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Enhancing human activity recognition with multi-head self-attention and stacked autoencoders Cover

Enhancing human activity recognition with multi-head self-attention and stacked autoencoders

Open Access
|May 2026

References

  1. Qin, Y., Yang, J., Zhou, J., Pu, H., & Mao, Y. (2023). A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction. Advanced Engineering Informatics, 56, 101973. DOI: 10.1016/j.aei.2023.101973
  2. Ahrari, S., Moonaghi, H. K., Mahdizadeh, S. M., & Bakavoli, A. H. (2023). Experiences of what influences physical activity adherence in Iranian patients with heart failure: A qualitative study. Journal of Education and Health Promotion, 12, 276. DOI: 10.4103/jehp.jehp_1002_22
  3. Borland, M., Bergfeldt, L., Cider, Å., Rosenkvist, A., Jakobsson, M., Olsson, K., Lundwall, A., Andersson, L., & Nordeman, L. (2022). Effects of 3 months of detraining following cardiac rehabilitation in patients with atrial fibrillation. European Review of Aging and Physical Activity, 19, 14. DOI: 10.1186/s11556-022-00349-2
  4. Lyu, T., Qian, H., & Chung, S.-P. (2024). Impact of physical activity, sedentary behavior, and basal metabolic rate on PTSD, depression, and emotional instability. Brain Sciences, 14(11), 1071. DOI: 10.3390/brainsci1411107
  5. Medeiros, A. G., Cintra, M. M. M., dos Reis, M. A., Rocha, L. P., Neto, J. R. D. C., & Machado, J. R. (2024). The effects of various therapies on vulvovaginal atrophy and quality of life in gynecological cancer patients: A systematic review. Archives of Gynecology and Obstetrics, 310, 631–641. DOI: 10.1007/s00404-024-06723-3
  6. Nikolić, M., di Plinio, S., Sauter, D., Keysers, C., & Gazzola, V. (2024). The blushing brain: Neural substrates of cheek temperature increase in response to self-observation. Proceedings of the Royal Society B: Biological Sciences, 291(20240958). DOI: 10.1098/rspb.2024.0958
  7. Arora, I., Mal, P., Arora, P., Paul, A., & Kumar, M. (2024). GABAergic implications in anxiety and related disorders. Biochemical and Biophysical Research Communications, 724, 150218. DOI: 10.1016/j.bbrc.2024.150218
  8. Maallo, A. M. S., Novembre, G., Kusztor, A., McIntyre, S., Israr, A., Gerling, G., Björnsdotter, M., Olausson, H., Boehme, R. (2024). Primary somatosensory cortical processing in tactile communication. Philosophical Transactions of the Royal Society B: Biological Sciences, 379, 20230249. DOI: 10.1098/rstb.2023.0249
  9. Siciliano, R. E., Anderson, A. S., Gruhn, M. A., Henry, L. M., Vreeland, A. J., Watson, K. H., Ciriegio, A. E., Liu, Q., Ebert, J., Kuhn, T., et al. (2024). Momentary autonomic engagement during parent-adolescent conflict: Coping as a moderator of associations with emotions. Psychophysiology, 61, e14666. DOI: 10.1111/psyp.14666
  10. Zhang, Y., Tao, Y., Choi, H., & Qian, H. (2024). Exploring the causal effects of physical activity, sedentary behaviour, and diet on atrial fibrillation and heart failure: A multivariable Mendelian randomisation analysis. Nutrients, 16(23), 4055. DOI: 10.3390/nu16234055
  11. Choi, J., Lee, S., Choi, E.-K., Lee, K.-Y., Ahn, H.-J., Kwon, S., Han, K., Oh, S., & Lip, G. Y. H. (2024). Effect of physical activity on incident atrial fibrillation in individuals with varying duration of diabetes: A nationwide population study. Cardiovascular Diabetology, 23, 115. DOI: 10.1186/s12933-024-01716-1
  12. Jeong, H., Jeong, Y. W., Park, Y., Kim, K., Park, J., & Kang, D. R. (2022). Applications of deep learning methods in digital biomarker research using noninvasive sensing data. Digital Health, 8, 20552076221136640. DOI: 10.1177/20552076221136640
  13. Dooley, E. E., Winkles, J. F., Colvin, A., Kline, C. E., Badon, S. E., Diaz, K. M., et al. (2023). Method for Activity Sleep Harmonization (MASH): A novel method for harmonizing data from two wearable devices to estimate 24-h sleep–wake cycles. Journal of Activity, Sedentary, and Sleep Behavior, 2, 8. DOI: 10.1186/s42489-023-00039-7
