🦾Technical Architecture

iPulse Technical Architecture

iPulse Ecosystem Architecture

Data Collection Layer

• Integrates data sources from wearable devices (such as the PulseX smart bracelet), medical devices, mobile applications, etc., and uploads health data via Bluetooth, LoRa, Wi-Fi, and other protocols.

Data Storage & Management

• Uses decentralized storage (such as IPFS) to protect user data privacy while reducing the risk of single-point failures.

• Incorporates federated learning, differential privacy, and homomorphic encryption to ensure the anonymized storage and analysis of user data.

AI Large Model Training

• Trains models based on health data using Transformer architectures (e.g., BioBERT) for pre-training on massive datasets to enhance foundational understanding.

• Through federated learning, models are continually updated on the user side, ensuring data privacy while maintaining personalization and generalization.

• Employs time-series analysis models (e.g., RNN, LSTM) to predict user health trends, and integrates reinforcement learning to provide personalized treatment recommendations.

• Utilizes multi-task learning models to analyze the complex relationships between physiological data and disease characteristics.

Model Inference & Application

• Deploys models using ONNX Runtime, combining edge AI technologies for low-latency inference on devices.

• Integrates Generative Adversarial Networks (GAN) or diffusion models to simulate various treatment options and recommend the optimal choice for users.

Data Assetization & Ecosystem Collaboration

• Converts health data into tokens compliant with ERC-721 or ERC-1155 standards, managed through blockchain smart contracts for access control and revenue distribution.

• Offers open APIs and SDKs, enabling healthcare institutions and researchers to use anonymized data for new drug development, disease research, and DeSci collaboration.

Data Process