A transparent, detailed account of the science InnerLight is built on, the technologies it uses, and why — written for researchers, clinicians, and reviewers. InnerLight itself is not yet validated in a controlled trial; this page documents the established principles behind its design and our commitment to testing it honestly.
A note on our posture: every design choice below draws on peer-reviewed work. That grounds our approach. It does not mean InnerLight is proven — validating the tool itself is precisely the research we are undertaking. We will report negative results as readily as positive ones.
InnerLight’s use of sound is built on the Iso-Principle from music therapy: meet a person’s current emotional state with matching music, then gradually shift the music toward calm to carry them with it. This is a long-standing clinical method with controlled experimental support.
Starcke K., Mayr J., von Georgi R. (2021). “Emotion modulation through music after sadness induction — the Iso principle in a controlled experimental study.” International Journal of Environmental Research and Public Health, 18(23).
Music with auditory beat stimulation RCT protocol (2025). BMJ Open, 15(6):e094784 — describes Iso-principle personalization against baseline Self-Assessment Manikin (SAM) scores.
Research indicates that music in the 60–80 beats-per-minute range supports relaxation by aligning neural oscillations (alpha-wave activity) with the musical rhythm, shifting arousal from tense toward calm. InnerLight prioritizes tracks and, in development, dynamic tempo shaping toward this range.
Xu R., Li J. (2025). “AI-driven music intervention based on five-tone theory for anxiety: a preliminary pre-post feasibility study.” Frontiers in Psychology, 16:1669029. (Real-time HRV-guided tempo modulation.)
Frontiers in Digital Health (2025), 7:1552396 — review of music therapy, entrainment, and AI-driven biofeedback.
The strongest current evidence favors adjusting musical parameters — tempo, volume, complexity — smoothly and in real time in response to physiological signals, rather than abruptly switching tracks. When tension rises, effective systems slow the tempo and simplify the music with soft transitions. This is the direction of InnerLight’s ongoing sound development. We are deliberately re-examining which signal should drive it: because beats-per-minute is an unreliable indicator of emotional state, we do not use heart rate as a distress signal, and are building a more trustworthy read of how a person is doing.
REMAST: Real-time Emotion-based Music Arrangement with Soft Transition (arXiv:2305.08029).
Williams et al. (2020); Jiao (2025) — adaptive functional music generation with real-time biofeedback, reviewed in Frontiers in Psychology (2026), 16:1741463.
InnerLight reads heart rate from a standard webcam using remote photoplethysmography: detecting the tiny color changes in facial skin as blood pulses beneath it. We combine forehead and cheek skin regions (avoiding the eyes and mouth, which introduce motion noise), verify skin pixels, detect the beat period by autocorrelation, and apply physiology-informed smoothing so implausible jumps are rejected. In low light, the signal is automatically brightened (adaptive gamma correction) before analysis so people in dim conditions are not excluded.
Method basis: chrominance- and plane-based rPPG (POS/CHROM family); forehead and cheek regions of interest shown to carry strong pulsatile signal in systematic reviews of rPPG ROI selection.
Low-light handling follows gamma-correction and histogram-based enhancement approaches evaluated for rPPG under poor illumination.
Why webcam rPPG, and not a wearable or a specific product: a crisis tool must work for anyone, instantly, with no device to buy, pair, or install. Wearables and clinical pulse oximeters are more accurate but exclude anyone who doesn’t own one in the moment. Deep-learning rPPG models are strong but require a server and heavy computation. Browser-based rPPG is the only approach that runs immediately for everyone on a phone or computer — so we use it, and we are transparent about its limits: it needs reasonable light and a mostly still face, and we label every reading by confidence (measured / estimated / baseline-held) rather than overstating precision.
For facial-expression signals InnerLight uses Google’s MediaPipe Face Landmarker, which measures dozens of specific facial-movement values (blendshapes) rather than guessing a single emotion label. We chose MediaPipe because it is free, runs entirely in the browser (no images ever leave the person’s device for this), is well-documented, and is widely used and maintained — important for a tool that must be reproducible by a research team.
InnerLight uses real photographs, not animation, as grounding scenes. Realism is used deliberately: concrete sensory grounding is a recognized technique for interrupting distress and dissociation and returning attention to the present.
Privacy is foundational, not an afterthought. Specifically:
The Axiom Harmony Protocol (AHP) is InnerLight’s encryption layer — the system that turns anything a person chooses to save into scrambled, unreadable data that only they can unlock. It is not a marketing name over weak protection; it is built on the same class of encryption used to protect banking and government data. Here is exactly how it works, first in plain terms and then in technical terms.
In plain terms: when you save your story, InnerLight takes your private return code and, through a deliberately slow mathematical process, turns it into a unique digital key. It then locks your words with that key so thoroughly that the stored result looks like random noise. Your code is the only thing that can produce that key again. We never keep your code, so we can never unlock your story — and neither can anyone who breaks into the server. If you lose the code, the data is gone for good. That is the trade-off of true privacy: the lock is real, and you hold the only key.
In technical terms, for reviewers: AHP encrypts each payload with AES-256-GCM — the Advanced Encryption Standard at 256 bits in Galois/Counter Mode, an authenticated cipher that protects both confidentiality (no one can read it) and integrity (tampering is detectable). The key is derived from the person’s return code using PBKDF2-HMAC-SHA256 with 390,000 iterations and a random salt — a deliberately slow key-stretching function that makes brute-force guessing enormously expensive. Every encryption uses a fresh random nonce, and the protocol version is bound in as authenticated associated data. The key is never written to disk; only the ciphertext, salt, and nonce are stored. This is a zero-knowledge design toward the operator: InnerLight holds encrypted bytes it cannot read.
Honest limit: AES-256-GCM with strong key derivation is robust modern cryptography, but it is not yet post-quantum. A documented future hardening path is to add a post-quantum key-exchange layer (for example, ML-KEM / Kyber) alongside the authenticated symmetric encryption. We state this openly rather than overstate the protection.
InnerLight records anonymous, aggregate research metrics designed around recognized digital-health frameworks: uptake, engagement, session duration, adherence, and completion, alongside expression shifts, sound responses, self-reported calm (a wordless Self-Assessment Manikin scale), and heart-rate trends measured against each person’s own baseline. Every heart reading carries a confidence tier so coverage is complete without overstating precision. We follow the scientific method explicitly: a falsifiable hypothesis, stated predictions, an instrument that gathers the data, and a commitment to replication and peer review.
InnerLight does not diagnose, prescribe, or practice medicine or law. It is a companion for the wait and a bridge to human help — never a replacement for it. If you are in immediate danger, call or text 988, or call 911.
Citations above reference published, peer-reviewed literature supporting the principles InnerLight applies. They do not constitute evidence that InnerLight itself is effective; that evaluation is ongoing. Full reference details are available on request.
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