Raw Biopotential Processing

Predictive Cardiac Analytics via Sub-Surface Electrophysiology.

Captures raw microvolt ECG streams using the BioAmp EXG Pill. Eliminates environmental noise via 50/60Hz digital notch filters and deploys Edge-ML feature extraction to catch early structural anomalies missing from standard optical wearables.

Predictive Fatigue Monitoring via Direct Neural Signatures.

Tracks real-time microvolt neuro-signals (Alpha, Beta, Theta) and EOG artifacts via frontal electrode arrays. Targeted at B2B enterprise safety to predict micro-sleep events and cognitive drop-offs before industrial accidents happen.

Next-Gen Cybernetics via Intent-Driven Decoding.

Decodes microvolt muscle activity using the Muscle BioAmp Patchy. Deploys ultra-low-latency ML pattern recognition to map contractions into multi-gesture commands, driving low-cost smart prosthetics with zero perceived lag.

ESP32_EXG_PILL TI_ADS1299_SPI_8CH ESP32_EMG_PATCHY
500 Hz Sampling 1 kHz 24-bit Sampling 1000 Hz Sampling
FATIGUE CRITICAL
Current BPM
72
Theta/Beta Ratio
1.2
Motor Unit Output
0.2 mV
Signal Quality
99.8% SNR
The PPG Disruption Matrix

Consumer Optical Estimation vs. Electrical Truth

Standard consumer wearables rely on Photoplethysmography (PPG), observing light reflection to guess at physiology. We interface directly with the body's electrical conduction network across the heart, brain, and muscle.

Cardiac Analytics

Consumer PPG (Apple/Garmin) Tracks pulse rate via blood volume. Useless during heavy motion. Cannot see P-waves or QRS complexes.
Divyaera Electrical Pipeline Measures the actual SA/AV node depolarization. Captures complex morphology changes to predict ischemic events and micro-arrhythmias instantly.

Neural Processing

Consumer Eye-Tracking Camera-based systems in vehicle cabins. Prone to lighting failures, glasses glare, and only detect fatigue after the driver's eyes close.
Divyaera Electrical Pipeline Measures localized voltage fluctuations in the prefrontal cortex. Detects Alpha/Theta surges indicative of deep fatigue *hours* before physical eye closure.

Cybernetic Interfaces

Motion Sensors (IMUs) Rely on physical acceleration. High latency, requires physical movement to trigger actions. Poor for prosthetics.
Divyaera Electrical Pipeline Intercepts the brain's motor action potentials at the muscle surface *before* physical movement occurs. Enables zero-latency prosthetic actuation.
Upside Down Labs
BioAmp Hardware Series
Texas Instruments
ADS1299 AFE (8-CH)
ESP32 / Pico Edge
ADC & WebSockets
SPI & WebSockets
AI Core Matrix
Inference Engine
Hardware Interoperability

Open-Source Hardware Alignment

We believe in democratizing telemetry. Our ingestion API natively supports the Upside Down Labs BioAmp EXG Pill out of the box, for predictive cardiac topologies.

We demand uncompromised resolution. Our neural ingestion API natively supports the Texas Instruments ADS1299, an industrial-grade 8-channel 24-bit analog front-end communicating via high-speed SPI for clinical EEG topologies.

We believe in democratizing telemetry. Our ingestion API natively supports the Upside Down Labs Muscle BioAmp Patchy out of the box, for high-fidelity muscular topologies.

  • Raw Signal Telemetry Stream unfiltered, 16-bit resolution biopotentials directly to our edge-compute SDK for zero-latency processing without data loss.
  • Analog Front-End (AFE) Tuning Configurable gain and bandpass filter mapping tailored for the specific physical characteristics of the BioAmp circuitry. Precise programmable gain amplifier (PGA) settings mapped directly to the internal registers of the ADS1299 over SPI.

Predictive Cardiac Telemetry

Cognitive Load & Fatigue Dashboard

Cybernetic Intent Decoder

Simulated live feed processing biopotentials and extracting AI insights.

WS://STREAM_ACTIVE

LEAD_II_AMPLIFIED (mV)

PREFRONTAL_EEG_RAW (μV)

ULNAR_EMG_ENVELOPE (mV)

Continuous Wavelet Transform Applied

RR Interval
833 ms
Blink Rate
18 /min
Latency
4.2 ms
> Init BioAmp stream... OK
> Init TI ADS1299 via SPI... 24-bit resolution confirmed. OK
> Baseline wander filter... Applied (0.5Hz High-pass)
> Notch filter... Applied (50/60Hz)
> Listening for data streams...

Event / Actuation Log

No events logged in current session.
Click "Simulate" above to trigger.

30-Day Arrhythmia Risk

Real-Time Cognitive Burnout

Motor Unit Efficiency

AI inference based on extracted morphology features.

12% Nominal
Key Contributing Factors:
  • HRV SDNN 65ms (Normal)
  • Ectopic Burden < 0.1%
  • QTc Interval 410ms
  • Theta Dominance Low
  • PERCLOS 4% (Alert)
  • Beta Attenuation Nominal
  • Signal-to-Noise Ratio 42 dB
  • Intent Confidence 98.5%
  • Actuation Latency 4.2 ms