kaihe-plus

pixel-level-contrastive-learning

Implementation of Pixel-level Contrastive Learning, proposed in the paper "Propagate Yourself", in Pytorch

ContrastiveLearningHAR

Contrastive Learning (SimCLR) for Human Activity Recognition

bsp-book

Time-Series-Library

A Library for Advanced Deep Time Series Models.

ml4ht_data_source

Multimodal data loader compatible with pytorch and tensorflow

ml4h

DeepSQA

DeepSQA repo for the paper "DeepSQA: Understanding Sensor Data via Question Answering"

wearable-infection

Real-time detection of infection diseases using wearables

PCovNet

hrv-covid19

COVID-19 and Wearables Open Data Research

lightly

A python library for self-supervised learning on images.

Kats

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

LLaVA-Med

Large Language-and-Vision Assistant for Biomedicine, built towards multimodal GPT-4 level capabilities.

time_series_augmentation

An example of time series augmentation methods with Keras

tsai

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

dl-4-tsc

Deep Learning for Time Series Classification

COMET

[Neurips 2023] A Hierarchical Contrastive Framework for Medical Time-Series

Contrastive-Learning-in-Medical-Time-Series-Survey

A Systematic Review: Self-Supervised Contrastive Learning for Medical Time Series

covid

UnsupervisedScalableRepresentationLearningTimeSeries

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Time-series-Metric-Learning

Metric Learning applied to a Multivariate Time Series Classification problem in Pytorch

pynhanes

Python parser and scraper for NHANES

ssl-wearables

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

Homekit2020

FluStudy

Step2heart

🏋️ Extract generalizable physiological representations from sensor time-series data with self-supervision (code for CHIL 2021, NeurIPS-W 2020 papers)

LAAD

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

WearableDetection

Alarm

AnomalyDetect

Detects anomalous resting heart rate from smartwatch data.