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THERADIA: Digital Therapies Augmented by Artificial Intelligence

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Abstract

Digital plays a key role in the transformation of medicine. Beyond the simple computerisation of healthcare systems, many non-drug treatments are now possible thanks to digital technology. Thus, interactive stimulation exercises can be offered to people suffering from cognitive disorders, such as developmental disorders, neurodegenerative diseases, stroke or traumas. The efficiency of these new treatments, which are still primarily offered face-to-face by therapists, can be greatly improved if patients can pursue them at home. However, patients are left to their own devices which can be problematic. We introduce THERADIA, a 5-year project that aims to develop an empathic virtual agent that accompanies patients while receiving digital therapies at home, and that provides feedback to therapists and caregivers. We detail the architecture of our agent as well as the framework of our Wizard-of-Oz protocol, designed to collect a large corpus of interactions between people and our virtual assistant in order to train our models and improve our dialogues.

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References

  1. Joubert, C., Chainay, H.: Aging brain: the effect of combined cognitive and physical training on cognition as compared to cognitive and physical training alone - a systematic review. Clin. Interven. Aging 13, 1267–1301 (2018)

    Article  Google Scholar 

  2. Klimová, B., Vališ, M.: Smartphone applications can serve as effective cognitive training tools in healthy aging. Front. Aging Neurosci. 9, 436 (2018)

    Article  Google Scholar 

  3. van der Linden, S., Sitskoorn, M.M., Rutten, G.-J.M., Gehring, K.: Feasibility of the evidence-based cognitive telerehabilitation program Remind for patients with primary brain tumors. J. Neuro-Oncol. 137, 523–532 (2018)

    Google Scholar 

  4. Wilms, I.L.: The computerized cognitive training alliance – a proposal for alliance model for home-based computerized cognitive training. CellPress, Heliyon 6, e03254 (2020)

    Google Scholar 

  5. Turunen, M., et al.: Computer-based cognitive training for older adults: determinants of adherence. PlosOne 14(7), e0219541 (2019)

    Google Scholar 

  6. Kethuneni, S., August, S.E., Ian Vales, J.: Personal health care assistant/companion in virtual world. In: Association for the Advancement of Artificial Intelligence (AAAI), Fall Symposium Series (2009)

    Google Scholar 

  7. Vaidyam, A.N., Wisniewski, H., Halamka, J.D., Kashavan, M.S., Torous, J.B.: Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can. J. Psychiatr. 64(7), 456–464 (2019)

    Article  Google Scholar 

  8. Cassell, J., Sullivan, J., Prevost, O., Churchill, E.: Embodied Conversational Agents. MIT Press, Cambridge (2000)

    Book  Google Scholar 

  9. Cummins, N., Baird, A., Schuller, B.W.: Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning. Methods 41–54 (2018)

    Google Scholar 

  10. Ringeval, F., et al.: AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition. In: International Workshop on Audio/Visual Emotion Challenge, AVEC 2019, Nice, France (2019)

    Google Scholar 

  11. Swerts, M., Krahmer, E.: Audiovisual prosody and feeling of knowing. J. Memory Lang. 81–94 (2005)

    Google Scholar 

  12. Barbulescu, A., Ronfard, R., Bailly, G.: A generative audio-visual prosodic model for virtual actors. IEEE Comput. Graphics Appl. 37(6), 40–51 (2017)

    Article  Google Scholar 

  13. Picard, R.W.: Affective Computing. MIT Press, Cambridge (2000)

    Book  Google Scholar 

  14. Khare, A., Parthasarathy, S., Sundaram, S.: Self-Supervised learning with cross-modal transformers for emotion recognition. arXiv preprint arXiv:2011.10652 (2020)

  15. Siriwardhana, S., Reis, A., Weerasekera, R., Nanayakkara, S.: Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition. arXiv preprint arXiv:2008.06682 (2020)

  16. Thórisson, K.R.: Natural turn-taking needs no manual: computational theory and model, from perception to action. In: Multimodality in Language and Speech Systems, pp. 173–207. Springer, Dordrecht (2002)

    Google Scholar 

  17. Skantze, G.: Turn-taking in conversational systems and human-robot interaction: a review. Comput. Speech Lang. 67, 101–178 (2021)

    Article  Google Scholar 

  18. Ekman, P.: Facial expressions of emotion: New findings, new questions (1992)

    Google Scholar 

  19. Russell, J.A.: Reading emotions from and into faces: resurrecting a dimensional-contextual perspective, In: Russell, J.A., Fernández-Dols, J.M. (eds.) Studies in Emotion and Social Interaction. The Psychology of Facial Expression, pp. 295–320. CUP (1997)

    Google Scholar 

  20. Scherer, K.R.: The dynamic architecture of emotion: Evidence for the component process model. Cogn. Emot. 23(7), 1307–1351 (2009)

    Article  Google Scholar 

  21. Scherer, K.R., Dieckmann, A., Unfried, M., Ellgring, H., Mortillaro, M.: Investigating appraisal-driven facial expression and inference in emotion communication. Emotion 21(1), 73 (2019)

    Google Scholar 

  22. Shen, J., et al.: Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4779–4783 (2018)

    Google Scholar 

  23. Tits, N., Wang, F., El Haddad, K., Pagel, V., Dutoit, T.: Visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis. Interspeech, pp. 4475–4479 (2019)

    Google Scholar 

  24. Stanton, D., Wang, Y., Skerry-Ryan, R.J.: Predicting expressive speaking style from text in end-to-end speech synthesis. In: IEEE Spoken Language Technology Workshop (SLT), pp. 595–602 (2018)

    Google Scholar 

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Acknowledgments

This research has received funding from the Banque Publique d’Investissement (BPI) under grant agreement THERADIA, the Association Nationale de la Recherche et de la Technologie (ANRT), under grant agreement No. 2019/0729, and has been partially supported by MIAI@Grenoble-Alpes, (ANR-19-P3IA-0003).

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Correspondence to Franck Tarpin-Bernard .

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Tarpin-Bernard, F. et al. (2021). THERADIA: Digital Therapies Augmented by Artificial Intelligence. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_55

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  • DOI: https://doi.org/10.1007/978-3-030-80285-1_55

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