An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (14), p.4773
• Main Authors: Kosasih, David Ishak, Lee, Byung-Gook, Lim, Hyotaek, Atiquzzaman, Mohammed
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Cellular telephones; Cloud computing; Communication; convolutional autoencoder; Geographical locations; Global positioning systems; GPS; low-power consumption sensor; mobile application; Performance evaluation; Power management; Sensors; Smartphones; spatial co-location detection; Spatial data; Unsupervised learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21144773

Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused on analyzing mobile GPS data to accomplish this task. While this approach may guarantee high accuracy from the perspective of the data, it is considered inefficient since knowing the object’s absolute geographic location is not required to accomplish this task. This work proposed the implementation of the unsupervised learning-based algorithm, namely convolutional autoencoder, to infer the co-location of people from a low-power consumption sensor data—magnetometer readings. The idea is that if the trained model can also reconstruct the other data with the structural similarity (SSIM) index being above 0.5, we can then conclude that the observed individuals were co-located. The evaluation of our system has indicated that the proposed approach could recognize the spatial co-location of people from magnetometer readings.