FLAAP: An open Human Activity Recognition (HAR) dataset for learning and finding the associated activity patterns

Published: 15 July 2022| Version 1 | DOI: 10.17632/bdng756rgw.1


The purpose of this research is to propose a dataset for Human Activity Recognition (HAR) utilizing smartphone sensors. The dataset consists of 10 activities that were completed throughout three trials by eight different people. After examining the results of each experiment, the dataset only contains one of the optimal activity patterns. For intelligent learning algorithms to detect and understand the related activity patterns, this time-series data collected from smartphone-embedded (accelerometer and gyroscope) sensors provided valuable information. With the growing need for context-aware apps relating to people depending on their activities such as sports, health care, surveillance, yoga, the gym, and many more. The creation of solutions for these kinds of problems depends on the availability of such data. To collect data while doing the human activities indicated in this article, we give a HAR dataset of measurements taken from smartphone sensors targeted at the subject's body. Millions of raw sensor activity data samples were continuously collected between February 1st and May 31st of 2022 at sampling speeds of 100 Hz. The performance evaluation of various machine and deep learning algorithms for the learning and identification of activity patterns might benefit from this dataset. The performance of the learning algorithms while using various data preprocessing methods, knowledge transfer to target domains, and other methods may be of special interest to the research community. Such learning algorithms may also be used with HAR data that has been gathered over a long period. This makes it possible to determine activity patterns for a certain period. After that, after a predetermined period, we can detect changes. For patients with dementia, the physically challenged, the elderly, and youngsters who care for others, among others, this can assist in recognizing abnormalities and delivering early therapies.


Steps to reproduce

The data was acquired using a smartphone (accelerometer and gyroscope) sensor, deployed on the center position of the subject’s body part. Eight subjects participated in the data acquisition process. At the experimental site, the subjects have instructed to perform the listed activities according to their willingness. We have not restricted or forced the subjects to perform all activities, as mentioned in the consent form, which are dependent on their choice, comfortability, and pleasure. During the data collection process, the experimental environment was hospitality with a medical health emergency, ambulance, primary aid kit, and medical practitioners. This experiment was performed in a located place. The subjects wore the smartphone carry belt at the center position of their body, horizontally. We have installed an android application on the smartphone to collect the raw sensory data and configured it at a sampling rate of 100Hz.


Banaras Hindu University


Activity Recognition, Machine Learning, Feature Extraction, Pattern Recognition Classification Process, Accelerometer, Sensor, Time Series, Deep Learning, Gyroscope