Public scene recognition using mobile phone sensors

Abstract

Smartphones evolve rapidly and become more powerful in computing capabilities. More importantly, they are becoming smarter as more sensors such as the accelerometer, gyroscope, compass and the camera have been embedded on the digital board. In this paper, we propose a novel framework to recognize public scenes based on the sensors embedded in mobile phones. We build individual models for audio, light, wifi and bluetooth first, then integrate these sub-models using dynamically-weighted majority voting. We consider two factors when deciding the voting weight. One factor is the recognition rate of each sub-model and the other factor is recognition precision of the sub-model in specific scenes. We build the data-collecting app on the Android phone and implement the recognition algorithm on a Linux server. Evaluation of the data collected in the bar, cafe, elevator, library, subway station and the office shows that the ensemble recognition model is more accurate and robust than each individual sub-models. We achieved 83.33% (13.33% higher than audio sub-model) recognition accuracy when we evaluated the ensemble model with test dataset.