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

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

wearablecompute is an open source Python package containing over 50 data and domain-driven features that can be computed from wearables and mHealth sensor data.

A python DIgital Signal ProcEssing Library developed to standardize extraction of sensor-derived measures (SDMs) from wearables or smartphones data.

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

Uses LSTM-based autoencoders to detect abnormal resting heart rate during the coronavirus (SARS-CoV-2) infectious period using the wearables data.

A signal quality assessment pipeline and dashboard for ambulatory cardiovascular data

Python package for digital phenotyping data synchronization and acquisition

An activity classification model based on self-supervised learning for wrist-worn accelerometer data.

Official implementation of our paper "ENHANCING HEALTHCARE WITH EOG: A NOVEL APPROACH TO SLEEP STAGE CLASSIFICATION"

A smart dog vest connected to a cellular network, collecting biometrics, and performing YOLO object recognition

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.

wearablecompute is an open source Python package containing over 50 data and domain-driven features that can be computed from wearables and mHealth sensor data.

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"

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

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.

Code for NeurIPS2022 TS4H workshop paper "Inferring mood disorder symptoms from multivariate time-series sensory data"

Analysis of my fitbit wearable data

[SenSys'25] BioQ: Towards Context-Aware Multi-Device Collaboration with BIo-Cues

POLARstress- Lightweight Real-Time Stress Detection from Polar Verity Sense HR Data