Next Article in Journal
Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research
Previous Article in Journal
A System for Acquisition, Processing and Visualization of Image Time Series from Multiple Camera Networks
Open AccessData Descriptor

HANDY: A Benchmark Dataset for Context-Awareness via Wrist-Worn Motion Sensors

1
Department of Computer Engineering, Başkent University, Bağlıca Kampüsü, Fatih Sultan Mahallesi Eskişehir Yolu 18 Km, Ankara 06790, Turkey
2
Faculty of Computer Science, Østfold University College, P.O. Box 700, 1757 Halden, Norway
*
Author to whom correspondence should be addressed.
Received: 23 May 2018 / Revised: 14 June 2018 / Accepted: 22 June 2018 / Published: 24 June 2018
Being aware of a personal context is a promising task for various applications, such as biometry, human-computer interactions, telemonitoring, remote care, mobile marketing and security. The task can be formally defined as the classification of a person being considered into one of predefined labels, which may correspond to his/her identity, gender, physical properties, the activity that he/she performs or any other attribute related to the environment being involved. Here, we offer a solution to the problem with a set of multiple motion sensors worn on the wrist. We first provide an annotated and publicly accessible benchmark set for context-awareness through wrist-worn sensors, namely, accelerometers, magnetometers and gyroscopes. Second, we present an evaluation of recent computational methods for two relevant tasks: activity recognition and person identification from hand movements. Finally, we show that fusion of two motion sensors (i.e., accelerometers and magnetometers), leads to higher accuracy for both tasks, compared with the individual use of each sensor type. View Full-Text
Keywords: activity recognition; person identification; sensor data analysis; dataset; context-awareness; wearable computing activity recognition; person identification; sensor data analysis; dataset; context-awareness; wearable computing
Show Figures

Figure 1

MDPI and ACS Style

Açıcı, K.; Erdaş, Ç.B.; Aşuroğlu, T.; Oğul, H. HANDY: A Benchmark Dataset for Context-Awareness via Wrist-Worn Motion Sensors. Data 2018, 3, 24. https://doi.org/10.3390/data3030024

AMA Style

Açıcı K, Erdaş ÇB, Aşuroğlu T, Oğul H. HANDY: A Benchmark Dataset for Context-Awareness via Wrist-Worn Motion Sensors. Data. 2018; 3(3):24. https://doi.org/10.3390/data3030024

Chicago/Turabian Style

Açıcı, Koray; Erdaş, Çağatay B.; Aşuroğlu, Tunç; Oğul, Hasan. 2018. "HANDY: A Benchmark Dataset for Context-Awareness via Wrist-Worn Motion Sensors" Data 3, no. 3: 24. https://doi.org/10.3390/data3030024

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop