Abstract
Meditation is a mindful practice known for its difficulties, requiring focused attention despite distractions. Many people have traditionally relied on meditation apps with calming audio for support. This paper introduces an innovative AI-driven system aimed at improving the meditation experience, personalized to each user’s needs. This system consists of three core parts. First, it uses a language model to create meditation scripts that match user preferences. Second, it converts these scripts into audio, accompanied by selected background music to create a serene atmosphere. Lastly, a Compositional Pattern-Producing Network (CPPN) generates visually appealing videos featuring intricate patterns influenced by sentiment analysis and input audio. One of the system’s strengths is its adaptability since users can indicate their preferences among generation options and inform the system about their feelings and thoughts. An experiment, involving 14 participants, demonstrated comparable content and audio quality as traditional methods. Participants perceived the system as more personalized, expressing a preference for tailored meditation practices and indicating potential for increased user engagement. In summary, this AI-powered meditation system represents a significant advancement in the field, providing personalized, immersive experiences that integrate text, audio, and visuals. Its ability to adapt to users’ preferences holds promise for enhancing meditation outcomes and fostering inner peace.
References
Awasthi, B.: Issues and perspectives in meditation research: in search for a definition. Front. Psychol. 3, 613 (2013)
Behan, C.: The benefits of meditation and mindfulness practices during times of crisis such as COVID-19. Irish J. Psychol. Med. 4, 256–258 (2020)
Bohr, A., Memarzadeh, K.: The rise of artificial intelligence in healthcare applications. Artif. Intell. Healthcare 25–60 (2020). https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Bond, K., et al.: Defining a complex intervention: the development of demarcation criteria for “meditation’’. Psychol. Relig. Spiritual. 1(2), 129 (2009)
Clark, T., Barnes, P., Black, L., Stussman, B., Nahin, R.: Use of yoga, meditation, and chiropractors among U.S. adults aged 18 and over. NCHS Data Brief 325 (2018)
Cramer, H., et al.: Prevalence, patterns, and predictors of meditation use among us adults: a nationally representative survey. Sci. Rep. 6(1), 36760 (2016). https://doi.org/10.1038/srep36760
Durette, P.N.: Google text-to-speech (2023). https://gtts.readthedocs.io/en/latest/
Fox, K.C., et al.: Functional neuroanatomy of meditation: a review and meta-analysis of 78 functional neuroimaging investigations. Neurosci. Biobehav. Rev. 65, 208–228 (2016)
Ha, D.: Generating abstract patterns with tensorflow. blog.otoro.net (2016). https://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/
Hanley, A.W., Dehili, V., Krzanowski, D., Barou, D., Lecy, N., Garland, E.L.: Effects of video-guided group vs. solitary meditation on mindfulness and social connectivity: a pilot study. Clin. Soc. Work J. 50(3), 316–324 (2021). https://doi.org/10.1007/s10615-021-00812-0
Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text (2014). https://doi.org/10.1609/icwsm.v8i1.14550, https://www.semanticscholar.org/paper/bcdc102c04fb0e7d4652e8bcc7edd2983bb9576d
Institute, U.S.: Body scan meditation. https://www.uclahealth.org/programs/marc/free-guided-meditations/guided-meditations#english
Ku, B., Itagaki, T., Seaborn, K.: Dis/immersion in mindfulness meditation with a wandering voice assistant. In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. ACM (2023). https://doi.org/10.1145/3544549.3585627
Markus, N.: Using cppns to generate abstract visualizations from audio data. Published on the web at https://nenadmarkus.com/p/visualizing-audio-with-cppns/ (2018)
Matko, K., Sedlmeier, P.: What is meditation? proposing an empirically derived classification system. Front. Psychol. 10, 2276 (2019). https://doi.org/10.3389/fpsyg.2019.02276
McFee, B., et al.: librosa/librosa: 0.10.1 (2023)
Nash, J.D., Newberg, A.: Toward a unifying taxonomy and definition for meditation. Front. Psychol. 4, 806 (2013)
Álvarez Pérez, Y., et al.: Effectiveness of mantra-based meditation on mental health: a systematic review and meta-analysis (2022). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949812/#:~:text=Mantra%2Dbased%20meditation%20(MBM),or%20without%20religious%2Fspiritual%20content.
Ratzlaff, N.: Cppn (2013). https://github.com/neale/CPPN
Robert, J., Webbie, M., et al.: Pydub (2018). http://pydub.com/
Sharma, H., Clark, C.: London: Singing dragon; 2012. Ayurvedic Healing.[Google Scholar]
Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models (2023)
Wang, X., Mo, X., Fan, M., Lee, L.H., Shi, B.E., Hui, P.: Reducing stress and anxiety in the metaverse: a systematic review of meditation, mindfulness and virtual reality (2022)
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We would like to acknowledge the support of Templeton World Charity Foundation (TWCF) grant No. 0470. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the TWCF.
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Nguyen, P., Fdez, J., Witkowski, O. (2024). AI-Driven Meditation: Personalization for Inner Peace. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_19
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