[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Evaluation of a Voice-Based Emotion Recognition Software in the Psycho-Oncological Care of Cancer Patients

  • Conference paper
  • First Online:
Human-Computer Interaction (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14684))

Included in the following conference series:

  • 870 Accesses

Abstract

Due to the increasing global incidence of cancer, the growing number of long-term survivors and the prevalence of psychological distress among cancer patients, psycho-oncological support is becoming more crucial. Recognizing the rising demand for psycho-oncological care the “Cancer Counselling App” project was initiated. As part of this project, a cancer counselling app is being developed. The development of the app incorporates the investigation a of voice-based emotion recognition which is enabled through the increasing capabilities of machine and deep learning algorithms, aiming to support the psycho-oncological care of cancer patients. The objective of this study is to identify use cases for this functionality and determine which of them are suitable for enhancing the psycho-oncological care. Through a literature review and expert interviews, seven distinct use cases were identified and evaluated. The highest-priority use case for voice-based emotion recognition is the long-term monitoring of the emotional state of cancer patients. The functionality should particularly focus on the emotions anxiety and distress, along with the psychological disorder depression, to effectively support psycho-oncological treatment.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 49.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 64.99
Price includes VAT (United Kingdom)
  • 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

Similar content being viewed by others

References

  1. Quante, A.S., et al.: Projections of cancer incidence and cancer-related deaths in Germany by 2020 and 2030. Cancer Med. 5, 2649–2656 (2016). https://doi.org/10.1002/cam4.767

    Article  Google Scholar 

  2. Arnold, M., et al.: Global burden of cutaneous melanoma in 2020 and projections to 2040. JAMA Dermatol. 158, 495–503 (2022). https://doi.org/10.1001/jamadermatol.2022.0160

    Article  Google Scholar 

  3. Sharma, P., Vuthaluru, S., Chowdhury, S., Are, C.: Global trends in the incidence and mortality of pancreatic cancer based on geographic location, socioeconomic status, and demographic shift. J. Surg. Oncol. 128, 989–1002 (2023). https://doi.org/10.1002/jso.27462

    Article  Google Scholar 

  4. Mazzocco, K., Masiero, M., Carriero, M.C., Pravettoni, G.: The role of emotions in cancer patients’ decision-making. Ecancermedicalscience 13, 914 (2019). https://doi.org/10.3332/ecancer.2019.914

    Article  Google Scholar 

  5. Kracen, A., Nelson, A., Michl, T., Rowold, M., Taylor, N., Raque, T.L.: Perspectives of postdoctoral fellows: a qualitative study of clinical supervision in psycho-oncology. Psychol. Serv. 20, 206–218 (2023). https://doi.org/10.1037/ser0000740

    Article  Google Scholar 

  6. Lovrić, L., Fischer, M., Röderer, N., Wünsch, A.: Evaluation of the cross-platform framework flutter using the example of a cancer counselling app. In: Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health, pp. 135–142. SCITEPRESS - Science and Technology Publications (2023). https://doi.org/10.5220/0011824500003476

  7. Brandão, T., Tavares, R., Schulz, M.S., Matos, P.M.: Measuring emotion regulation and emotional expression in breast cancer patients: a systematic review. Clin. Psychol. Rev. 43, 114–127 (2016). https://doi.org/10.1016/j.cpr.2015.10.002

    Article  Google Scholar 

  8. Subramanian, B., Kim, J., Maray, M., Paul, A.: Digital twin model: a real-time emotion recognition system for personalized healthcare. IEEE Access 10, 81155–81165 (2022). https://doi.org/10.1109/ACCESS.2022.3193941

    Article  Google Scholar 

  9. Chen, M., Zhou, P., Fortino, G.: Emotion communication system. IEEE Access 5, 326–337 (2017). https://doi.org/10.1109/ACCESS.2016.2641480

    Article  Google Scholar 

  10. Murray, I.R., Arnott, J.L.: Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion. J. Acoust. Soc. Am. 93, 1097–1108 (1993). https://doi.org/10.1121/1.405558

    Article  Google Scholar 

  11. Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in speech. In: Bunnell, H.T. (ed.) Proceedings / ICSLP 1996, Wyndham Franklin Plaza Hotel, Philadelphia, PA, USA, 3–6 October 1996, pp. 1970–1973. Citation Delaware, New Castle (1996). https://doi.org/10.1109/ICSLP.1996.608022

