ermshaua/mobile-sensing-human-activity-data-set

This is the supporting website for the paper "MOSAD – a new data set for mobile sensing of human activities".

32
/ 100
Emerging

This project provides a comprehensive benchmark dataset, MOSAD, and analysis tools for developing Human Activity Recognition (HAR) systems. It takes raw motion data from smartphone sensors as input and evaluates how well different algorithms can automatically segment these recordings into distinct activities. Researchers and data scientists building HAR solutions can use this to train and compare their models.

No commits in the last 6 months.

Use this if you are a researcher or data scientist developing and evaluating machine learning models to automatically detect human activities from wearable sensor data.

Not ideal if you are looking for a plug-and-play library for immediate deployment of an activity recognition system, as this is a research-focused dataset and benchmark.

human-activity-recognition wearable-technology machine-learning-research time-series-analysis mobile-sensing
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 11 / 25

How are scores calculated?

Stars

12

Forks

2

Language

Jupyter Notebook

License

Last pushed

Mar 24, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ermshaua/mobile-sensing-human-activity-data-set"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.