LSMOE

✨ Update! ✨: txtools now has a dedicated webpage and a preprint in bioRxiv.

🔍 Overview

Personalized Hemodynamic Parameter Estimation Framework

Key Components:
- (a) Acquisition of PPG signals
- (b) Cloud-edge collaborative architecture for seamless hemodynamic parameter estimation
- (c) Real-world data acquisition scenarios
- (d) CNAP device implementation
- (e) APP interface for data transmission from E4 wearable
- (f) Comparative performance analysis of the proposed algorithm against baseline models for BP estimation

IoMT Framework for Hemodynamic Parameter Estimation

Framework Breakdown:
- (a) Real-time edge computing on a Linux-based embedded platform
- (b) PPG signal acquisition and the proposed multitask framework with multidimensional compression techniques
- (c) Multi-IoMT device integration, enabling synchronized diagnostics and instant feedback to healthcare professionals

🚀 Quick Example

🔧 Try it out now!
You can quickly get started with the code by visiting our GitHub repository. ✨

🌐 LSMOE integrates cutting-edge technologies to offer high-performance hemodynamic parameter estimation, tailored for real-world, multi-task healthcare environments. Stay ahead with real-time, personalized analysis through this innovative framework!