Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer

被引：44

作者：

Asim, Yusra ^{[1
]}

Azam, Muhammad Awais ^{[1
]}

Ehatisham-ul-Haq, Muhammad ^{[1
]}

Naeem, Usman ^{[2
]}

Khalid, Asra ^{[3
]}

机构：

[1] Univ Engn & Technol Taxila, Dept Comp Engn, Taxila 47050, Pakistan

[2] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England

[3] COMSATS Univ Islamabad, Comp Sci Dept, Islamabad Campus, Islamabad 45550, Pakistan

来源：

IEEE SENSORS JOURNAL | 2020年 / 20卷 / 08期

关键词：

Accelerometers; Biomedical monitoring; Wearable sensors; Feature extraction; Sensor fusion; Support vector machines; Activity recognition; accelerometer; behavioral context; context-aware; smartphone; ubiquitous computing; DATA FUSION; MOBILE; CLASSIFICATION; FRAMEWORK; SYSTEM;

D O I：

10.1109/JSEN.2020.2964278

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.

引用

页码：4361 / 4371

页数：11