metadata

annotations_creators: []
language: en
size_categories:
  - n<1K
task_categories:
  - video-classification
task_ids: []
pretty_name: 2025.01.16.10.33.04
tags:
  - fiftyone
  - video
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 335
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = load_from_hub("pjramg/GMNCSA24-FO")


  # Launch the App

  session = fo.launch_app(dataset)

  ```
license: mit

Dataset Card for 2025.01.16.10.33.04

This dataset is a modified version of the GMNCSA24 dataset, tailored for video classification tasks focusing on Activities of Daily Living (ADL) and fall detection in older populations. It is designed to support research in human activity recognition and safety monitoring. The dataset includes annotated video samples for various ADL and fall scenarios, making it ideal for training and evaluating machine learning models in healthcare and assistive technology applications.

This is a FiftyOne dataset with 335 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/GMNCSA24-FO")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

Original Dataset: GMNCSA24 Repo
Curated by: Paula Ramos
Language(s): en
License: [MIT License]

Dataset Sources [optional]

Repository: [https://github.com/ekramalam/GMDCSA24-A-Dataset-for-Human-Fall-Detection-in-Videos]
Paper [optional]: [E. Alam, A. Sufian, P. Dutta, M. Leo, I. A. Hameed "GMDCSA24: A Dataset for Human Fall Detection in Videos", Data in Brief (communicated)]
Blog [optional]: Journey with FiftyOn: Part III
Notebook: fiftyOne Example
Readme_DataPrepartation Awesome_FiftyOne

Uses

Elderly Action Recognition Challenge

Direct Use

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Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

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Annotations [optional]

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

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