Detecting social interaction of elderly in a nursing home evironment.pdfVIP

1 Detecting Social Interaction of Elderly in a Nursing Home Environment Datong Chen, Jie Yang, Robert Malkin, and Howard D. Wactlar Computer Science Department Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University _______________________________________________________________________ Social interaction plays an important role in our daily lives. It is one of the most important indicators of physical or mental changes in aging patients. In this paper, we investigate the problem of detecting social interaction patterns of patients in a skilled nursing facility. Our studies consist of both a “wizard of Oz” study and an experimental study of various sensors and detection models for detecting and summarizing social interactions among aging patients and caregivers. We first simulate plausible sensors using human labeling on top of audio and visual data collected from a skilled nursing facility. The most useful sensors and robust detection models are determined using the simulated sensors. We then present the implementation of some real sensors based on video and audio analysis techniques and evaluate the performance of these implementations in detecting interaction. We conclude the paper with discussions and future work. Categories and Subject Descriptors J.3 life and medical science; H3 information storage and retrieval; I.6 simulation and modeling Keywords Social interaction, human activity, medical care, health care, stochastic modeling, sensor _______________________________________________________________________ 1. INTRODUCTION As the elderly population continues to grow, clinicians and geriatric professionals need advanced technologies to support them in monitoring and managing patients’ quality of life in nursing homes. This research is part of an NSF project, CareMedia1, which aims to create a meaningful, manageable information resource that enables more complete and accurate assessment, di