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

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.