Cardiotune
The Platform

The Enabling Layer

Cardiotune serves as a layer of tools between the physical fleet and the digital cloud. We turn every trip into a data stream without replacing your bikes.

The Pipeline Architecture Edge Computing

The Data Pipeline

From raw sensor stream to high-value indicators in three steps.

01

Source

Raw Sensor Stream. SmartGrips capture physiological (ECG/PPG), mechanical (IMU/Strain), and environmental signals directly from the handlebars.

Hardware Details →
02

Processing

High-Relevance Indicators (HRIs). Our AI Core uses Learn2Compress and AWS Greengrass for efficient on-device processing.

AI Core Specs →
03

Value

Forecasting & Decisions. Enriched data feeds into Urban DaaS marketplaces for revenue, operations, and city planning insights.

See Applications →
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System Topology

End-to-End Data Architecture

A seamless pipeline from physical sensor to digital insight.

IoT / Edge

Smart Hardware
Firmware & Sensors
Raw Signals
  • • Physiological (ECG)
  • • Mechanical (IMU)
  • • Environmental
Edge Processing
Denoising & Compression

Backend Cloud

AWS IoT Core
AI PROCESSING
Feature Eng.
→
Inference
Raw Analysis
→
Anomaly Det.
AI API Layer

Platform

REST Endpoints
Secure Data Delivery
Web Console
Fleet Management
Mobile SDK
Rider App Integration

Consumers

Smart Cities
Businesses
Riders
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Edge Advantage

Intelligence at the Handlebar

Cloud-only is too slow for safety and too expensive for scale. We moved the brain to the bike.

MOD: CMPRSS

Smart Compression

Intelligent signal decimation reduces cellular data overhead by 90% without losing signal fidelity.

Raw Data Optimized
SYS: AUTO

Edge Independence

Powered by AWS Greengrass. The fleet continues to analyze and buffer data even when 4G connectivity fails.

Status: Standby Uptime: 99.99%
LAT: LOW

Real-Time Response

Immediate anomaly detection on the device. Critical safety alerts are generated instantly, not post-processing.

LIVE
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