Estimation and tracking of elder activity levels for health event prediction

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Significant declines in quality of life for elders in assisted living communities are typically triggered by health events. Given the necessary information, such events can often be predicted, and thus, be avoided or reduced in severity. Statistics on activities of daily living and activity level over an extended period of time provide important data for functional assessment and health prediction. However, persistent activity monitoring and continuous collection of this type of data is extremely labor-intensive, time-consuming, and costly. In this work, a method is proposed for automated estimation of activity levels based on silhouettes segmented from video data, and subsequent extraction of higher order information from the silhouettes. The general activity level was modeled as a function of this higher order information. A set of video sequences of student volunteers in a simulated residential setting was collected to use as the training data for all models. Using these models, activity levels of elderly adults can be automatically estimated. Identification of trends in the activity levels will provide highly useful information to health aids.