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

Data

Çağatay Berke Erdaş¹  Hasan Oğul¹  Tunç Aşuroğlu¹  Koray Açıcı¹ 

[1] Department of Computer Engineering, Başkent University, Bağlıca Kampüsü, Fatih Sultan Mahallesi Eskişehir Yolu 18 Km, Ankara 06790, Turkey;

关键词: activity recognition;    person identification;    sensor data analysis;    dataset;    context-awareness;    wearable computing;   

DOI  :  10.3390/data3030024

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown