Abstract
Social interaction plays an important role in our daily lives. It is one of the most important indicators of physical or mental diseases of aging patients. In this paper, we present a Wizard of Oz study on the feasibility of detecting social interaction with sensors in skilled nursing facilities. Our study explores statistical models that can be constructed to monitor and analyze social interactions among aging patients and nurses. We are also interested in identifying sensors that might be most useful in interaction detection; and determining how robustly the detection can be performed with noisy sensors. We simulate a wide range of plausible sensors using human labeling of audio and visual data. Based on these simulated sensors, we build statistical models for both individual sensors and combinations of multiple sensors using various machine learning methods. Comparison experiments are conducted to demonstrate the effectiveness and robustness of the sensors and statistical models for detecting interactions.
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Chen, D., Yang, J., Wactlar, H. (2005). A Study of Detecting Social Interaction with Sensors in a Nursing Home Environment. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_20
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DOI: https://doi.org/10.1007/11573425_20
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