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Smart sensing garments can extend the functionality of clothing beyond aesthetic and protective purposes. In contrast to skin-mounted sensors, garment-embedded sensor performance depends on fitting. In this work, we present a modeling and simulation approach to predict sensor performance in garments. We implemented a state-of-the-art particle-based model of fabrics to simulate the topology and drape of the garment. Based on the simulation, we introduce a method to extract and visualize maps for garment-embedded sensor performance metrics. We present performance maps for three basic modalities pertained to orientation, skin contact, and strain. Design parameters as garment fitting and material, and body proportion are analyzed regarding position specific sensor performance. To evaluate our model, we compared simulations to participant study data, confirming that our approach is suitable to plan sensor positioning and garment design before implementing garment prototypes.

Does loose fitting matter? Predicting sensor performance in smart garments.