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

•Describes the content of the KU-HAR dataset, and the associated data collection procedure in details.•Provides a comparison with other contemporary HAR datasets.•Presents some HAR classification results involving the KU-HAR dataset. In Artificial Intelligence, Human Activity Recognition (HAR) refer...

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Bibliographic Details
Published in:Pattern recognition letters 2021-06, Vol.146, p.46-54
Main Authors: Sikder, Niloy, Nahid, Abdullah-Al
Format: Article
Language:English
Subjects:
Accelerometers
Algorithms
Artificial intelligence
Datasets
Electronic devices
Human activity recognition
Knowledge acquisition
Learning algorithms
Machine learning
Moving object recognition
Smartphones
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Summary:•Describes the content of the KU-HAR dataset, and the associated data collection procedure in details.•Provides a comparison with other contemporary HAR datasets.•Presents some HAR classification results involving the KU-HAR dataset. 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) time-domain 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.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2021.02.024