Abstract
In some medical areas, video activity recognition has been used for patient rehabilitation, evaluating their performance doing some exercises to determine if they are correct or not. In this article we emphasize this approach applied on older adults’ physical activity motivated by the problem caused by falls in this segment of the population. Furthermore, we have developed 8 Deep Learning models to classify different video recordings with the purpose of evaluating and determining how accurately those exercises are executed by people. This article is presented as a first step work, and taking into account this progress, good results were obtained considering the problem of the small number of samples, but addressed including the typical data augmentation techniques. The main results obtained from this work is that the models achieved between 71% and 89% of accuracy, depending on the exercise, and as a conclusion, it allows us to consider this approach to be a valid tool to address the problem of fall risk evaluation in older adults.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adimark, G.: Microestudio: radiografía a los adultos mayores en chile 2020 (2020). https://www.gfk.com/es/prensa/radiografiaalosadultosmayores2020. Accessed 17 Nov 2022
Aldunate, R.: Fondef id20i10418: Self-assessment technology to prevent and reduce falls of older adults. Tech. rep, CEININA, Chile (2020)
Bergen, G., Stevens, M.R., Burns, E.R.: Falls and fall injuries among adults aged \(\ge \) 65 years-united states, 2014. Morb. Mortal. Wkly Rep. 65(37), 993–998 (2016)
Bozinovski, S.: Reminder of the first paper on transfer learning in neural networks, 1976. Informatica 44, 291–302 (2020). https://doi.org/10.31449/INF.V44I3.2828. https://www.informatica.si/index.php/informatica/article/view/2828
Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST 2014–8th Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111 (2014). https://doi.org/10.3115/V1/W14-4012. https://aclanthology.org/W14-4012
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Lee, K., Lee, I., Lee, S.: Propagating LSTM: 3D pose estimation based on joint interdependency. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 123–141. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_8
Leiva, A.M., et al.: Factores asociados a caídas en adultos mayores chilenos: evidencia de la Encuesta Nacional de Salud 2009–2010. Revista médica de Chile 147, 877–886 (2019). http://www.scielo.cl/scielo.php?script=sci_arttext &pid=S0034-98872019000700877 &nrm=iso
Luo, Y., et al.: LSTM pose machines. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5207–5215 (2018). https://doi.org/10.1109/CVPR.2018.00546
Paul, S.: Video classification with a CNN-RNN architecture (2021). https://keras.io/examples/vision/video_classification/. Accessed 19 Nov 2022
Sherrington, C., et al.: Evidence on physical activity and falls prevention for people aged 65+ years: systematic review to inform the who guidelines on physical activity and sedentary behaviour. Int. J. Behav. Nutr. Phys. Act. 17(1), 1–9 (2020)
Stromback, D., Huang, S., Radu, V.: MM-Fit multimodal deep learning for automatic exercise logging across sensing devices. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4 (2020). https://doi.org/10.1145/3432701
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th International Conference on Machine Learning, ICML 2019 2019-June, pp. 10691–10700 (2019). https://doi.org/10.48550/arxiv.1905.11946. https://arxiv.org/abs/1905.11946v5
TensorFlow: pose estimation – tensorflow lite (2022). https://www.tensorflow.org/lite/examples/pose_estimation/overview. Accessed 19 Nov 2022
Zurbuchen, N., Wilde, A., Bruegger, P.: A machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection. Sensors 21(3) (2021). https://doi.org/10.3390/s21030938. https://www.mdpi.com/1424-8220/21/3/938
Acknowledgment
The authors of this article want to thank the Chilean “Agencia Nacional de Investigación y Desarrollo (ANID)” and “ANID-Subdirección de Capital Humano/Doctorado Nacional/2019-21191017” - for the support in the development of FONDEF ID20I10418 project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Architectures of the models used in the work are presented in Table 3.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aldunate, R., San Martin, D., Manzano, D. (2024). Exploring a Deep Learning Approach for Video Analysis Applied to Older Adults Fall Risk. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-031-45648-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-45648-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45647-3
Online ISBN: 978-3-031-45648-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)