Gait variability, fractal dynamics, and statistical regularity of treadmill and overground walking recorded with a smartphone

• Published in: Gait & posture 2024-06, Vol.111, p.53-58
• Main Authors: Di Bacco, Vincenzo E., Gage, William H.
• Format: Article
• Language: English
• Subjects: Entropy; Fractal; Nonlinear gait dynamics; Smartphone accelerometer; Variability; Wearables
• ISSN: 0966-6362; 1879-2219
• DOI: 10.1016/j.gaitpost.2024.04.002

The nonlinear variability present during steady-state gait may provide a signature of health and showcase one’s walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW). This study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW. Sixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure. Compared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures. Smartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations. •Smartphones can enable gait monitoring outside the lab and for clinical purposes.•Treadmills reduce gait variability and dynamics compared to overground setting.•Context of walking must be considered within the interpretation of the results.