ICRS_LAB_HAR Dataset

Citation Author(s):
Vishwanath
Bijalwan
SR University Warangal, India
Submitted by:
Vishwanath Bijalwan
Last updated:
Thu, 06/27/2024 - 01:03
DOI:
10.21227/7de2-2243
Data Format:
*.csv
License:
Creative Commons Attribution
198 Views
Categories:
Signal Processing
Wearable Sensing
Biophysiological Signals
Keywords:
HAR, IMU sensor, Activities of Daily Living (ADL); Human Motion
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Abstract 

The Human Activity Recognition (HAR) dataset comprises comprehensive data collected from various human activities including walking, running, sitting, standing, and jumping. The dataset is designed to facilitate research in the field of activity recognition using machine learning and deep learning techniques. Each activity is captured through multiple sensors providing detailed temporal and spatial data points, enabling robust analysis and model training.

The dataset includes CSV files with annotated activity labels and sensor data, ensuring ease of use for researchers and practitioners. We provide the dataset to authors upon reasonable request to maintain data integrity and confidentiality. This dataset serves as a valuable resource for advancing the development of intelligent systems capable of recognizing and interpreting human activities in real-world scenarios.

Instructions: 

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 45 subjects of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data.The dataset, which consists of 3-D acceleration and angular velocities, is captured for 45 healthy subjects of different age and sex ranges for seven specific activities recorded with a sampling rate of 100 Hz.  

Data acquisition through IMU sensor model IMU BWT61CL and Wit motion mini IMU software, pre-processing (normalizing and scaling), filtering, labelling and analyses of raw data, shuffling of raw data and random splitting to test and train data

Comments

tanks

Submitted by nahid kheibari on Thu, 08/29/2024 - 10:46

hi sir,

i wanted to use this  data for my  machine learning project 

it would be great of you if you provide me with your datasets

Submitted by priyansh singh on Sat, 10/19/2024 - 10:08

Dataset Files

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