Social interactions play an important role in the overall well-being. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user effort. However, sensing capabilities of smart-phones can play a significant role in automatic detection of social interactions. In this paper, we describe our method of detecting interactions between people, specifically focusing on interactions that occur in synchrony, such as walking. Walking together between subjects is an important aspect of social activity and thus can be used to provide a better insight into social interaction patterns. For this work, we rely on sampling smartphone accelerometer and Wi-Fi sensors only. We analyse Wi-Fi and accelerometer data separately and combine them to detect walking in synchrony. The results show that from seven days of monitoring using seven subjects in real-life setting, we achieve 99% accuracy, 77.2% precision and 90.2% recall detection rates when combining both modalities.

Detecting Walking in Synchrony through Smartphone Accelerometer and Wi-Fi traces

Venet Osmani;Alban Maxhuni;Oscar Mayora
2014

Abstract

Social interactions play an important role in the overall well-being. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user effort. However, sensing capabilities of smart-phones can play a significant role in automatic detection of social interactions. In this paper, we describe our method of detecting interactions between people, specifically focusing on interactions that occur in synchrony, such as walking. Walking together between subjects is an important aspect of social activity and thus can be used to provide a better insight into social interaction patterns. For this work, we rely on sampling smartphone accelerometer and Wi-Fi sensors only. We analyse Wi-Fi and accelerometer data separately and combine them to detect walking in synchrony. The results show that from seven days of monitoring using seven subjects in real-life setting, we achieve 99% accuracy, 77.2% precision and 90.2% recall detection rates when combining both modalities.
978-3-319-14112-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/314645
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