  12. Weninger, F., Wöllmer, M., Schuller, B.: Emotion recognition in naturalistic speech and language—a survey. In: Konar, A., Chakraborty, A. (eds.) Emotion Recognition. A Pattern Analysis Approach, pp. 237–267. Wiley, Hoboken (2015). https://doi.org/10.1002/9781118910566.ch10

  13. Konar, A., Chakraborty, A. (eds.): Emotion Recognition. A Pattern Analysis Approach. Wiley, Hoboken (2015). https://doi.org/10.1002/9781118910566

  14. Jain, M., et al.: Speech Emotion Recognition using Support Vector Machine (2020)

    Google Scholar 

  15. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45–77 (2007). https://doi.org/10.2753/MIS0742-1222240302

    Article  Google Scholar 

  16. Khan, K.S., Kunz, R., Kleijnen, J., Antes, G.: Five steps to conducting a systematic review. JRSM 96, 118–121 (2003). https://doi.org/10.1258/jrsm.96.3.118

    Article  Google Scholar 

  17. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26, xiii–xxiii (2002)

    Google Scholar 

  18. Brocke, J., Simons, A., Niehaves, B., Niehaves, B., Reimer, K.: Reconstructing the giant: on the importance of rigour in documenting the literature search process (2009)

    Google Scholar 

  19. Frehe, V., Adelmeyer, T., Teuteberg, F.: A balanced scorecard for systematic data quality management in the context of big data. Multikonferenz Wirtschaftsinformatik (2016). (in German)

    Google Scholar 

  20. Akinloye, F.O., Obe, O., Boyinbode, O.: Development of an affective-based e-healthcare system for autistic children. Sci. Afr. 9, e00514 (2020). https://doi.org/10.1016/j.sciaf.2020.e00514

    Article  Google Scholar 

  21. Bhangdia, Y., Bhansali, R., Chaudhari, N., Chandnani, D., Dhore, M.L.: Speech emotion recognition and sentiment analysis based therapist bot. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 96–101. IEEE (2021). https://doi.org/10.1109/ICIRCA51532.2021.9544671

  22. Chang, K., Fischer, D., Canny, J., Hartmann, B.: How's my mood and stress? An efficient speech analysis library for unobtrusive monitoring on mobile phones, pp. 71–77 (2011)

    Google Scholar 

  23. Chen, Z., et al.: A web-based longitudinal mental health monitoring system. In: Hammal, Z., Busso, C., Pelachaud, C., Oviatt, S., Salah, A.A., Zhao, G. (eds.) Companion Publication of the 2021 International Conference on Multimodal Interaction, pp. 121–125. ACM, New York (2021). https://doi.org/10.1145/3461615.3491113

  24. Eeswar, S.S., et al.: Better you: automated tool that evaluates mental health and provides guidance for university students. In: TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), pp. 1–6. IEEE (2022). https://doi.org/10.1109/TENCON55691.2022.9977977

  25. Egger, M., Ley, M., Hanke, S.: Emotion recognition from physiological signal analysis: a review. Electron. Notes Theoret. Comput. Sci. 343, 35–55 (2019). https://doi.org/10.1016/j.entcs.2019.04.009

    Article  Google Scholar 

  26. Elsayed, N., ElSayed, Z., Asadizanjani, N., Ozer, M., Abdelgawad, A., Bayoumi, M.: Speech emotion recognition using supervised deep recurrent system for mental health monitoring. In: 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), pp. 1–6. IEEE (2022). https://doi.org/10.1109/WF-IoT54382.2022.10152117

  27. Gong, Y., Poellabauer, C.: Continuous assessment of children’s emotional states using acoustic analysis. In: 2017 IEEE International Conference on Healthcare Informatics (ICHI), pp. 171–178. IEEE (2017). https://doi.org/10.1109/ICHI.2017.53

  28. Jiang, Y., Li, W., Hossain, M.S., Chen, M., Alelaiwi, A., Al-Hammadi, M.: A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition. Inf. Fusion 53, 209–221 (2020). https://doi.org/10.1016/j.inffus.2019.06.019

    Article  Google Scholar 

  29. Joshi, D., Dhok, A., Khandelwal, A., Kulkarni, S., Mangrulkar, S.: Real time emotion analysis (RTEA). In: 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV), pp. 1–5. IEEE (2021). https://doi.org/10.1109/AIMV53313.2021.9670908

  30. Kocaballi, A.B., et al.: Conversational agents for health and wellbeing. In: Bernhaupt, R., et al. (eds.) Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–8. ACM, New York (2020). https://doi.org/10.1145/3334480.3375154

