Abstract
Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it is a challenging problem in terms of environments’ variability, sensory-motor systems, user habits, but also sparsity of signals and redundancy of models. Therefore, end-to-end systems fail at automatically extracting key features, and need to access context and domain knowledge. We propose to tackle feature extraction for activity recognition in smart homes by merging methods of Natural Language Processing (NLP) and Time Series Classification (TSC) domains.
We evaluate the performance of our method with two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). We analyze the contributions of the use of embedding based on term frequency encoding, to improve automatic feature extraction. Moreover we compare the classification performance of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.
The work is partially supported by project VITAAL and is financed by Brest Metropole, the region of Brittany and the European Regional Development Fund (ERDF).
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References
Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2012)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1367–1376. IEEE (2018)
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1
Gochoo, M., Tan, T.H., Liu, S.H., Jean, F.R., Alnajjar, F.S., Huang, S.C.: Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and DCNN. IEEE J. Biomed. Health Inform. 23(2), 693–702 (2018)
Hamad, R.A., Hidalgo, A.S., Bouguelia, M.R., Estevez, M.E., Quero, J.M.: Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors. IEEE J. Biomed. Health Inform. 24(2), 387–395 (2019)
Hussain, Z., Sheng, M., Zhang, W.E.: Different approaches for human activity recognition: a survey. arXiv preprint arXiv:1906.05074 (2019)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4), 150 (2019)
Li, Q., et al.: A survey on text classification: from shallow to deep learning. arXiv e-prints. arXiv-2008 (2020)
Liciotti, D., Bernardini, M., Romeo, L., Frontoni, E.: A sequential deep learning application for recognising human activities in smart homes. Neurocomputing 396, 501–513 (2019). https://doi.org/10.1016/j.neucom.2018.10.104
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mohmed, G., Lotfi, A., Pourabdollah, A.: Employing a deep convolutional neural network for human activity recognition based on binary ambient sensor data. In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–7 (2020)
Quigley, B., Donnelly, M., Moore, G., Galway, L.: A comparative analysis of windowing approaches in dense sensing environments. In: Proceedings, vol. 2, no. 19, p. 1245 (October 2018). https://doi.org/10.3390/proceedings2191245. http://dx.doi.org/10.3390/proceedings2191245
Sedky, M., Howard, C., Alshammari, T., Alshammari, N.: Evaluating machine learning techniques for activity classification in smart home environments. Int. J. Inf. Syst. Comput. Sci. 12(2), 48–54 (2018)
Singh, D., Merdivan, E., Hanke, S., Kropf, J., Geist, M., Holzinger, A.: Convolutional and recurrent neural networks for activity recognition in smart environment. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 194–205. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69775-8_12
Singh, D., et al.: Human activity recognition using recurrent neural networks. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2017. LNCS, vol. 10410, pp. 267–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66808-6_18
Tahir, S.F., Fahad, L.G., Kifayat, K.: Key feature identification for recognition of activities performed by a smart-home resident. J. Ambient Intell. Humaniz. Comput. 11(5), 2105–2115 (2019). https://doi.org/10.1007/s12652-019-01236-y
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585. IEEE (2017)
Yan, S., Lin, K.J., Zheng, X., Zhang, W.: Using latent knowledge to improve real-time activity recognition for smart IoT. IEEE Trans. Knowl. Data Eng. 32, 574–587 (2019)
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Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., Kanellos, I. (2021). Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes. In: Li, X., Wu, M., Chen, Z., Zhang, L. (eds) Deep Learning for Human Activity Recognition. DL-HAR 2021. Communications in Computer and Information Science, vol 1370. Springer, Singapore. https://doi.org/10.1007/978-981-16-0575-8_9
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