PPG wearable devices play an important role in sports, health monitoring and medical fields. These devices use optical technology to measure blood pressure and blood glucose, so their test accuracy directly affects the user's health assessment. 

With the increase in market demand, automated testing has become an important means to improve product quality and reduce production costs. This article will explore how to achieve automated testing of PPG wearable devices and introduce the programming languages supported by WhaleTeq to help engineers apply relevant technologies in practice.  

 

Part 1: Testing Requirements for PPG Wearable Devices

Overview of Test Items  

PPG devices are subject to a number of tests to ensure that their performance is up to standard. The main test items include:

  1. LED Test (DUT Tx Path Testing): Checks the brightness and stability of the Light Emitting Diode (LED).
  2. PD Test (DUT Rx Path Testing): Evaluates the performance of optical components (such as photodiodes), including sensitivity and response speed.
  3. Algorithm Testing: Check the accuracy of data processing algorithms to ensure the reliability of the final output results.

Figure 1: Testing Items for PPG wearable devices


Part 2: Implementation Methods of Automated Testing

Automated Test System Framework

An automated testing system usually consists of two parts: hardware and software:

  • Hardware components: including sensors, MCU, signal processing unit and interface, etc., used to collect data and execute control instructions.
  • Software components: Use a dedicated SDK (Software Development Kit) to write control programs to achieve data collection, signal processing and result analysis.

Testing with the AECG100 SDK

AECG100 is a multifunctional optical simulator that can output ECG and PPG signals synchronously. Its SDK provides a rich API interface, allowing developers to easily use various integrated functions. The following are the general steps for automated testing using the AECG100 SDK:

1.    Environment Setup

  • Make sure the AECG100 host and related accessories are properly installed and connected.
  • Install a suitable development environment, such as Python or C/C++.

2.    Write the Control Program

  • Use the functions in the SDK to set the parameters of LED and PD (photodiode).
  • For example, you can use WTQ Enable Sampling to enable the sampling function and use the Sampling Callback to extract the ADC counts.

3.    Execute Automated Tests

  • Start the automated testing program and begin collecting and analyzing data.
  • Analyze the results based on the data collected.
  • Generate reports to evaluate device performance.

Figure 2: AECG100 Automated Testing Framework

 

Support Multiple Platforms

AECG100 SDK supports multiple operating systems, including Mac, Windows, Linux, and Raspberry Pi. This allows developers to choose the appropriate platform for development according to their needs, improving the flexibility of the system.

 

Part 3: Automated Testing Process

DUT-LED Test (DUT Tx Path Testing)

In the DUT-LED test, it is necessary to compare the original data of the DUT-LED with the set value. The specific steps are as follows:

  1. Enable the PD-sampling function . 
  2. Collect the optical signal data emitted by the DUT-LED. 
  3. Compare the collected data with the preset values to evaluate whether the LED performance meets the standards.

DUT-PD Test (DUT Rx Path Testing)

The DUT-PD test is designed to check the performance of the photodiode. The steps include:

  1. Set the appropriate DC value and keep the AC value at 0 to maintain signal stability.
  2. Collect raw data of DUT-PD.
  3. Compare the DUT-PD data with the AECG-LED set value to analyze its sensitivity and response speed.

System Performance Test

System performance testing includes two aspects: repeatability and reproducibility:

  1. Repeatability Test: Testing the same unit multiple times at different SpO2 values to assess its stability.
  2. Reproducibility Test: Comparison of different units at the same SpO2 value to check the consistency of the device.

These steps help engineers confirm that the PPG wearable device functions properly under a variety of conditions.

 

Part 4: Programming Languages Supported by WhaleTeq

WhaleTeq SDK supports multiple programming languages, allowing developers to choose the most suitable tools based on their professional background. The main supported languages include: 

  • C/ C#/ C++: Suitable for applications that require high performance and low latency, especially when processing large amounts of data.
  • Python : It is widely popular because of its simplicity and ease of use, and is very suitable for rapid prototyping and data analysis.

 

Conclusion

Automated testing of PPG wearable devices is critical to improving product quality. Through the setting and operation of AECG100 SDK, the automated testing process can efficiently complete various performance evaluation tasks. 

In addition, WhaleTeq supports multiple programming languages, so developers can choose the most suitable tools according to their needs. In the future, we expect innovations to improve the reliability and functionality of wearable devices, so that they can continue to play an important role in the field of health monitoring.
 

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