Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone

TLDR

Smartphones now possess powerful processors and multiple built‑in sensors, yet most context‑aware apps rely on only a single sensor type. This study proposes a comprehensive context‑aware system that exploits all smartphone sensors and introduces a novel feature‑selection algorithm for the accelerometer module. The system activates or deactivates power‑hungry sensors as needed, applies the feature‑selection algorithm to improve accelerometer classification, and integrates data from all sensors to recognize activities. Experimental evaluation shows the system classifies eight activities with 92.43 % accuracy, works while the phone is on the body during calls or app use, and reduces power consumption by selectively enabling sensors.

Abstract

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%.