Related Experiment Video
Updated: May 12, 2025
Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Fusing GPS, Activity Monitors, and Self-Report to Improve Assessment of Walking Activity and Community Participation
George D Fulk1, Karen Klingman, Emily N Peterson
1Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia (G.D.F.); College of Nursing, Upstate Medical University, Syracuse, New York (K.K.); and Rollins School of Public Health, Emory University, , Atlanta, Georgia (E.N.P.).
Background And Purpose:
Walking and participation in the community are important goals for people post-stroke (PPS). These constructs are challenging to measure given limitations in current data collection methodologies. The purpose of this study was to (1) develop a data fusion approach that combined data from global positioning system (GPS), activity monitor (AM), and daily trip log to identify walking activity and participation in the community, and (2) to examine the construct validity of the data fusion method.
Methods:
At 60 days post-stroke, PPS wore a GPS and AM and completed a daily trip log for 7 days. Using a combination of a density-based spatial clustering algorithm and geocoding GPS, AM, and daily trip log data were time synched and fused to identify total trips taken outside the home; locations visited per trip; number of steps taken in the home, in the community, at each location visited, and in total. Associations between stroke outcomes and the data fusion metrics were determined to support the construct validity of the data fusion method.
Results:
Forty-four PPS took a mean of 2,541 steps/day, of which 56% were in the community, and took a mean of 0.39 trips/day outside the home and visited a mean of 0.42 locations. A social visit was the most common reason for going into the community. There were fair associations between number of trips outside the home and gait speed (GS), r = 0.49, Berg Balance Scale (BBS), r = 0.48, modified Rankin Scale (mRS), r = -0.47, and Stroke Impact Scale participation subscale (SIS-P) (0.45). There were moderate associations between steps taken in the community and GS, r = 0.63, BBS, r = 0.51, mRS, r = -0.61, and SIS-P, r = 0.43.
Discussion And Conclusions:
Participants did not often access their community. Fusing GPS, AM, and trip log data may provide a comprehensive method to identify walking activity and community participation in PPS.
Video Abstract Available:
for more insights from the authors (see the Video, Supplemental Digital Content, available at: http://links.lww.com/JNPT/A529 ).
