Abstract
The research presents an interactive toy agent leveraging a deep learning approach for treating autistic kids by making them learn Yoga. The objective of the proposed toy is to understand the basic needs of autistic kids and make them socially adaptable to their surrounding environment. Since kids with autism face social insecurities while interacting and communicating with people, we introduce an interactive toy to accompany the kid, making him or her more likely to act as a companion. The toy is orchestrated with IoT and the Deep Learning framework (HARNet) which makes it interactively instruct Yoga Asana to the autistic kid. The motion of the toy is controlled by touch sensors, and interaction is developed through the recognition of Yogo postures performed by the kid. This paper uses snippets of data in the Yoga-82 dataset. The gestures of Yoga asanas are leveraged, and the same is used for modeling HARNet. Empirical evaluations show that HARNet exhibits an accuracy of 98.52% against the Yoga-82 dataset. The cost of the Toy framework is also compared with state-of-the-art research on Humanoid Toys and the economic range of the proposed framework is evident.
Similar content being viewed by others
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Chaudhari, A., Dalvi, O., Ramade, O., Ambawade, D.: Yog-Guru: real-time yoga pose correction system using deep learning methods. In: 2021 International Conference on Communication Information and Computing Technology (ICCICT), pp. 1–6 (2021). https://doi.org/10.1109/ICCICT50803.2021.9509937
Trejo, E.W., Yuan, P.: Recognition of yoga poses through an interactive system with Kinect device. In: 2018 2nd International Conference on Robotics and Automation Sciences (ICRAS), pp. 1–5 (2018). https://doi.org/10.1109/ICRAS.2018.8443267
Rishan, F., De Silva, B., Alawathugoda, S., Nijabdeen, S., Rupasinghe, L., Liyanapathirana, C.: Infinity yoga tutor: yoga posture detection and correction system. In: 2020 5th International Conference on Information Technology Research (ICITR), pp. 1–6 (2020). https://doi.org/10.1109/ICITR51448.2020.9310832
Jose, J., Shailesh, S.: Yoga Asana identification: a deep learning approach. IOP Conf. Ser. Mater. Sci. Eng. 1110(1), 012002 (2021). https://doi.org/10.1088/1757-899x/1110/1/012002
Verma, M., Kumawat, S., Nakashima, Y., Raman, S.: Yoga-82: a new dataset for fine-grained classification of human poses. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 4472–4479 (2020)
Mohanty, A., Ahmed, A., Goswami, T., Das, A., Vaishnavi, P., Sahay, R.R.: Robust pose recognition using deep learning. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds.) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol. 460. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2107-7_9
Choudhary, P., Tazi, S.N.: An adaptive system of yogic gesture recognition for human computer interaction. In: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), pp. 399–402 (2020). https://doi.org/10.1109/ICIIS51140.2020.9342678
Raja Subramanian, R., Vasudevan, V.: A deep genetic algorithm for human activity recognition leveraging fog computing frameworks. J. Vis. Commun. Image Represent. 77, 103132 (2021). https://doi.org/10.1016/j.jvcir.2021.103132
Radhakrishna, S., Nagarathna, R., Nagendra, H.R.: Integrated approach to yoga therapy and autism spectrum disorders. J Ayurveda Integr Med. 1(2), 120–124 (2010). https://doi.org/10.4103/0975-9476.65089
Cano, S., González, C.S., Gil-Iranzo, R.M., Albiol-Pérez, S.: Affective communication for Socially Assistive Robots (SARs) for children with autism spectrum disorder: a systematic review. Sensors 21, 5166 (2021)
Milling, M., Baird, A., Bartl-Pokorny, K.D., Liu, S., Alcorn, A.M., Shen, J., Tavassoli, T., Ainger, E., Pellicano, E., Pantic, M., Cummins, N., Schuller, B.W.: Evaluating the impact of voice activity detection on speech emotion recognition for autistic children. Front. Comput. Sci. 4, 837269 (2022)
Bartl-Pokorny, K.D., et al.: Robot-based intervention for children with autism spectrum disorder: a systematic literature review. IEEE Access 9, 165433–165450 (2021)
Sotoodeh, M.S., Arabameri, E., Panahibakhsh, M., Kheiroddin, F., Mirdoozandeh, H., Ghanizadeh, A.: Effectiveness of yoga training program on the severity of autism. Complement. Ther. Clin. Pract. 28, 47–53 (2017). https://doi.org/10.1016/j.ctcp.2017.05.001
Rubio-Martín, S., García-Ordás, M.T., Bayón-Gutiérrez, M., Prieto-Fernández, N., Benítez-Andrades, J.A.: Early detection of autism spectrum disorder through AI-powered analysis of social media texts. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L'Aquila, Italy, pp. 235–240 (2023)
Yadav, S.K., Singh, A., Gupta, A., et al.: Real-time Yoga recognition using deep learning. Neural Comput. Appl. 31, 9349–9361 (2019). https://doi.org/10.1007/s00521-019-04232-7
Wang, P., Li, W., Gao, Z., Zhang, J., Tang, C., Ogunbona, P.O.: Action recognition from depth maps using deep convolutional neural networks. IEEE Trans. Hum. Mach. Syst. 46, 498–509 (2016)
Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: IEEE International Conference on Computer Vision (ICCV), pp. 4041–4049 (2015)
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1110–1118 (2015)
Wang, P., Li, W., Gao, Z., Tang, C., Zhang, J., Ogunbona, P.: ConvNets-based action recognition from depth maps through virtual cameras and Pseudocoloring. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1119–1122 (2015)
Zhu, W., Lan, C., Xing, J.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: ArXiv Preprint, AAAI, 2, Phoenix, Arizona, USA, 8
Li, Y., Li, W., Mahadevan, V., Vasconcelos, N.: Vlad3: encoding dynamics of deep features for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1951–1960 (2016)
AlDahoul, N., Sabri, M., Qalid, A., Mansoor, A.M.: Real-time human detection for aerial captured video sequences via deep models. Comput. Intell. Neurosci. (2018). https://doi.org/10.1155/2018/1639561
Mliki, H., Bouhlel, F., Hammami, M.: Human activity recognition from UAV-captured video sequences. Pattern Recognit. 100, 107140 (2019)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems (2014)
Ng, J.Y., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4694–4702 (2015)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Tenth IEEE International Conference on Computer Vision, ICCV’05, pp. 166–173 (2005)
Islam, N., Faheem, Y., UdDin, I., Talha, M., Guizani, M., Khalil, M.: A blockchainbased fog computing framework for activity recognition as an application to e-healthcare services. Future Gener. Comput. Syst. 100, 569–578 (2019)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Mu, C.-H., Li, C.-Z., Liu, Y., Qu, R., Jiao, L.-C.: Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images. Appl. Soft Comput. 84, 105727 (2019)
Pepper, SoftBank Robotics. https://www.softbankrobotics.com/emea/en/index. Accessed 13 July 2022
Leka, smart toys. https://leka.io/en/home/. Accessed 13 July 2022
Author information
Authors and Affiliations
Contributions
R. Raja conducted the complete research implementation on activity & posture recognition, built the architecture and validated the model. G. Vishnuvardhanan reviewed the research and examined the test cases.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Subramanian, R.R., Govindaraj, V. HARNet: design and evaluation of a deep genetic algorithm for recognizing yoga postures. SIViP (2024). https://doi.org/10.1007/s11760-024-03173-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11760-024-03173-6