  Objective. Using raw, sub-second level, accelerometry data, we propose and
validate a method for identifying and characterizing walking in the free-living
environment. We focus on the sustained harmonic walking (SHW), which we define
as walking for at least 10 seconds with low variability of step frequency.
Approach. We utilize the harmonic nature of SHW and quantify local periodicity
of the tri-axial raw accelerometry data. We also estimate fundamental frequency
of observed signals and link it to the instantaneous walking (step-to-step)
frequency (IWF). Next, we report total time spent in SHW, number and durations
of SHW bouts, time of the day when SHW occurred and IWF for 49 healthy, elderly
individuals. Main results. Sensitivity of the proposed classification method
was found to be 97%, while specificity ranged between 87% and 97% and
prediction accuracy between 94% and 97%. We report total time in SHW between
140 and 10 minutes-per-day distributed between 340 and 50 bouts. We estimate
the average IWF to be 1.7 steps-per-second. Significance. We propose a simple
approach for detection of SHW and estimation of IWF, based on Fourier
decomposition. The resulting approach is fast and allows processing of a
week-long raw accelerometry data (approx. 150 million measurements) in
relatively short time (~half an hour) on a common laptop computer (2.8 GHz
Intel Core i7, 16 GB DDR3 RAM).
