Explore @OxWearables Open Source projects

Oxford Wearables Group (@OxWearables)

OxWearables

Stars
274
Global Org. Rank 34,055 (Top 11 %)
Registered almost 9 years ago
Most used languages

Jupyter Notebook
45.0 %

Python
35.0 %

R
10.0 %

JavaScript
5.0 %

C#
5.0 %
Location 🇬🇧 United Kingdom
Country Total Rank 4,458
Country Ranking

Jupyter Notebook
159

R
624

Python
919

C#
5,156

JavaScript
9,745

biobankAccelerometerAnalysis

Extracting meaningful health information from large accelerometer datasets

ssl-wearables

Self-supervised learning for wearables using the UK-Biobank (>700,000 person-days)

Jupyter Notebook

actipy

Python Software Development Kit (SDK) to process Axivity, GENEActiv, and Actigraph files

Oxford_Wearables_Activity_Recognition

Notebooks for Oxford CDT Wearables Data Challenge

Jupyter Notebook

asleep

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

stepcount

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

Jupyter Notebook

ukb_download_and_prep_template

This repository facilitates common data pre-processing steps when working with UK Biobank data.

capture24

Capture24: Human activity recognition with activity trackers

Jupyter Notebook

rap_wearables

A demo/example pipeline for analysing wearables data using the RAP.

Jupyter Notebook

epicoda

Supports epidemiological analyses using compositional exposure variables

oxford-wearable-camera-browser

Browse and annotate wearable camera images in health studies

UKB_steps_mortality

This repository holds the statistical analysis code for the association of steps and mortality in the UK Biobank

Jupyter Notebook

RMLHDS

Jupyter Notebook

capture24_neurips

Capture24: Activity recognition on a large activity tracker dataset collected in the wild

Jupyter Notebook

wearable-camera-desktop-browser

Analysing wearable camera on a standalone PC

wearable-teaching

Jupyter Notebook

pyfew

A simple and light-weight feature extractor for wearable accelerometer data.

actinet

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

manuscript_ml_behaviours_cvd_2021

pd_walking

Detect walking in the PD population