KU-HAR: An open dataset for heterogeneous human activity recognition

In Artificial Intelligence, Human Activity Recognition (HAR) refers to the capability of machines to identify various activities performed by the users. The knowledge acquired from these recognition systems is integrated into many applications where the associated device uses it to identify actions or gestures and performs predefined tasks in response. HAR requires a large quantity of meticulously collected action data of diverse nature to fuel its learning algorithms. This paper aims to introduce a new set of HAR data collected from 90 participants who are 18 to 34 years old. The constructed dataset is named KU-HAR, and it contains 1945 raw activity samples that belong to 18 different classes. We used built-in smartphone sensors (accelerometer and gyroscope) to collect these HAR samples. Apart from the original (raw) timedomain samples, the dataset contains 20,750 subsamples (extracted from them) provided separately, each containing 3 seconds of data of the corresponding activity. Some classification results have been provided as well to propound the quality of the proposed dataset. The acquired results show that Random Forest (RF), an ensemble learning algorithm, can classify the subsamples with almost 90% accuracy. This dataset will enable smartphones and other smart devices to identify new activities and help researchers to design more delicate models based on practical HAR data. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a new HAR dataset named KU-HAR, consisting of 1945 raw activity samples and 20,750 subsamples for machine recognition of different activities. The research results show that the Random Forest (RF) algorithm can classify subsamples with almost 90% accuracy, aiding in the identification of new activities by smart devices and supporting research in designing more delicate models.