InOut: Lightweight Transferable Multimodal Indoor-Outdoor Detection System with Smartphones

Location awareness in mobile devices, particularly the detection of indoor and outdoor transitions, empowers devices to ascertain their own or user's position and offer pertinent intelligent services accordingly. In this paper, we present InOut, a robust and realistic indoor-outdoor detection system characterized by high precision, low latency, and cross-device transferability. Regarding effectiveness, considering that a singular sensor signal is inadequate in providing comprehensive environmental information for detection, we employ a multimodal fusion approach. Concerning efficiency, we optimize the model by pruning non-essential features through the calculation of Shapley values for importance assessment. Furthermore, given the heterogeneity of data from different devices, we implement an unsupervised domain adaptation method that enables effective model transfer across devices with limited unlabeled target domain data. Experimental results demonstrate that our InOut system achieves over 96% accuracy on the test dataset, with detection latency consistently maintained to be within 3.1 seconds (including 3 seconds of interface latency and less than 0.1 seconds of inference latency). Moreover, utilizing unlabeled data from a disparate mobile phone model, amounting to one-sixth the size of the original dataset, we enhance the model's accuracy from 83% before transfer to over 91%.