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A Study of Detecting Social Interaction with Sensors in a Nursing Home Environment

  • Datong Chen
  • Jie Yang
  • Howard Wactlar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

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.

Keywords

Nursing Home Information Gain Support Vector Machine Model Hand Gesture Sensor Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Datong Chen
    • 1
  • Jie Yang
    • 1
  • Howard Wactlar
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon University 

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