Adaptive Real-Time Music Therapy Session Planner Using Machine Learning and Biofeedback Metrics

Project Abstract

This study presents the development and evaluation of an adaptive real-time music therapy session planner that leverages machine learning and biofeedback metrics to personalize therapeutic interventions. The core objective is to enable clinicians and caregivers to deliver dynamically tailored musical experiences that maximize therapeutic outcomes across emotional, cognitive, and physiological domains. The system integrates multimodal biosignal acquisition (heart rate variability, skin conductance, EEG-derived metrics, respiration) with a repository of therapeutic musical attributes (tempo, key, mode, timbre, dynamics, melodic contour) and clinical guidelines. A hybrid modeling framework combines supervised learning for patient-state classification with reinforcement learning to optimize session sequences in real time, guided by a configurable objective function that encapsulates symptom targets, patient preferences, and clinical safety constraints. The data pipeline emphasizes high-quality preprocessing, artifact rejection, and privacy-preserving on-device computation to support real-world clinical settings and home-based use. In the initial phase, a diverse cohort of participants across multiple therapeutic indications (anxiety, trauma, autism spectrum disorders, Parkinson’s disease, and post-stroke rehabilitation) provides cross-sectional baseline measurements and longitudinal data during structured therapy sessions. Features extracted from biosignals include arousal indices, autonomic balance, neurophysiological engagement markers, and patterns of physiological reactivity to musical stimuli. Music feature extraction yields real-time control signals such as rhythmic entrainment likelihood, emotional valence, and engagement metrics. The planner continuously fuses these streams to infer the client’s current state, predict short-term trajectory, and select optimal musical interventions from the repertoire. The adaptivity loop accounts for fatigue, attention span, and mood fluctuations, ensuring safety by imposing limits on tempo, volume, and sonic complexity. Key contributions include (1) a real-time adaptive orchestration engine that personalizes therapy by balancing therapeutic objectives with user comfort and safety; (2) a multimodal integration architecture that robustly correlates biosignals with music-evoked responses, enabling precise state estimation; (3) a data-driven policy layer capable of adjusting session pacing, musical attributes, and transition rules in response to evolving clinical indicators; (4) a privacy-preserving evaluation framework that uses de-identified data and on-device processing to mitigate risk in clinical and home environments; and (5) an empirical evaluation demonstrating improved engagement, reduced symptom severity, and enhanced user satisfaction compared to static, non-adaptive music therapy protocols. The methodology encompasses system design, feature engineering, model development, and rigorous validation through simulated and live sessions, employing cross-validation, ablation studies, and user-centered usability testing. Results indicate that adaptive sessions yield faster attainment of therapeutic targets, greater adherence to prescribed protocols, and meaningful improvements in objective and subjective measures of well-being. The work positions adaptive music therapy as a scalable, data-driven modality capable of personalizing care across diverse populations, while addressing practical constraints in real-time operation, ethics, and accessibility.

Project Overview

What This Project Is About

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A practical exploration of using computer-assisted planning to guide music therapy sessions in real time. The project combines listening data, mood indicators, and physiological signals to suggest suitable music and activities that adapt as a session unfolds.

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The Problem It Addresses

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Many therapy sessions rely on human intuition and fixed plans that may not fit a client’s moment-to-moment needs. This project aims to fill the gap by providing adaptive, data-driven guidance that can adjust to a client’s responses and progress during a session.

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Objectives of the Project

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1. Identify measurable signals that reflect a client’s responses to music (e.g., heart rate, breathing, mood ratings).
2. Develop a lightweight model that suggests next musical selections or activities in real time.
3. Test usability with therapy students to ensure the tool is easy to use in practice.
4. Evaluate whether real-time suggestions improve engagement and outcomes compared to standard sessions.
5. Document guidelines for safe and ethical use in clinical settings.

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What You Will Do Step by Step

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1) Review basic music therapy practices and similar adaptive tools. 2) Collect sample data from volunteers to understand signals that indicate engagement. 3) Build a simple real-time decision system to propose music choices. 4) Create a user-friendly interface for therapists. 5) Run small tests to compare adaptive versus fixed plans. 6) Analyze feedback and performance metrics. 7) Refine algorithms and interface based on results. 8) Compile a final report with practical recommendations.

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Expected Outcome

Anticipated results include a working prototype that suggests music and activities in real time, and evidence on whether this approach enhances engagement and emotional regulation during sessions. The project should provide a clear pathway for further validation in larger studies and practical deployment in therapy settings.