Skip to main content

Towards a New Multi-tasking Learning Approach for Human Fall Detection

  • Conference paper
  • First Online:
The 12th Conference on Information Technology and Its Applications (CITA 2023)


Many fall detection systems are being used to provide real-time responses to fall occurrences. Automated fall detection is challenging because it requires near perfect accuracy to be clinically acceptable. Recent research has tried to improve the accuracy along with reducing the high rate of false positives. Nevertheless, there are still limitations in terms of having efficient learning approaches and proper datasets to train. To improve the accuracy, one approach is to include non-fall data from public datasets as negative examples to train the deep learning model. However, this approach could increase the imbalance of the training set. In this paper, we propose a multi-task deep learning model to tackle this problem. We divide datasets into multiple training sets for multiple tasks, and we prove this approach gives better results than a single-task model trained on all datasets. Many experiments are conducted to find the best combination of tasks for multi-task model training for fall detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. World Health Organization. Falls (2021).

  2. Rubenstein, L.Z.: Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 35(Suppl. 2), ii37–ii41 (2006).

  3. Wang, X., Ellul, J., Azzopardi, G.: Elderly fall detection systems: a literature survey. Front. Robot. AI 7, 74 (2020).

  4. Ren, L., Peng, Y.: Research of fall detection and fall prevention technologies: a systematic review. IEEE Access 7, 77702–77722 (2019).

    Article  Google Scholar 

  5. Nahian, M.J.A., et al.: Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9, 39413–39431 (2021).

    Article  Google Scholar 

  6. Santos, G.L., Endo, P.T., de Carvalho Monteiro, K.H., da Silva Rocha, E., Silva, I., Lynn, T.: Accelerometer-based human fall detection using convolutional neural networks. Sensors 19(7), 1644 (2019).

  7. Xiaodan, W., Zheng, Y., Chu, C.-H., Cheng, L., Kim, J.: Applying deep learning technology for automatic fall detection using mobile sensors. Biomed. Sig. Process. Control 72, 103355 (2022)

    Article  Google Scholar 

  8. Galvão, Y.M., Ferreira, J., Albuquerque, V.A., Barros, P., Fernandes, B.J.T.: A multimodal approach using deep learning for fall detection. Exp. Syst. Appl. 168, 114226 (2021)

    Article  Google Scholar 

  9. Kostopoulos, P., Nunes, T., Salvi, K., Deriaz, M., Torrent, J.: F2D: a fall detection system tested with real data from daily life of elderly people. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), Boston, MA, USA, pp. 397–403 (2015).

  10. Palmerini, L., Klenk, J., Becker, C., Chiari, L.: Accelerometer-based fall detection using machine learning: training and testing on real-world falls. Sensors 20(22), 6479 (2020).

  11. Broadley, R.W., Klenk, J., Thies, S.B., Kenney, L.P.J., Granat, M.H.: Methods for the real-world evaluation of fall detection technology: a scoping review. Sensors 18(7), 2060 (2018).

  12. Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., Jämsä, T.: Sensitivity and false alarm rate of a fall sensor in long-term fall detection in the elderly. Gerontology 61(1), 61–68 (2015). Epub 2014 Aug 13. PMID: 25138139.

  13. Bourke, A.K., et al.: Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach. In: 2016 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 2016, pp. 3712–3715 (2016). PMID: 28269098.

  14. Chen, K.-H., Hsu, Y.-W., Yang, J.-J., Jaw, F.-S.: Enhanced characterization of an accelerometer-based fall detection algorithm using a repository. Instrum. Sci. Technol. 45 (2016).

  15. Yu, S., Chen, H., Brown, R.A.: Hidden Markov model-based fall detection with motion sensor orientation calibration: a case for real-life home monitoring. IEEE J. Biomed. Health Inform. 22(6), 1847–1853 (2018).

    Article  Google Scholar 

  16. Taoran Sheng and Manfred Huber. 2020. Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(2), 1–18 (2020). Article 57.

  17. Barut, O., Zhou, L., Luo, Y.: Multitask LSTM model for human activity recognition and intensity estimation using wearable sensor data. IEEE Internet Things J. 7(9), 8760–8768 (2020).

    Article  Google Scholar 

  18. Peng, L., Chen, L., Ye, Z., Zhang, Y.: AROMA: a deep multi-task learning based simple and complex human activity recognition method using wearable sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(2), 1–16 (2018). Article 74.

  19. Parsa, B., Banerjee, A.: A multi-task learning approach for human activity segmentation and ergonomics risk assessment. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2352–2362 (2021)

    Google Scholar 

  20. Li, Y., Zhang, S., Zhu, B., et al.: Accurate human activity recognition with multi-task learning. CCF Trans. Pervasive Comp. Interact. 2, 288–298 (2020).

  21. Saeed, A., Ozcelebi, T., Lukkien, J.: Multi-task self-supervised learning for human activity detection. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(2), 1–30 (2019). Article 61.

  22. Pham, C., Nguyen, L., Nguyen, A., et al.: Combining skeleton and accelerometer data for human fine-grained activity recognition and abnormal behaviour detection with deep temporal convolutional networks. Multimed. Tools Appl. 80, 28919–28940 (2021).

  23. Woo, S., Park, J., Lee, J., Kweon, I.S.: CBAM: convolutional block attention module. CoRR abs/1807.06521. arXiv: 1807.06521

  24. Um, T.T., et al.: Data Augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. CoRR, abs/1706.00527 (2017)

    Google Scholar 

  25. Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Meth. Program. Biomed. 117(3), 489–501 (2014). ISSN 0169–2607

    Google Scholar 

  26. Yu, X., Jang, J., Xiong, S.: A large-scale open motion dataset (KFall) and benchmark algorithms for detecting pre-impact fall of the elderly using wearable inertial sensors. Front. Aging Neurosci. 13, 1–14 (2021)

    Article  Google Scholar 

  27. Reyes-Ortiz, J.-L., Oneto, L., SamÃ, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)

    Article  Google Scholar 

  28. Aziz, O., Musngi, M., Park, E.J., et al.: A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med. Biol. Eng. Comput. 55, 45–55 (2017).

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nhien-An Le-Khac .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, DA., Pham, C., Argent, R., Caulfield, B., Le-Khac, NA. (2023). Towards a New Multi-tasking Learning Approach for Human Fall Detection. In: Nguyen, N.T., Le-Minh, H., Huynh, CP., Nguyen, QV. (eds) The 12th Conference on Information Technology and Its Applications. CITA 2023. Lecture Notes in Networks and Systems, vol 734. Springer, Cham.

Download citation

Publish with us

Policies and ethics