Mobile Music Therapy Integrating AI-Driven Emotion Prediction and a Human-Computer Interaction Experience Model.
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| Title: | Mobile Music Therapy Integrating AI-Driven Emotion Prediction and a Human-Computer Interaction Experience Model. |
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| Authors: | Bai, Ruqi1 18603502125@163.com |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 3, p55-70. 16p. |
| Subjects: | Music therapy, Affective computing, Human-computer interaction, Emotion regulation, Multisensor data fusion, Deep learning, Medical care, Affective forecasting (Psychology) |
| Abstract: | The integration of affective computing with mobile health technologies has created transformative opportunities for scalable and personalized music therapy. However, traditional music therapy remains limited by insufficient personalization, subjective emotion assessment, and low adaptability in human--computer interaction (HCI). Existing artifi- cial intelligence (AI) emotion prediction methods also lack robust multimodal data fusion in mobile environments and alignment with clinical workflows. A closed-loop mobile music therapy system incorporating multimodal AI emotion prediction was developed in this study to address these limitations. The system integrates multimodal emotion sensing, dynamic therapy matching, empathic interaction, and closed-loop optimization. A convolutional neural network-long short-term memory (CNN-LSTM) hybrid model was employed to predict emotional states using physiological signals, behavioral features, and subjective feedback, while a prescriptive dynamic music therapy model and a multidimensional HCI evaluation framework were constructed. Experimental results showed that the multimodal CNN--LSTM model outperformed unimodal models and traditional algorithms, and the closed-loop system achieved significantly greater improvements in emotional regulation and stress reduction than non-adaptive interventions (traditional therapy approaches and static playlists). Dynamic enhancements in heart rate variability and reductions in cortisol levels provided objective physiological evidence of therapeutic efficacy. The empathic interaction framework increased usability and treatment adherence. Mediation analysis confirmed the pathway linking emotion prediction, music therapy matching, and outcome enhancement. Superior efficacy in individuals with anxiety or depressive tendencies further highlighted scenario-specific therapeutic responsiveness compared with postoperative rehabilitation groups. This study advances the integration of AI and music therapy, and the proposed real-time mapping model linking emotional states, music parameters, and interaction feedback provides an actionable framework for the development of empathic AI systems in healthcare. The closed-loop design establishes a new paradigm for personalized mobile medical interventions with significant clinical and interdisciplinary relevance. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191627703 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mobile Music Therapy Integrating AI-Driven Emotion Prediction and a Human-Computer Interaction Experience Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bai%2C+Ruqi%22">Bai, Ruqi</searchLink><relatesTo>1</relatesTo><i> 18603502125@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 3, p55-70. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Music+therapy%22">Music therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+computing%22">Affective computing</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+regulation%22">Emotion regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+forecasting+%28Psychology%29%22">Affective forecasting (Psychology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of affective computing with mobile health technologies has created transformative opportunities for scalable and personalized music therapy. However, traditional music therapy remains limited by insufficient personalization, subjective emotion assessment, and low adaptability in human--computer interaction (HCI). Existing artifi- cial intelligence (AI) emotion prediction methods also lack robust multimodal data fusion in mobile environments and alignment with clinical workflows. A closed-loop mobile music therapy system incorporating multimodal AI emotion prediction was developed in this study to address these limitations. The system integrates multimodal emotion sensing, dynamic therapy matching, empathic interaction, and closed-loop optimization. A convolutional neural network-long short-term memory (CNN-LSTM) hybrid model was employed to predict emotional states using physiological signals, behavioral features, and subjective feedback, while a prescriptive dynamic music therapy model and a multidimensional HCI evaluation framework were constructed. Experimental results showed that the multimodal CNN--LSTM model outperformed unimodal models and traditional algorithms, and the closed-loop system achieved significantly greater improvements in emotional regulation and stress reduction than non-adaptive interventions (traditional therapy approaches and static playlists). Dynamic enhancements in heart rate variability and reductions in cortisol levels provided objective physiological evidence of therapeutic efficacy. The empathic interaction framework increased usability and treatment adherence. Mediation analysis confirmed the pathway linking emotion prediction, music therapy matching, and outcome enhancement. Superior efficacy in individuals with anxiety or depressive tendencies further highlighted scenario-specific therapeutic responsiveness compared with postoperative rehabilitation groups. This study advances the integration of AI and music therapy, and the proposed real-time mapping model linking emotional states, music parameters, and interaction feedback provides an actionable framework for the development of empathic AI systems in healthcare. The closed-loop design establishes a new paradigm for personalized mobile medical interventions with significant clinical and interdisciplinary relevance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i03.60249 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 55 Subjects: – SubjectFull: Music therapy Type: general – SubjectFull: Affective computing Type: general – SubjectFull: Human-computer interaction Type: general – SubjectFull: Emotion regulation Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Medical care Type: general – SubjectFull: Affective forecasting (Psychology) Type: general Titles: – TitleFull: Mobile Music Therapy Integrating AI-Driven Emotion Prediction and a Human-Computer Interaction Experience Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bai, Ruqi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
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