Abstract
Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5–466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.
Highlights
Fitness trackers motivate increased physical activity by counting steps during everyday life [1]
We found that varying sensor position resulted in an error rate ranging from 1–9%, similar to other studies examining how this condition affects pedometer accuracy
While error ranged from 3–8% during regular gait, it ranged from 7–34% during semi-regular gait and 11–466% during unstructured gait
Summary
Fitness trackers motivate increased physical activity by counting steps during everyday life [1]. Studies have found that pedometer accuracy is generally high during exercise activities [8]. In the U.S people dedicate about 30 min per day on average to exercise activities [9]. This means that a substantial number of steps are being taken and counted during activities other than exercise. Several studies conducted in free-living conditions have found large differences in pedometer accuracy by comparing steps counted by different pedometer devices worn at the same time [10,11,12,13]
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