic data augmentation method to maximize the performance by automatically finding the range of augmentation that preserves the labels of the augmented data. The experimental results show that the proposed method to extract the label-preserving range is practical and that the retrained model using data augmented within this range improves the performance by at least 5% without the need to further optimize the model architecture.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D.A., Hernández, M.V., Wardlaw, J., & Rueckert, D. (2018) Gan augmentation: Augmenting training data using generative adversarial networks. arXiv:1810.10863
Chatziagapi, A., Paraskevopoulos, G., Sgouropoulos, D., Pantazopoulos, G., Nikandrou, M., Giannakopoulos, T., et al. (2019). Data augmentation using gans for speech emotion recognition. Proc Interspeech, 2019, 171–175.
Chollet, F., et al. (2015). Keras: Deep learning library for theano and tensorflow. https://keras.io/k.7(8):T1
Chung, S., Lim, J., Noh, K. J., Kim, G., & Jeong, H. (2019). Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning. Sensors, 19(7), 1716.
Cui, X., Goel, V., & Kingsbury, B. (2015). Data augmentation for deep neural network acoustic modeling. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 23(9), 1469–1477.
Dawar, N., Ostadabbas, S., & Kehtarnavaz, N. (2018). Data augmentation in deep learning-based fusion of depth and inertial sensing for action recognition. IEEE Sensors Letters, 3(1), 1–4.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
Howard, A.G. (2013). Some improvements on deep convolutional neural network based image classification. arXiv:1312.5402
Jeong, C. Y., & Kim, M. (2019). An energy-efficient method for human activity recognition with segment-level change detection and deep learning. Sensors, 19(17), 3688.
Kim, M., Jeong, C.Y., & Shin, H.C. (2018). Activity recognition using fully convolutional network from smartphone accelerometer. In 2018 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 1482–1484). IEEE
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th international conference on neural information processing systems (NIPS’12) (pp. 1097–1105).
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011). Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2), 74–82.
Le Guennec, A., Malinowski, S., & Tavenard, R. (2016). Data augmentation for time series classification using convolutional neural networks. In ECML/PKDD workshop on advanced analytics and learning on temporal data
Nalepa, J., Myller, M., & Kawulok, M. (2019). Training- and test-time data augmentation for hyperspectral image segmentation. IEEE Geoscience and Remote Sensing Letters, 17(2), 292–296.
Nanni, L., Maguolo, G., & Paci, M. (2019). Data augmentation approaches for improving animal audio classification. arXiv:1912.07756
Ohashi, H., Al-Nasser, M., Ahmed, S., Akiyama, T., Sato, T., & Nguyen, P., et al. (2017). Augmenting wearable sensor data with physical constraint for dnn-based human-action recognition. ICML 2017 Times Series Workshop (pp. 6–11). Sydney: Australia.
Piczak, K.J. (2015). Environmental sound classification with convolutional neural networks. In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) (pp 1–6). IEEE
Redmon, J., & Farhadi, A. (2017). Yolo9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7263–7271)
Salamon, J., & Bello, J. P. (2017). Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Processing Letters, 24(3), 279–283.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., & Kulić, D. (2017). Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. arXiv:1706.00527
Weiss, G.M., & Lockhart, J. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence
Wong, S.C., Gatt, A., Stamatescu, V., & McDonnell, M.D. (2016). Understanding data augmentation for classification: when to warp? In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–6). IEEE
Yoo, S. B., & Han, M. (2020). Temporal matching prior network for vehicle license plate detection and recognition in videos. ETRI Journal,.
Acknowledgements
This work was supported by the ICT R&D Program of MSIT/IITP. [1711103127, Development of Human Enhancement Technology for Auditory and Muscle Support]
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kim, M., Jeong, C.Y. Label-preserving data augmentation for mobile sensor data. Multidim Syst Sign Process 32, 115–129 (2021). https://doi.org/10.1007/s11045-020-00731-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-020-00731-2