CMA: Cross-Modal Association between Wearable and Structural Vibration Signal Segments for Indoor Occupant Sensing

Abstract

Indoor occupant sensing enables many smart home applications, and various sensing systems have been explored. Based on their installation requirements, we consider two categories of sensors - on- and off-body - and we look into the combination of them for occupant sensing due to their spatial and temporal complementarity. We focus on an example modality pair of wearable IMU and structural vibration that demonstrate modality complementarity in prior work. However, current efforts are built upon the assumption that the knowledge of the signal segments from two modalities are known, which is challenged in a multiple occupants co-living scenario. Therefore, establishing accurate cross-modal signal segment associations is essential to ensure that a correct complementary relationship is learned. We present CMA, a cross-modal signal segment association scheme between structural vibration and wearable sensors. We propose AD-TCN, a framework built upon a temporal convolutional network that calculates the amount of shared context between an structural vibration sensor and associated wearable sensor candidates from the parameters of the trained model. We evaluate CMA via a public multimodal dataset for systematic evaluation, and we collect a continuous uncontrolled dataset for robustness evaluation. CMA achieves up to AUC value, F1 score, and accuracy improvement compared to baselines.