A test project to explore smartwatch data simulation and human activity recognition

This project presents an innovative approach to how static hand gestures can be classified with wrist-worn barometric pressure sensors.

Use python to extract and model your whoop data; you might just unlock a new level

This repository presents the implementation of our collaborative project between UPenn/Orthopaedics, UDel/Physical Therapy, and CMU/Engineering on exercise prediction.

Dashboard to display wearable data of Long COVID patients collected in the WATCH project.

[IJCAI 2024] Official TensorFlow implementation of "Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for Motor-Impaired Individuals via Latent Embedding Exploitation".

The W4H Integrated Toolkit Repository provides a unified platform for managing, analyzing, and visualizing wearable health data using a suite of open-source tools and frameworks.

Deep Learning models to fuse imu-based motion capture and first-person video data to improve the prediction of future knee and ankle joint kinematics, in complex real-world environments.

More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors

Insurance Recommendation System is to suggest best insurance to the users based on thier health. Every user is given a health score based on his/her vitals; of which data is measured by the the wearables data. Wearables data such as Cardiopulmonary and Vascular data and Glucose level. So basically suggesting the best insurance policy based on th…

Arm mounted computer, which includes detection for bio signatures, accleration, gyroscope(Orientaion to Gravity well), and uploads to master control server.

Python package for extraction, visualization, and analysis of physiological data collected through wearable sensors.

Attend and Discriminate: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors

[ECAI 2020] Tensorflow 2.x Implementation of "Human Activity Recognition from Wearable Sensor Data Using Self-Attention"