  31. Liang, D., Zhang, A., Thomaz, E.: Automated face-to-face conversation detection on a commodity smartwatch with acoustic sensing. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 7, 1–29 (2023). https://doi.org/10.1145/3610882

  32. Liu, X., Zhang, L., Yadegar, J.: A multi-modal emotion recognition system for persistent and non-invasive personal health monitoring. In: Jacobs, I.M., Soon-Shiong, P., Topol, E., Toumazou, C. (eds.) Proceedings of the 2nd Conference on Wireless Health, pp. 1–2. ACM, New York (2011). https://doi.org/10.1145/2077546.2077577

  33. Marchi, E., Eyben, F., Hagerer, G.J., Schuller, B.: Real-time tracking of speakers’ emotions, states, and traits on mobile platforms. In: Proceedings of the Interspeech 2016, pp. 1182–1183 (2016)

    Google Scholar 

  34. Muaremi, A., Arnrich, B., Tröster, G.: Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3, 172–183 (2013). https://doi.org/10.1007/s12668-013-0089-2

    Article  Google Scholar 

  35. Porcheron, M., Arch, K.G., Luland, S.D., Blanchfield, P., Valstar, M.F., Chowanda, A.: Swiss Cottage – a game to train speech recognition for an affective computing treatment of ADHD patients, pp. 1–5 (2013)

    Google Scholar 

  36. Sheykholeslami, N.: Emotion AI in Mental Healthcare. How can affective computing enhance mental healthcare for young adults? pp. 1–36 (2022)

    Google Scholar 

  37. Söderberg, E.: An evaluation of the usage of affective computing in healthcare. In: UMEÅ’s 25th Student Conference in Computing Science, pp. 69–78

    Google Scholar 

  38. Tong, Y., Mo, W., Sun, Y.: Emovo: a real-time anger detector on the smartphone using acoustic signal. In: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 392–395. ACM, New York (2023). https://doi.org/10.1145/3594806.3594833

  39. Wahbeh, A., Al-Ramahi, M., El-Gayar, O., Elnoshokaty, A., Nasralah, T.: Conversational agents for mental health and well-being: discovering design recommendations using text mining. In: Proceedings of the 56th Hawaii International Conference on System Sciences, pp. 3184–3193

    Google Scholar 

  40. Yamashita, Y., Onodera, M., Shimoda, K., Tobe, Y.: Emotion-polarity visualizer on smartphone. In: 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pp. 96–99. IEEE (2019). https://doi.org/10.1109/REW.2019.00020

  41. Yang, J., Zhou, J., Tao, G., Alrashoud, M., Mutib, K.N.A., Al-Hammadi, M.: Wearable 3.0: from smart clothing to wearable affective robot. IEEE Netw. 33, 8–14 (2019). https://doi.org/10.1109/MNET.001.1900059

  42. Zygadlo, A.: A therapeutic dialogue agent for polish language. In: 2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 1–5. IEEE (2021). https://doi.org/10.1109/ACIIW52867.2021.9666281

  43. Bogner, A., Littig, B., Menz, W.: Interviewing Experts. Palgrave Macmillan, Houndmills (2009)

    Book  Google Scholar 

  44. Monke, S.: The Expert Interview as a Method of Qualitative Social Research. GRIN Verlag (2007)

    Google Scholar 

  45. Mayring, P.: Qualitative content analysis. Forum Qual. Soc. Res. 1(2), 1–10 (2000). Arzt. 20

    Google Scholar 

Download references

Acknowledgements

This study was funded by Federal Ministry of Education and Research in Germany.

Author information

Authors and Affiliations

  1. Heilbronn University of Applied Science, Heilbronn, Germany

    Leonard Georg Klotz & Mahsa Fischer

  2. University Medical Center Freiburg, Albert Ludwigs University, Freiburg, Germany

    Alexander Wünsch

  3. Bern University Hospital, University of Bern, Bern, Switzerland

    Alexander Wünsch

Authors
  1. Leonard Georg Klotz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Wünsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahsa Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonard Georg Klotz .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Hosei University, Tokyo, Japan

    Ayako Hashizume

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Klotz, L.G., Wünsch, A., Fischer, M. (2024). Evaluation of a Voice-Based Emotion Recognition Software in the Psycho-Oncological Care of Cancer Patients. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14684. Springer, Cham. https://doi.org/10.1007/978-3-031-60405-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-60405-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60404-1

  • Online ISBN: 978-3-031-60405-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation