LSTM Based Scene Detection with Smartphones

With rapid adoption of smartphones, context detection is becoming increasingly important to enable new and sophisticated context-aware mobile apps and provide better communication services. In this paper, we propose an Long Short Term Memory (LSTM) based indoor/outdoor/underground detection system for smartphone scene detection with low energy consumption. The proposed system is first compared with other deep learning methods including fully connected network (FC), standard LSTM network and Gated Recurrent Unit (GRU) based models. and then with traditional machine learning methods including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR) and Random Forest (RF). Experimental results show that our proposed system is superiors in identifying indoor/outdoor/underground scene using only ultra-low power sensors. We collect real data at different periods and locations using multiple mobile devices. The required sensors are common in all types of smartphones, implying high compatibility and availability of the system.