MindKestra

Problem

Anxiety and stress are pervasive, and adherence to cognitive exercises is often low. Static audio and meditation content is one-size-fits-all and tends to lose its effect. What people need is low-friction, engaging training that adapts to each person's state.

Solution

MindKestra mirrors the user's autonomic state with adaptive generative music. It closes the loop between body signals and sound to train self-regulation: real-time autonomic biomarkers become music parameters, and continuous feedback becomes gentle guidance.

It is designed for supervised clinical adjuncts and for wellness contexts.

1. Sensing and featuresA wearable captures heart rate, heart-rate variability, electrodermal activity, respiration, skin temperature and movement. Preprocessing and feature extraction feed an affect mapping of valence and arousal.
2. Mapping and compositionCross-ontology mapping translates biometric features into composition parameters: mode, tempo, density, harmony and timbre.
3. Real-time sonificationThe music is rendered in real time, the user entrains to it, and the loop updates continuously.

Data flow: HR, HRV, EDA, RR, temperature and motion become features, features become music, music becomes audio, and the listener's response feeds back into the signals.

The demo above uses the same idea with a visual intermediary: physiology sets the colour of a landscape, and the landscape sets the spectrum of the music.

Digital biomarkers

HRV, EDA, respiration rate and temperature correlate with arousal and stress, and give real-time physiological insight.

Neurofeedback learning

Real-time feedback supports emotion-regulation practice and keeps users engaged.

Music as a channel

Music is continuous, affectively rich and can be coupled to breath, with a low cognitive load for the person using it.

Adaptive rather than static

A personalised, evolving parameter set offers a more dynamic experience than a fixed playlist.

Cross-ontology mapping

The mapping encodes relationships between psychophysiological features and musical semantics, which keeps behaviour interpretable and tunable by a clinician.

• Closed-loop music feedback with minimal cognitive load.
• Personalisation through feature-to-music mapping and learned responses.
• Ethics first: consent-centric, private by design and clinician-supervised where needed.
• Literature synthesis across more than 580 papers on physiological indicators of mental health.
• Python pipelines for preprocessing, artefact removal and feature extraction.
• Wearable evaluation: low-latency BLE links and signal-quality checks in everyday conditions.
• Iterative prototype testing, which has strengthened the sense of the music as a mirror.

Validation plan and timeline

Feasibility and pilot (now). Acceptability, usability and signal quality, with a focus on near-term anxiety reduction measured before and after sessions.

Randomised trials (within 12 months, with your support). Closed-loop feedback compared with open-loop static audio and with sham feedback. Key outcomes are GAD-7, PHQ-8 and physiological secondaries such as HRV indices and EDA.

Durability and generalisation. Follow-ups at one to three months to assess transfer into daily life and to identify what drives adherence, such as playfulness and coaching.

Safety and guardrails

• Clear distress thresholds and escalation protocols, with options for clinician oversight.
• Content safeguards, inclusive design and accessibility features.
• Rigorous adverse-event reporting during trials and continuous risk review.

Ethics and compliance

• Privacy by default: data minimisation and local processing wherever feasible.
• Consent by design: granular control over sharing with therapists and for research.
• Transparency: full access to one's data, export and deletion, and audit logs for clinical programmes.

Regulatory pathway: compliance in 20 months

Medical claims will follow the Software as a Medical Device pathway, with a Quality Management System aligned for every clinical release. Wellness deployments keep strict boundaries and clear disclaimers so that they stay distinct from medical claims.

Technology and data compliance designed by ALT (Assistive Law Technology), Società Benefit e Startup Innovativa.

The white paper details the technical and strategic foundations of the work. A public summary covers:

• The executive overview: rationale, pipeline, intended outcomes and safeguards.
• Methods: indicators, processing, devices and prototyping.
• Architecture: modules, data flow and a security and privacy outline.
• Validation: from feasibility to randomised trial, durability and safety monitoring.

Request the full PDF

The current draft is gated and shared on request.

• Andrea Marinelli, CEO and founder
• Alessandro Tibo, CSO and founder
• Lorenzo Brusci, COO and founder
• Stefano Imperiale, CTO and founder
• Wojtek Friediger, CMO and founder

Advisors and partners are available on request.

The interactive demo on this page is powered by MUSI-CO, our generative multimodality engine.

Pilot sites. Research partners, clinics and wellbeing programmes.

Integration. Modular APIs for wearables and for EHR and digital-therapeutic adjacencies.

Data governance. GDPR-ready, with role-based access for supervised programmes.

Definitions

ANS

Autonomic nervous system; regulates arousal through sympathetic and parasympathetic balance.

HRV

Heart-rate variability; time- and frequency-domain measures linked to stress.

EDA

Electrodermal activity; a proxy of sympathetic arousal.

Closed loop

A real-time system that adapts its output to the user's current state.

Spectral centroid

The amplitude-weighted mean frequency of a sound; a common correlate of perceived brightness.

Disclaimers

• MindKestra is investigational; it is not intended to diagnose or to replace clinical care.
• Clinical claims are contingent on regulatory clearance and validated outcomes.
• Users keep control of their data, subject to consent and applicable law.

Music biofeedback: key resources

• Schneck, D. J., & Berger, D. S. (2006). The Music Effect: Music Physiology and Clinical Applications. Jessica Kingsley Publishers.
• Lundqvist, L. O., Carlsson, F., Hilmersson, P., & Juslin, P. N. (2009). Emotional responses to music: Experience, expression, and physiology. Psychology of Music, 37(1), 61–90.
• Pelletier, C. L. (2004). The effect of music on decreasing arousal due to stress: A meta-analysis. Journal of Music Therapy, 41(3), 192–214.
• Thaut, M. H. (2005). Rhythm, Music, and the Brain: Scientific Foundations and Clinical Applications. Routledge.
• McKinney, C. H., Tims, F. C., Kumar, A. M., & Kumar, M. (1997). The effect of selected classical music and spontaneous imagery on plasma β-endorphin. Journal of Behavioral Medicine, 20(1), 85–99.
• Juslin, P. N., & Sloboda, J. A. (Eds.). (2010). Handbook of Music and Emotion: Theory, Research, Applications. Oxford University Press.
• Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3(8), 655–666.
• Kramer, G., Walker, B., Bonebright, T., Cook, P., Flowers, J., Miner, N., & Neuhoff, J. (1999). Sonification Report: Status of the Field and Research Agenda. International Community for Auditory Display.