Edge AI-enabled ECG wearable with 10x lower latency
Extend the battery life of wearable medical devices by using edge AI with low-power sensors for immediate and precise detection of cardiac events
Application overview
?
Achieve real-time artificial intelligence classification with the on-device neural-network hardware accelerator to deliver immediate and precise detection of cardiac events.
Extend wear time with advanced low power features as well as high-resolution recording and transmission used only when necessary to enable extended wear time.
Develop a smaller and lighter weight wireless medical patch solution with wafer-chip-scale-packaging.
Starting evaluation
Data collection
Utilize the AFE159RP4 - Low-power, 4 channel ECG analog front-end with respiration and pace pulse detection to enable reliable accusation of ECG data to capture different classes. Data capture for ECG data is supported using the CCStudio? Edge AI Studio along with firmware support in MSPM0-SDK.
Build and train your model
With your dataset ready, explore, train, and evaluate models using the Edge AI Studio software development tool for edge AI. This intuitive GUI-based tool simplifies the entire process of model creation and deployment.
Get started quickly with the PIR motion detection example project, complete with a preloaded dataset.
Prefer custom data? The platform includes integrated data capture and hosting tools within? Edge AI Studio.
|
MSPM0 ECG performance (model dependent) |
(MSPM0G5187) Software AI implementation |
Hardware AI implementation | Improvement |
|---|---|---|---|
| Accuracy | >95% @ 4 classes | >95% @ 4 classes | similar |
| Memory | >30% memory improvement | ||
| 356ms | 37ms | >9x latency improvement | |
| Energy per class | 4322μJ | 360 μJ | >12x energy improvement |
Deploying your model
CCStudio? Edge AI Studio?gives a start to finish? workflow for deploying trained models to embedded targets. For developers seeking deeper customisation and control, refer to the deployment guides that offer a comprehensive framework for building and integrating edge AI functionality into your own embedded applications
- Deploy machine learning models on MSPM0 microcontrollers using EdgeAI Studio GUI Tools
- Deploy machine learning models on MSPM0 microcontrollers using CLI tools
All the hardware, software and resources you’ll need to get started
Hardware
LP-MSPM0G5187
MSPM0G5187 LaunchPad? development kit evaluation module,?This is needed for the EdgeAI inferencing, current data is captured using ADC and passed to the AI model after feature extraction.
TIDA-010288
Small, wireless 6-lead ECG Holter monitor reference design with respiration, pace, and temperature
Software & development tools
CCStudio? Edge AI Studio
A fully integrated no-code solution for training and compiling PIR Motion detection models, to deploy onto TI embedded microcontroller devices.?
CLI tools
Use this end-to-end model development tool that contains dataset handling, model training and compilation?
MSPM0-SDK
The MSPM0 SDK provides the ultimate collection of software, tools and documentation to accelerate the development of applications for the MSPM0 MCU platform?under a single software package.?