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Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception.
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2019
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2020
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2020
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A. Gu, K. Goel, and C. Ré, “Efficiently Modeling Long Sequences with Structured State Spaces,” International Conference on Learning Representations , 2021
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S. Ahmed, I. E. Nielsen, A. Tripathi, S. Siddiqui, R. P. Ramachandran, and G. Rasool, “Transformers in time-series analysis: A tutorial,” Circuits, Systems, and Signal Processing , vol. 42, no. 12, pp. 7433–7466, 2023
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Z. N. Khan and J. Ahmad, “Attention induced multi-head convolutional neural network for human activity recognition,” Applied soft computing , vol. 110, p. 107671, 2021
2021
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W. Gao, L. Zhang, W. Huang, F. Min, J. He, and A. Song, “Deep neural networks for sensor-based human activity recognition using selective kernel convolution,” IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1–13, 2021
2021
Cited alongside, same era.
L. Zhang, W. Zhang, and N. Japkowicz, “Conditional-unet: A condition-aware deep model for coherent human activity recognition from wearables,” in 2020 25th International Conference on Pattern Recognition (ICPR) . IEEE, 2021, pp. 5889–5896
2021
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T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, and J. Choo, “Reversible instance normalization for accurate time-series forecasting against distribution shift,” in International Conference on Learning Representations , 2021
2021
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I. Dirgová Luptáková, M. Kubovčík, and J. Pospíchal, “Wearable sensor-based human activity recognition with transformer model,” Sensors , vol. 22, no. 5, p. 1911, 2022
2022
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Z. Xia, X. Pan, S. Song, L. E. Li, and G. Huang, “Vision transformer with deformable attention,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 4794–4803
2022
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A. Gupta, A. Gu, and J. Berant, “Diagonal state spaces are as effective as structured state spaces,” Advances in Neural Information Processing Systems , vol. 35, pp. 22 982–22 994, 2022
2022
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A. Gu, K. Goel, A. Gupta, and C. Ré, “On the parameterization and initialization of diagonal state space models,” Advances in Neural Information Processing Systems , vol. 35, pp. 35 971–35 983, 2022
2022
Cited alongside, same era.
M. A. Al-Qaness, A. Dahou, M. Abd Elaziz, and A. Helmi, “Multi-resatt: Multilevel residual network with attention for human activity recognition using wearable sensors,” IEEE Transactions on Industrial Informatics , vol. 19, no. 1, pp. 144–152, 2023
2023
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M.-K. Yi, W.-K. Lee, and S. O. Hwang, “A human activity recognition method based on lightweight feature extraction combined with pruned and quantized cnn for wearable device,” IEEE Transactions on Consumer Electronics , vol. 69, no. 3, pp. 657–670, 2023
2023
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2023
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E. Essa and I. R. Abdelmaksoud, “Temporal-channel convolution with self-attention network for human activity recognition using wearable sensors,” Knowledge-Based Systems , vol. 278, p. 110867, 2023
2023
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W. Huang, L. Zhang, H. Wu, F. Min, and A. Song, “Channel-equalization-har: A light-weight convolutional neural network for wearable sensor based human activity recognition,” IEEE Transactions on Mobile Computing , vol. 22, no. 9, pp. 5064–5077, 2023
2023
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F. Duan, T. Zhu, J. Wang, L. Chen, H. Ning, and Y. Wan, “A multi-task deep learning approach for sensor-based human activity recognition and segmentation,” IEEE Transactions on Instrumentation and Measurement , 2023
2023
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J. Li, H. Xu, and Y. Wang, “Multi-resolution fusion convolutional network for open set human activity recognition,” IEEE Internet of Things Journal , 2023
2023
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Y. Wang, H. Xu, Y. Liu, M. Wang, Y. Wang, Y. Yang, S. Zhou, J. Zeng, J. Xu, S. Li et al. , “A novel deep multifeature extraction framework based on attention mechanism using wearable sensor data for human activity recognition,” IEEE Sensors Journal , vol. 23, no. 7, pp. 7188–7198, 2023
2023
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2024
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J. Smith, S. De Mello, J. Kautz, S. Linderman, and W. Byeon, “Convolutional state space models for long-range spatiotemporal modeling,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2024
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