Context - The integration of AI into music therapy enables the creation of personalized playlists, generation of new music, providing real-time biofeedback and even creation of responsive and adaptive clinical, home-based, and digital therapeutic musical environments. Integration process need to consider hardware and software aspects related to advanced sensing, efficient computing, robust security purposes.

Purpose - The authors perform a critical review of existing publications related to therapeutic use of music for the relaxation purposes by implementing biofeedback loops with consumer-grade biosensing wearables coupled with other devices. Also it is emphasised the requirement for BSc, MSc and PhD students in music therapy to learn about the potential offered by Edge AI in the design and development of emotion-responsive music generation and other therapeutic tools which contain personalised real-time feedback systems.

Approach - Integration process need to consider hardware and software aspects related to advanced sensing, efficient computing and robust security purposes.

Advanced Sensing - Smart wearable devices based on real-time contextual analysis aim to provide a more personalised user experience by analysing environmental data alongside personal biometrics. The precise data acquisition and analysis in real-time is based on the integration of novel multi-sensory fusion techniques that leverage Edge AI so the decision making process is fast and responsive.

Efficient Computing – sustainable AI of Things (AIoT) requires substantial electricity consumption leading to significant carbon emissions. Real-time feedback and improved gesture recognition accuracy are provided by integration of wearable sensors with advanced data processing techniques. Music therapy has an important role in the management of stress and anxiety because the enhanced vascularization of various mesocorticolimbic structures and activation of dopaminergic neurotransmitters regulate the autonomic system, emotion and cognitive function.

Robust Security –The scalable and ethically grounded AI-wearable integration could consider federated learning for privacy, deep learning for noise filtering in EEG data, Generative Adversarial Networks (GAN) for enhanced performance in emotion recognition accuracy, temporal smoothness, and perceptual coherence of emotion-aware music generation

Anticipated Outcomes - The paper aims to provide useful technical information to music therapists, researchers and undergraduate and postgraduate students studying music therapy courses on how edge AI can enhance traditional methods (i.e. lyric analysis, music-assisted relaxation, Guided Imagery and Music, music assisted relaxation etc) by offering new creative possibilities, increasing efficiency, and addressing challenges like personalization, cost, ethical implications of AI use in music therapy.

Conclusions - The latest developments in sensing, computing and security aspects related to the use of edge AI in music therapy enhance the capacity of therapists, students, researchers and other users to generate personalised music, perform real time emotion recognition using long term comfort and ergonomic wearable devices, develop intelligent assistive tools for improvisation, music composition and music theory. etc. This paper provides a foundation for understanding current state of the art for wearable devices used in music therapy, efficient computing solutions and robust security methods. Also it emphasises areas for future research and development related to technological aspects of the interdisciplinary field of music therapy.