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 | 96KB flash 22KB RAM | 72 KB flash 19KB RAM | >30% memory improvement |
Classification Latency | 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.