README.md

---

language:
  - en
tags:
  - sign language recognition
  - emergency response
  - computer vision
---


# CLARIS - Critical Emergency Sign Language Dataset

This dataset is a curated subset of the "Google - Isolated Sign Language Recognition" dataset, specifically filtered for the **CLARIS (Clear and Live Automated Response for Inclusive Safety)** project.

## Dataset Description

The primary goal of the CLARIS project is to develop a mobile application that provides a lifeline for the Deaf community during emergencies. This dataset was created to train a proof-of-concept AI model capable of recognizing a vocabulary of critical emergency-related signs.

The data consists of pre-extracted landmark coordinates from video clips of isolated signs. It originates from the [Google - Isolated Sign Language Recognition Kaggle Competition](https://www.kaggle.com/competitions/asl-signs).

## Dataset Structure

The dataset is provided in both CSV and Parquet (coming soon) formats. Each row represents the coordinates of a single landmark in a single frame of a video sequence.

| Column           | Dtype   | Description                                                                 |
| ---------------- | ------- | --------------------------------------------------------------------------- |
| `frame`          | int16   | The frame number within the sequence.                                       |
| `row_id`         | object  | A unique identifier for the landmark within the frame.                      |
| `type`           | object  | The type of landmark (`face`, `left_hand`, `right_hand`, `pose`).             |
| `landmark_index` | int16   | The index of the landmark within its type.                                  |
| `x`              | float64 | The normalized x-coordinate of the landmark.                                |
| `y`              | float64 | The normalized y-coordinate of the landmark.                                |
| `z`              | float64 | The normalized z-coordinate of the landmark (depth).                        |
| `path`           | object  | The path to the original source parquet file for the sequence.              |
| `participant_id` | int64   | A unique identifier for the participant (signer).                           |
| `sequence_id`    | int64   | A unique identifier for the sign sequence.                                  |
| `sign`           | object  | The ground truth label for the sign being performed.                        |

## Curation Process

To create a focused dataset for our specific use case, we performed a two-step curation process:

1.  **Vocabulary Filtering:** We selected **62 signs** deemed most relevant for describing medical, fire, or intruder emergencies.
2.  **Participant Filtering:** To create a manageable dataset for rapid prototyping, we constrained the data to sequences from **two distinct participants** who had a balanced distribution of the target signs.

This process resulted in a final dataset containing **1,719 unique sign sequences**, comprising over 37 million landmark rows.

## Usage

We recommend using the Parquet file for faster loading times.

```python

import pandas as pd



# Load the full curated dataset

df = pd.read_parquet('claris_curated_dataset.parquet')



# Or load the smaller, subsampled version

df_sample = pd.read_parquet('claris_subsample_dataset.parquet')



print(df.head())

```

## Link to Project Notebook

The complete methodology, including data preprocessing, model training, and analysis, can be found in our Kaggle notebook:
https://www.kaggle.com/code/eveelyn/datathon2025-med