Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data

TL;DR

This paper introduces a fast, accurate method using raw accelerometry data to identify and analyze sustained harmonic walking in free-living environments, focusing on elderly individuals.

Contribution

It presents a novel Fourier-based approach for detecting sustained harmonic walking and estimating step frequency from raw accelerometry data.

Findings

Sensitivity of 97% in classification

Total SHW time ranged from 10 to 140 minutes per day

Average instantaneous walking frequency estimated at 1.7 steps/sec

Abstract

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).