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

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 a...

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Bibliographic Details
Published in:IEEE sensors journal 2020-04, Vol.20 (8), p.4361-4371
Main Authors: Asim, Yusra, Azam, Muhammad Awais, Ehatisham-ul-Haq, Muhammad, Naeem, Usman, Khalid, Asra
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Activity recognition
Behavior
behavioral context
Biomedical monitoring
context-aware
Environment models
Feature extraction
Human activity recognition
Human performance
Machine learning
Sensor fusion
smartphone
Smartphones
Support vector machines
ubiquitous computing
Wearable sensors
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Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2964278