Smart glasses super app. AI assistant, language learning, proactive agents, screen mirror, captions, and more. Write 1 app that runs on any pair of smart glases.

Python package for the processing and analysis of Inertial Measurement Unit Data

Smart glasses software framework. Write 1 app that runs on any pair of smart glasses.

A step-counting model based on self-supervised learning for wrist-worn accelerometer data.

R Shiny dashboard for the SiA wearable interface

A simple application to support authorization of wearable devices using OAuth using a webservice with REST Endpoints.

ESP32Stat_Rev_3_5_PIO

A Do-It-Yourself AI-based module for your personalized interstitial glucose 30-min prediction! Compatible with different OS through Dockerization. Your data, your personalized model, and the execution are performed locally, without sharing anything with anyone! This module has been validated with real CGM data collected from 29 people with T1D.

Python package for digital phenotyping data synchronization and acquisition

The Health Research From Home partnership, led by the University of Manchester, will transform the future of health research using smartphones and wearables.

A Python package for the validation of heart rate and heart rate variability in wearables

Code for paper "A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder".

avatars, wearables, and animation interop research

simple OS for LillyGO T-Watch V3

A PulseSensor library (for Arduino) that collects our most popular projects in one place.

📊 Websites, Resources, Devices, Wearables, Applications, and Platforms for Self Tracking

RADAR-base platform's common Java utilities library containing basic schemas, streaming features, testing bridges and utils.

A free and open-source 2FA app for smartwatches.

asleep: a sleep classifier for wearable sensor data using machine learning

Code for paper "Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study"