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Detecting an Offset-Adjusted Similarity Score based on Duchenne Smiles
Manage episode 443660552 series 3605621
A tartalmat a Kai Kunze biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Kai Kunze vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Detecting interpersonal synchrony in the wild through ubiquitous wearable sensing invites promising new social insights as well as the possibility of new interactions between humans-humans and humans-agents. We present the Offset-Adjusted SImilarity Score (OASIS), a real-time method of detecting similarity which we show working on visual detection of Duchenne smile between a pair of users. We conduct a user study survey (N = 27) to measure a user-based interoperability score on smile similarity and compare the user score with OASIS as well as the rolling window Pearson correlation and the Dynamic Time Warping (DTW) method. Ultimately, our results indicate that our algorithm has intrinsic qualities comparable to the user score and measures well to the statistical correlation methods. It takes the temporal offset between the input signals into account with the added benefit of being an algorithm which can be adapted to run in real-time will less computational intensity than traditional time series correlation methods.
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