Incline, speed, and distance assessment during unconstrained walking

Abstract

Body accelerations during human walking were recorded by a portable measuring device. A new method for parameterizing body accelerations and finding the pattern of walking is outlined. Two neural networks were designed to recognize each pattern and estimate the speed and incline of walking. Six subjects performed treadmill walking followed by self-paced walking on an outdoor test circuit involving roads of various inclines. The neural networks were first "trained" by known patterns of treadmill walking. Then the inclines, the speeds, and the distance covered during overground walking (outdoor circuit) were estimated. The results show a good agreement between actual and predicted variables. The standard deviation of estimated incline was less than 2.6% and the maximum of the coefficient of variation of speed estimation is 6%. To the best of our knowledge, these results constitute the first assessment of speed, incline and distance covered during level and slope walking and offer investigators a new tool for assessing levels of outdoor physical activity.