Skip to main content

SAKHA: An Artificial Intelligence Enabled VisualBOT for Health and Mental Wellbeing During COVID’19 Pandemic

  • Chapter
  • First Online:
Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches

Abstract

COVID19 pandemic is playing havoc all around the world. Though the world is fighting this invisible enemy it has succumbed to the devastating potential of the Coronavirus. Largest of world economies and developed nations have been exposed and their health infrastructure has collapsed during this testing time. It is assessed and predicted that the novel coronavirus which is responsible for COVID19 pandemic, may turn into an endemic (just like HIV) and will never go away. It will become part and parcel of our life and humans have to learn to live with it even if the vaccine is developed. The government’s world over is concerned with containment and eradication of this virus at the earliest and massive efforts are on at all front to contain it’s spread. As of now (3rd week of May 2020), more than 4.4 million cases of the disease have been recorded worldwide and more than 300,000 have died. The world has also seen technological innovation during this time and mechanisms to tackle COVID19 patients. Innovations in carrying out quick testing using Rapid testing kits, Artificial Intelligence (AI) powered thermal scanning for temperature monitoring in the crowd, AI-enabled contact tracing, Mobile Apps, low-cost ventilators, and many other such similar solutions. All these pertain to checking for COVID19 symptoms and taking actions thereafter, but what about the stress, pain, and shock of a person who has been put under quarantine in a facility meant for the purpose or the person who is Corona positive? In this chapter, the authors have discussed briefly the pandemic and tried to provide a solution for the mental wellbeing of such people who are under quarantine and are isolated but heavily stressed or showing stress symptoms, by creating a VisualBOT which could understand the facial expression of the person and judge his mood, for providing suitable counseling and help.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Cherry, K.: How to Cope With Quarantine. Updated on 18 Mar 2020

    Google Scholar 

  2. Centers for Disease Control and Prevention: About Quarantine and Isolation. Updated 27 Jan 2020

    Google Scholar 

  3. Novotney, A.: Social isolation: it could kill you. Monit. Psychol. Am. Psychol. Assoc. 50(5), 32 (2019)

    Google Scholar 

  4. https://www.cnbc.com/2018/04/06/how-to-make-sure-youre-investing-with-the-right-robo-advisor.html

  5. https://www.valuecoders.com/blog/technology-and-apps/history-and-evolution-of-chatbots/

  6. Ko, B.C.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018)

    Article  Google Scholar 

  7. Li, S., Deng, W.: Deep facial expression recognition: a survey. IEEE Trans. Affect. Comput. (2020)

    Google Scholar 

  8. Minaee, S., Abdolrashidi, A.: Deep-Emotion: Facial Expression Recognition Using the Attentional Convolutional Network (2019). arXiv preprint arXiv:1902.01019

  9. Sridhar, R., Wang, H., McAllister, P., Zheng, H.: E-Bot: a facial recognition-based human-robot emotion detection system. In: Proceedings of the 32nd International BCS Human-Computer Interaction Conference, vol. 32, pp. 1–5 (2018

    Google Scholar 

  10. Canedo, D., Neves, A.J.: Facial expression recognition using computer vision: a systematic review. Appl. Sci. 9(21), 4678 (2019)

    Article  Google Scholar 

  11. Gudipati, V.K., Barman, O.R., Gaffoor, M., Abuzneid, A.: Efficient facial expression recognition using AdaBoost and haar cascade classifiers. In: 2016 Annual Connecticut Conference on Industrial Electronics, Technology & Automation (CT-IETA), pp. 1–4. IEEE (2016)

    Google Scholar 

  12. Jayalekshmi, J., Mathew, T.: Facial expression recognition and emotion classification system for sentiment analysis. In: 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), pp. 1–8. IEEE (2017)

    Google Scholar 

  13. Rázuri, J.G., Sundgren, D., Rahmani, R., Cardenas, A.M.: Automatic emotion recognition through facial expression analysis in merged images based on an artificial neural network. In: 2013 12th Mexican International Conference on Artificial Intelligence, pp. 85–96. IEEE (2013)

    Google Scholar 

  14. Griol, D., Molina, J.M., de Miguel, A.S.: Developing multimodal conversational agents for an enhanced e-learning experience. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 3(1), 13–26 (2014)

    Google Scholar 

  15. Liu, C., Tang, T., Lv, K., Wang, M.: Multi-feature based emotion recognition for video clips. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 630–634 (2018)

    Google Scholar 

  16. Hu, P., Cai, D., Wang, S., Yao, A., Chen, Y.: Learning supervised scoring ensemble for emotion recognition in the wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 553–560 (2017)

    Google Scholar 

  17. Fan, Y., Lu, X., Li, D., Liu, Y.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445–450 (2017)

    Google Scholar 

  18. Yao, A., Shao, J., Ma, N., Chen, Y.: Capturing au-aware facial features and their latent relations for emotion recognition in the wild. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 451–458 (2015)

    Google Scholar 

  19. Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., Chen, X.: Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 494–501 (2014)

    Google Scholar 

  20. Ebrahimi Kahou, S.: Emotion Recognition with Deep Neural Networks. Doctoral dissertation, École Polytechnique de Montréal (2016)

    Google Scholar 

  21. Fitzpatrick, K.K., Darcy, A., Vierhile, M.: Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Mental Health 4(2), e19 (2017)

    Article  Google Scholar 

  22. Lee, J.: How chatbots Use AI, Machine Learning, and NLP to Transform Marketing and Sales (2018) https://blog.growthbot.org/how-chatbots-use-ai-machine-learning-and-nlp-to-transform-marketing-and-sales

  23. Noda, K., Arie, H., Suga, Y., Ogata, T.: Multimodal integration learning of robot behavior using deep neural networks. Robot. Auton. Syst. 62(6), 721–736 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roheet Bhatnagar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sachdev, J.S. et al. (2021). SAKHA: An Artificial Intelligence Enabled VisualBOT for Health and Mental Wellbeing During COVID’19 Pandemic. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_6

Download citation

Publish with us

Policies and ethics