Biorezonator Mini White Paper
1 Project Objective
The Biorezonator Mini is a portable, iPhone-HoloRezonator Mini-based biophotonic and vibrational
neuromodulation device. It is designed for targeted cellular activation, autonomic
nervous system (ANS) stabilization, and home-based, personalized biofeedback
therapy.
Primary Objectives:
• Alleviation of IBS-like symptoms through gut-brain axis modulation
• Support of autonomic balance (HRV enhancement)
• Promotion of cellular regeneration via photobiomodulation
• Real-time diagnostic feedback using embedded iPhone sensors
• Development of adaptive resonance profiles via AI-based modeling
2 Core Technological Components
• Near-Infrared Light Source (850 nm): Cellular photobiomodulation targeting
mitochondrial activation. Penetration depth optimized for cranial
and abdominal neuromodulation. Beam divergence angle calibrated to
maintain tissue safety (IEC 60825-1 compliant).
• Frequency Generator: Low-frequency modulation (0.5–50 Hz); customized
waveforms for neuromodulation. Capable of generating sine, square, and
trapezoidal waveforms for experimental resonance tuning. Firmware supports
sweep and burst modes for transient entrainment trials.
• Vibration Unit: Synchronized vibroacoustic stimulation via iPhone’s Taptic
Engine. Uses Apple Taptic Engine APIs to produce micro-pulses aligned
with heart rate or respiratory cycles. The phase-locking option enables cardiac
coherence training.
• Camera + HRV Analyzer: Detection of PLR, pulse wave, HRV patterns. Implements
open-source HRV algorithms (e.g., Kubios Lite core), and machine
vision detection for pupil segmentation.
• HTML-based Dashboard: Control panel for protocol selection and personalization.
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• Biofeedback Module: Adaptive modulation based on HRV/EMG input. Optional
support for Bluetooth-based EMG input. Integrated short-time Fourier
transform (STFT) and wavelet decomposition for signal preprocessing.
3 Adaptive Algorithm – Personalized Neuromodulation
Inputs:
• Pupil dynamics, HRV metrics, pulse rate variability, optional EMG
Outputs:
• Dynamic 850 nm light, vibration patterns, acoustic modulation
Architecture:
• JavaScript + WebAssembly AI model, runs locally on iOS
Safety:
• Frequency gating, auto-termination, real-time monitoring
4 Operating Protocol (Home-Based Use Case)
Step-by-step:
1. User opens dashboard
2. Device collects baseline biometrics
3. Personalized protocol generated
4. Light and vibration activated
5. Feedback loop fine-tunes during session
6. Session ends, report generated
5 Scientific Background and Rationale
Photobiomodulation (PBM) using near-infrared (NIR) light at 850 nm has been
shown to increase cytochrome c oxidase activity, leading to enhanced mitochondrial
respiration and ATP synthesis (Hamblin, 2017). This wavelength penetrates
up to 20–30 mm into tissue, reaching subcutaneous neuronal and vascular structures.
Recent studies also suggest that PBM may modulate the expression of reactive
oxygen species and influence anti-inflammatory pathways via NF-κB inhibition
(Zhang et al., 2023).
Low-frequency vibration (0.5–50 Hz) has demonstrated parasympathetic activation,
particularly via the auricular branch of the vagus nerve. These frequencies
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fall within the range of slow cortical potentials, which are associated with autonomic
regulation and affective state modulation (Kraus et al., 2013).
Pupil dynamics, measured through the camera system, act as a non-invasive
surrogate for autonomic tone. Changes in pupil light reflex (PLR) latency and
amplitude correlate with sympathetic and parasympathetic activity, and can be
used to infer stress reactivity and emotional load (Granholm et al., 1996; Mathôt,
2018).
The HRV analysis utilizes both time-domain and frequency-domain metrics (e.g.,
RMSSD, HF/LF ratio), calculated from photoplethysmographic (PPG) signals captured
by the iPhone’s camera and flashlight. Recent meta-analyses support the
validity of consumer-grade HRV measurements for clinical and behavioral monitoring
(Boudreaux et al., 2021; Teo et al., 2022).
AI-driven personalization is based on reinforcement learning principles, where
light/vibration parameters are continuously adjusted based on real-time biomarker
feedback. The embedded model is structured as a modular convolutional neural
network (CNN) optimized for low-latency processing on Apple’s Core ML infrastructure.
6 Potential Applications
• IBS symptom regulation
• Sleep optimization
• Stress and anxiety relief
• Inflammation modulation
• Biohacking and performance
• Post-COVID autonomic therapy
7 Experimental Roadmap & Validation Protocol
Pilot Study (N = 10):
• Participants: 10 IBS patients
• Design: 14-day home use
• Control: Relaxation audio group
• Endpoints: HRV, symptom VAS, EMG tone
• Biomarker Set: RMSSD, SDNN, HF Power (ms2), PLR amplitude (mm), EMG
RMS (μV)
• Secondary Measures: GAD-7 (general anxiety), PSQI (sleep quality), GSRS
(gastrointestinal symptom rating scale)
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• Data Storage: End-to-end encrypted data saved locally and optionally uploaded
to secure HIPAA-compliant cloud (EU server option available for
GDPR compliance)
• EEG Add-on Validation: Use of Muse S EEG headband for theta/beta ratio
changes during neuromodulation sessions
• Statistical Analysis: Repeated measures ANOVA, with Bonferroni correction
for multiple endpoints
Optional Add-ons: Portable EEG, 4-week follow-up
8 Future Development
• Binaural beats integration
• Cloud model tuning
• Android app (Q4 2025)
• CE/FDA Class II approval pathway
• AI Model Expansion: Federated learning framework for improving model
accuracy without compromising user privacy
• Signal Fusion Module: Combines multimodal signals (HRV, PLR, EMG, userreported
outcomes) into a composite physiological state index
• Developer API: Planned SDK for third-party integration (e.g., wellness apps,
therapist dashboards)
• Regulatory Path: Pre-submission filed under FDA’s Digital Health Center
of Excellence (Q3 2025). Pursuing CE mark under MDR Class IIa by 2026.
9 Closing Statement
Biorezonator Mini – Quantum Light Console: “Neurostimulation and biofeedback,
in your pocket.”
