Abstract
Human activity recognition (HAR) is necessary in numerous domains, including medicine, sports, and security. This research offers a method to improve HAR performance by using a temporally distributed integration of convolutional neural networks (CNN) and long short-term memory (LSTM). The proposed model combines the advantages of CNN and LSTM networks to obtain temporal and spatial details from sensor data. The model efficiently learns and captures the sequential dependencies in the data by scattering the LSTM layers over time. The proposed method outperforms baseline CNN, LSTM, and existing models, as shown by experimental results on benchmark datasets UCI-Sensor and Opportunity-Sensor dataset and achieved an accuracy of 97% and 96%, respectively. The results open up new paths for real-time applications and research development by demonstrating the promise of the temporally distributed CNN-LSTM model for improving the robustness and accuracy of human activity recognition from sensor data.
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References
An, S., Bhat, G., Gumussoy, S., Ogras, U.: Transfer learning for human activity recognition using representational analysis of neural networks. ACM Transactions on Computing for Healthcare 4(1), 1–21 (2023)
Ismail, W.N., Alsalamah, H.A., Hassan, M.M., Mohamed, E.: AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design. Heliyon 9(2), e13636 (2023). https://doi.org/10.1016/j.heliyon.2023.e13636
Nigam, S., Singh, R., Misra, A.K.: A review of computational approaches for human behavior detection. Archives of Computational Methods in Engineering 26, 831–863 (2019)
Gupta, N., Gupta, S.K., Pathak, R.K., Jain, V., Rashidi, P., Suri, J.S.: Human activity recognition in artificial intelligence framework: a narrative review. Artif. Intell. Rev. 55(6), 4755–4808 (2022)
Ciliberto, M., Fortes Rey, V., Calatroni, A., Lukowicz, P., Roggen, D.: Opportunity++: A multimodal dataset for video- and wearable, object, and ambient sensors-based human activity recognition. Frontiers in Computer Science 3, 1–7 (2021). https://doi.org/10.3389/fcomp.2021.792065
Gupta, S.: Deep learning based human activity recognition (HAR) using wearable sensor data. Int. J. Info. Manage. Data Insights 1(2), 100046 (2021). https://doi.org/10.1016/j.jjimei.2021.100046
Lv, T., Wang, X., Jin, L., Xiao, Y., Song, M.: Margin-based deep learning networks for human activity recognition. Sensors 20(7), 1871 (2020)
Cruciani, F., et al.: Feature learning for human activity recognition using convolutional neural networks: a case study for inertial measurement unit and audio data. CCF Trans. Pervasive Comp. Interac. 2(1), 18–32 (2020)
Shuvo, M.M.H., Ahmed, N., Nouduri, K., Palaniappan, K.: A hybrid approach for human activity recognition with support vector machine and 1d convolutional neural network. A hybrid approach for human activity recognition with support vector machine and 1D convolutional neural network. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–5. IEEE, Washington DC, USA (2020)
Nematallah, H., Rajan, S.: Comparative study of time series-based human activity recognition using convolutional neural networks. In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6. IEEE, Dubrovnik, Croatia (2020)
Du, X., Farrahi, K., Niranjan, M.: Transfer learning across human activities using a cascade neural network architecture. In: 2019 ACM International Symposium on Wearable Computers, pp. 35–44. London United Kingdom (2019)
Xu, C., Chai, D., He, J., Zhang, X., Duan, S.: InnoHAR: A deep neural network for complex human activity recognition. IEEE Access 7, 9893–9902 (2019)
Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3–11 (2019)
Rueda, F.M., Grzeszick, R., Fink, G.A., Feldhorst, S., Ten Hompel, M.: Convolutional neural networks for human activity recognition using body-worn sensors. Informatics 5(2), 1–17 (2018)
Yu, T., Chen, J., Yan, N., Liu, X.: A multi-layer parallel LSTM network for human activity recognition with smartphone sensors. In: 2018 10th International conference on wireless communications and signal processing (WCSP), pp. 1–6. IEEE, Hangzhou, Zhejiang, China (2018)
Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: 25th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1533–1540. New York, USA (2016)
Nigam, S., Singh, R., Singh, M.K., Singh, V.K.: Multiview human activity recognition using uniform rotation invariant local binary patterns. J. Ambient. Intell. Humaniz. Comput. 14(5), 4707–4725 (2023)
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Pareek, G., Nigam, S., Shastri, A., Singh, R. (2024). Human Activity Recognition with a Time Distributed Deep Neural Network. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_13
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