Advisor Information
Brian Knarr
Location
Dr. C.C. and Mabel L. Criss Library
Presentation Type
Poster
Start Date
2-3-2018 12:30 PM
End Date
2-3-2018 1:45 PM
Abstract
Activity monitoring in older adults is a great way to predict disability without interfering in their daily life. We can analyze that data using linear and nonlinear analysis. While linear analysis measures the variation of activity during a specific period of time (i.e., mean, standard deviation), nonlinear analysis focuses on the evolution of activity over a period of time. Therefore, the purpose of this study was to observe the relationship between linear and nonlinear analysis of activity data obtained by activity monitoring in older adults. Eight participants wore an activity monitor around their waist for seven days. Linear analysis was achieved by exporting average step count, average steps per day, average steps per bout, average active time, and average bout duration. Nonlinear analysis was achieved through the use of the Jensen-Shannon Divergence (JSD), which is an algorithm that measures similarity across multiple signals (i.e., days). A low JSD means that they are more similar across days. Over a 7-day period, a negative correlation was observed between JSD and the average steps per day (p2=0.45), but not between average total step count, average steps per bout, average active time, and average bout duration versus JSD. The negative correlation observed can suggest that older adults who are more active maintain a more similar structure of activity from day-to-day.
THE RELATIONSHIP BETWEEN LINEAR AND NONLINEAR ANALYSIS ON ACTIVITY DATA
Dr. C.C. and Mabel L. Criss Library
Activity monitoring in older adults is a great way to predict disability without interfering in their daily life. We can analyze that data using linear and nonlinear analysis. While linear analysis measures the variation of activity during a specific period of time (i.e., mean, standard deviation), nonlinear analysis focuses on the evolution of activity over a period of time. Therefore, the purpose of this study was to observe the relationship between linear and nonlinear analysis of activity data obtained by activity monitoring in older adults. Eight participants wore an activity monitor around their waist for seven days. Linear analysis was achieved by exporting average step count, average steps per day, average steps per bout, average active time, and average bout duration. Nonlinear analysis was achieved through the use of the Jensen-Shannon Divergence (JSD), which is an algorithm that measures similarity across multiple signals (i.e., days). A low JSD means that they are more similar across days. Over a 7-day period, a negative correlation was observed between JSD and the average steps per day (p2=0.45), but not between average total step count, average steps per bout, average active time, and average bout duration versus JSD. The negative correlation observed can suggest that older adults who are more active maintain a more similar structure of activity from day-to-day.