Samuel Pröll - Personal homepage

Customizing a gesture recognition model with MediaPipe

MediaPipe1 is an amazing library of ready-to-use deep learning models for common tasks in various domains. My previous post highlights how you can use it to easily detect facial landmarks. There are many other solutions available to explore. In this post however, I want to take a look at another feature, the MediaPipe Model Maker. Model maker allows you to extend the functionality of some MediaPipe solutions by customizing models to your specific use case.

Facial landmark detection is still easy with MediaPipe (2023 update)

In 2023, MediaPipe has seen a major overhaul and now provides various new features in addition to a more versatile API. While code from my older post still works (as of writing - November 2023, mediapipe==0.10.7), I want to briefly take a look at the new API and recreate the rotating face using it. MediaPipe preview Note that as of November 2023, MediaPipe is still in preview and the API could change again in coming versions.

ECG R peak detection in Python: a comparison of libraries

For decades now, electrocardiography (ECG) has been a crucial tool in medicine. And with wearable ECG devices making their way into clinical settings, the amount of ECG data available will continue to increase1. Automatic detection of heart beats (R peaks, QRS complexes) is an important step in ECG analysis. In this post, we will compare some of the libraries you may come across when looking for a ready-to-use solution in Python.