Omkar2118/datasets huggingface dataset:
IM-SportingBehaviors

# IM-SportingBehaviors Dataset

## Dataset Overview
The **IM-SportingBehaviors** dataset, developed by researchers at Air University Pakistan, provides detailed motion data from participants engaged in various sports activities. This dataset captures human movement through **triaxial accelerometers** attached to multiple parts of the body, specifically the **knee, wrist, and below-neck** areas. The dataset includes motion data from **six sports**: cycling, badminton, skipping, basketball, football, and table tennis, with participants comprising both professional and amateur athletes aged between 20 and 30 years, weighing between 60 and 100 kilograms.

## Dataset Characteristics
- **Total Samples**: 62,500
- **Sports Activities**: Cycling, badminton, skipping, basketball, football, and table tennis
- **Data Imbalance**: The dataset is imbalanced, with the ‘Skipping’ class as the most represented (22.4%) and ‘Football’ as the least represented (12.3%).
- **Sensors Used**: Triaxial accelerometers providing **9-dimensional data** from three sensor placements; however, only **6-dimensional data** is utilized in analyses, corresponding to two accelerometers.
- **Data Distribution**:
  - Skipping: 22.4%
  - Cycling: 19%
  - Table Tennis: 16.8%
  - Badminton: 16%
  - Basketball: 13.4%
  - Football: 12.3%

## Motivation and Summary of Content
The **IM-SportingBehaviors** dataset aims to enhance the understanding of human mobility patterns and biomechanics across a range of sports. By collecting and analyzing motion data through wearable accelerometers, researchers can gain insights into activity-specific movement dynamics and variability in performance between professional and amateur athletes. This dataset's detailed sensor data captures essential aspects of motion and posture, which are crucial for applications in sports science, human-computer interaction, and motion analysis.

## Potential Use Cases
The IM-SportingBehaviors dataset is valuable for:
1. **Activity Recognition**: Developing and validating algorithms to classify sports activities based on sensor data.
2. **Performance Analysis**: Analyzing movement efficiency and style differences between professional and amateur athletes.
3. **Biomechanics Research**: Studying human movement patterns, energy expenditure, and motion efficiency in various sports.
4. **Wearable Device Optimization**: Improving the accuracy of wearable fitness trackers in recognizing and categorizing sports-specific movements.
5. **Injury Prevention**: Identifying movement patterns or deviations that could contribute to sports-related injuries.

This dataset provides a robust foundation for advancing both theoretical and applied research in fields that depend on accurate motion and activity classification.
CMACD
Full name: Chinese Multi-label Affective Computing Dataset

This study collected data from the major social media platform Weibo, screening 11,338 valid users from over 50,000 individuals with diverse MBTI personality labels and acquiring 566,900 posts along with the user MBTI personality tags. Using the EQN method, we compiled a multi-label Chinese affective computing dataset that integrates the same user's personality traits with six emotions and micro-emotions, each annotated with intensity levels. 
This dataset is designed to advance machine recognition of complex human emotions and provide data support for research in psychology, education, marketing, finance, and politics.

Introducing paper: A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users
In-house

A Point Cloud Dataset for place recognition provided by PointNetVLAD, please refer to the [URL](https://openaccess.thecvf.com/content_cvpr_2018/papers/Uy_PointNetVLAD_Deep_Point_CVPR_2018_paper.pdf)
FFHNet
Full name: FFHNet Dexterous Grasping Dataset

https://syncandshare.lrz.de/getlink/fi9EZb33KiSAJ5rLHAkhg7/ffhnet-data.zip

Introducing paper: FFHNet : Generating Multi-Fingered Robotic Grasps for Unknown Objects in Real-time
MovieNet-TeViS

MovieNet-TeViS is a synopsis-storyboard pair dataset to facilitate the Text synopsis to Video Storyboard (TeViS). In the TeViS task, we aim to retrieve an ordered sequence of images from large-scale movie database as video storyboard to visualize an input text synopsis.

Introducing paper: TeViS:Translating Text Synopses to Video Storyboards
CARWC
Full name: Consolidated and refined world cup dataset

Consolidates the world cup 2014 (WC14) and time-series world cup (TSWC) datasets and refines their homography annotations.

Introducing paper: Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration