FLAAP: An Open Human Activity Recognition (HAR) Dataset for Learning and Finding the Associated Activity Patterns

• Published in: Procedia computer science Vol. 212; pp. 64 - 73
• Main Authors: Kumar, Prabhat, Suresh, S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2022
• Subjects: Activities of Daily Living (ADL); Associated Activity Patterns; Human Activity Recognition (HAR); Smartphone Sensors; Time Series Data; Activities of Daily Living (ADL); Smartphone Sensors; Human Activity Recognition (HAR); Time Series Data; Associated Activity Patterns
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.10.208

A significant quantity of research work has been completed to recognize human activities. The majority of the proposed learning algorithms have treated the activity data as the role of fuel in vehicles. The capacity of the learning algorithms to recognize the activity patterns can be improved by the adequate availability of activity data. In this paper, we introduced the FLAAP (Finding and Learning the Associated Activity Patterns) activity dataset and data acquired by using the smartphone (accelerometer and gyroscope) sensors placed at the waist of the subjects while performing the activities. This dataset contains the record of ten activities performed by eight distinct subjects. Between February 1st and May 31st, 2022, millions of raw sensor activity data samples were captured constantly at 100Hz sampling rates. The Human Activity Recognition (HAR) datasets, which keep a record of such activities and report associations in activity patterns (mostly used for recognizing the Activities of Daily Living (ADL)), were lacking. This paucity is addressed by the FLAAP dataset which can be useful in finding the associated patterns in ADL. The obtained experimental findings demonstrate that the learning algorithm Random Forest (RF), which was used, has recognized the activities with around 77.22% accuracy. The applied RF learning algorithm on the FLAAP dataset provides the research gap for the researchers in developing more delicate learning models for enhancing recognition rates. Furthermore, the research community could be particularly interested in examining the learning performance of algorithms while using various data pre-processing techniques, transferring knowledge to target domains, and other techniques.