  14. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. DOI: 10.1016/j.ins.2022.10.049
  15. Chong, J., Tjurin, P., Niemelä, M., Jämsä, T., & Farrahi, V. (2021). Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms. Gait & Posture, 89, 45–53. DOI: 10.1016/j.gaitpost.2021.03.004
  16. Wang, M., Flexeder, C., Harris, C. P., Thiering, E., Koletzko, S., Bauer, C.-P., et al. (2023). Accelerometry-assessed sleep clusters and cardiometabolic risk factors in adolescents. Obesity, 32, 200.
  17. Albalak, G., Stijntjes, M., van Bodegom, D., Jukema, J. W., Atsma, D. E., & van Heemst, D., et al. (2023). Setting your clock: Associations between timing of objective physical activity and cardiovascular disease risk in the general population. European Journal of Preventive Cardiology, 30, 232–240.
  18. Thornton, C. B., Kolehmainen, N., & Nazarpour, K. (2023). Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population. PLOS Digital Health, 2, 1–13.
  19. Farrahi, V., & Rostami, M. (2024). Machine learning in physical activity, sedentary, and sleep behavior research. Journal of Activity, Sedentary, and Sleep Behavior, 3(5). DOI: 10.1186/s44167-024-00045-9
  20. Mahyari, A., & Pirolli, P. (2021). Physical exercise recommendation and success prediction using interconnected recurrent neural networks. 2021 IEEE International Conference on Digital Health, 148–153.
  21. Sanchez, P., Voisey, J. P., Xia, T., Watson, H. I., O'Neil, A. Q., & Tsaftaris, S. A. (2022). Causal machine learning for healthcare and precision medicine. Royal Society Open Science, 9, 220638.
  22. Memon, A. R., Chen, S., To, Q. G., & Vandelanotte, C. (2023). Vigorously cited: A bibliometric analysis of the 100 most cited sedentary behaviour articles. Journal of Activity, Sedentary, and Sleep Behavior, 2, 13.
  23. Pratt, M., Varela, A. R., Salvo, D., Kohl, H. W., III, & Ding, D. (2020). Attacking the pandemic of physical inactivity: What is holding us back? British Journal of Sports Medicine, 54, 760–762.
  24. Gao Y, Li Q, Yang L et al. (2024) Causal association between sedentary behaviors and health outcomes: a systematic review and meta-analysis of mendelian randomization studies. Sports Med. DOI: 10.1007/s40279-024-02090-5
  25. Ivon Rosen, P. (2023). Analysing time-use composition as dependent variables in physical activity and sedentary behaviour research: Different compositional data analysis approaches. Journal of Activity, Sedentary, and Sleep Behavior, 2, 23.
  26. Widjiantoro, B. L., Indriawati, K., Buyung, T. S. N. A., & Wahyuadnyana, K. D. (2024). Experimental validation: Perception and localization systems for autonomous vehicles using the extended Kalman filter algorithm. International Journal on Smart Sensing and Intelligent Systems, 17(1)
  27. Deshpande, N. M., Gite, S., & Pradhan, B. (2024). Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model. International Journal on Smart Sensing and Intelligent Systems, 17(1).
  28. Bagane, P., Oswal, M., Mhetre, S., Shankar, P., Mahendrakar, P., & Jebessa, O. A. (2025). Predictive diagnostics for early identification of cardiovascular disease: A machine learning approach. International Journal on Smart Sensing and Intelligent Systems, 18(1). DOI: 10.2478/ijssis-2025-0021
  29. Rani, S., & Senthilkumar, P. (2025). Optimizing patient care with big data analytics and machine learning algorithms. International Journal on Smart Sensing and Intelligent Systems, 18(1). DOI: 10.2478/ijssis-2025-0023
  30. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006 Jul 28;313(5786):504–7. doi: 10.1126/science.1127647. PMID: 16873662.
  31. Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. NIPS.
  32. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30 (NeurIPS 2017), 6000–6010.
  33. Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv. DOI: 10.48550/arXiv.1312.4400
Language: English
Submitted on: May 19, 2025
Published on: May 15, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 S. Anandanarayanan, S. Thirumaran, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.