Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone

Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated t...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2012-09, Vol.12 (9), p.12588-12605
Main Authors: Han, Manhyung, Vinh, La The, Lee, Young-Koo, Lee, Sungyoung
Format: Article
Language:English
Subjects:
accelerometer classification
Accelerometers
Accuracy
Acoustics
Algorithms
audio classification
Classification
context aware
context recognition
Devices
Feature selection
Human body
multimodal sensors
Recognition
Sensors
smartphone
Smartphones
Wireless communication
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Summary:Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware applications using smartphones, these applications are currently only able to recognize simple contexts using a single type of sensor. Therefore, in this work, we introduce a comprehensive approach for context aware applications that utilizes the multimodal sensors in smartphones. The proposed system is not only able to recognize different kinds of contexts with high accuracy, but it is also able to optimize the power consumption since power-hungry sensors can be activated or deactivated at appropriate times. Additionally, the system is able to recognize activities wherever the smartphone is on a human's body, even when the user is using the phone to make a phone call, manipulate applications, play games, or listen to music. Furthermore, we also present a novel feature selection algorithm for the accelerometer classification module. The proposed feature selection algorithm helps select good features and eliminates bad features, thereby improving the overall accuracy of the accelerometer classifier. Experimental results show that the proposed system can classify eight activities with an accuracy of 92.43%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s120912588