  The ubiquity of personal digital devices offers unprecedented opportunities
to study human behavior. Current state-of-the-art methods quantify physical
activity using 'activity counts,' a measure which overlooks specific types of
physical activities. We proposed a walking recognition method for sub-second
tri-axial accelerometer data, in which activity classification is based on the
inherent features of walking: intensity, periodicity, and duration. We
validated our method against 20 publicly available, annotated datasets on
walking activity data collected at various body locations (thigh, waist, chest,
arm, wrist). We demonstrated that our method can estimate walking periods with
high sensitivity and specificity: average sensitivity ranged between 0.92 and
0.97 across various body locations, and average specificity for common daily
activities was typically above 0.95. We also assessed the method's algorithmic
fairness to demographic and anthropometric variables and measurement contexts
(body location, environment). Finally, we have released our method as
open-source software in MATLAB and Python.
