A practical python toolkit to visualize facial expression data — Introducing Py-Feat Py-feat is a free, open-source, and easy-to-use tool for working with facial expressions data, which provides a toolkit to easily detect facial expressions from images and videos, preprocess & analyze facial expression data, and visualize facial expression data. You can learn more about Py-feat by exploring the links below:

Using Pycaret, OpenFace, Pandas and other python magic — Background Pain is a unique experience resulting from a mix of sensory, cognitive, social, and emotional factors that vary between people. The combination of factors makes it difficult to assess a person’s experience with confidence. Currently, the most common and respected way to assess pain is through verbal reports (e.g., rating…

Implementing the ptitprince library with python to create a robust data visualization — What is a raincloud plot A raincloud plot is an intuitive, robust, and transparent form of data visualization. It is essentially a violin + boxplot + jittered dataset combo, which nicely provides an overview of raw data, probability distribution, and statistical inference at a glance via medians and confidence intervals. This can provide users with…

Bridging the gap between clinic and comfort of home Expanding the Scope of Home Exercise Programs in Physical Therapy Section 1: Deep Dive into the Evolution and Significance of HEPs The evolution of Home Exercise Programs (HEPs) in physical therapy is a reflection of the broader changes in healthcare towards more patient-centric and holistic approaches. Originally simple lists of exercises…

A Comprehensive Guide for Clinicians to Seamlessly Integrate and Leverage Motion Analytics in Therapy In the rapidly evolving landscape of healthcare, embracing innovative technologies is key to providing top-tier patient care. Motion tracking in clinical settings is emerging as a game-changer, offering detailed analytics that transform rehabilitation practices. …

Learn how to extract meaningful features from time series data using Pandas and Python, including moving averages, autocorrelation, and Fourier transforms. Introduction Time series analysis is a powerful tool for understanding and predicting trends in various industries such as finance, economics, healthcare, and many more. Feature extraction is a crucial step…

How Wearable Devices Can Help Individuals with Disabilities Monitor Their Physical Activity, Vital Signs, and Medication Intake, and What New Developments are on the Horizon Introduction Wearable technology has revolutionized the way we monitor our health, fitness, and daily activities. Wearable devices like smartwatches, fitness trackers, and health monitors have become…

Step by step guide to reconstruct a data visualization with python and plotly Introduction The aim of this article is to identify an interesting data visualization and learn how to reconstruct it as an interactive plot with python. As a researcher in wearable technology, I find the health and behaviroal trends…

An effective approach to visualize multidimensional data — Background Andrews curves, also known as Andrews plot, are a visualization technique used to identify underlying structure in multidimensional data. Similar to principal component analysis and T-distributed stochastic neighbor embedding, Andrews curves reduce multiple features into low dimensional space to display relationships with target variables or classifications. Andrews curves work by…