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A Study of Detecting Social Interaction with Sensors in a Nursing Home Environment
A Study of Detecting Social Interaction with Sensors
in a Nursing Home Environment
Datong Chen, Jie Yang, and Howard Wactlar
School of Computer Science, Carnegie Mellon University
{datong,yang+,hdw}@
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 pa-
tients. In this paper, we present a Wizard of Oz study on the feasibility of de-
tecting social interaction with sensors in skilled nursing facilities. Our study ex-
plores statistical models that can be constructed to monitor and analyze social
interactions among aging patients and nurses. We are also interested in identify-
ing 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 individ-
ual sensors and combinations of multiple sensors using various machine learn-
ing methods. Comparison experiments are conducted to demonstrate the effec-
tiveness and robustness of the sensors and statistical models for detecting
interactions.
1 Introduction
The worldwide population over age 65 is expected to more than double from 357
million in 1990 to 761 million by 2025 [17]. At present, five percent of Americans
over age 65 reside in nursing homes, with up to 50 percent of those over the age of 85
likely being placed in a nursing home at some point in their lives [13]. Among these
nursing home residents, about 80% of them are believed to suffer from a psychiatric
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