Efficient and Accurate Indoor/Outdoor Detection with Deep Spiking Neural Networks

Sensor-rich smartphones have facilitated a lot of services and applications. Indoor/Outdoor (IO) status serves as a critical foundation for various upstream tasks, including seamless pedestrian navigation, power management, and activity recognition. Nevertheless, achieving robust, efficient, and accurate IO detection remains challenging due to environmental complexities and device heterogeneity. To tackle this challenge, some researchers have turned to deep learning for IO detection, which can deal with complex scenarios and achieve high detection accuracy. However, deep learning methods are often blamed for their expensive computational cost. Therefore, in this paper, we introduce a novel efficient IO detection method-DeepSIO, which can detect IO status accurately and efficiently. Specifically, different from existing IO detection methods, DeepSIO is developed based on spiking neural networks (SNN) that are more biologically plausible and computationally efficient than other deep neural networks. To better capture useful features, we propose to utilize dense connections between SNN layers. Extensive experiments are conducted in three typical scenarios, and experimental results demonstrate that DeepSIO outperforms state-of-the-art methods, achieving an accuracy of about 99.7%. Moreover, it has better generalization ability and can adapt well to new environments and devices.