A new dataset for evaluating pedometer performance

This work describes a new dataset to improve pedometer evaluation. Prior evaluation techniques focus on regular gaits using laboratory assessment to simplify the manual counting of actual steps. Our goal is to analyze pedometer algorithms under more natural conditions that occur during daily living where gaits are frequently changing or remain regular for only brief periods of time. We video recorded 30 participants performing 3 activities: walking around a track, walking through a building, and moving around a room. Walking around a track uses a regular, consistent gait, and represents the traditional approach to pedometer evaluation. Walking through a building and around a room are activities that include varying amounts of pauses and gait changes, and represent a wider variety of normal daily activities. The ground truth time of each step was manually marked in the accelerometer signals by observing the videos. Collectively 60,853 steps were recorded and annotated. A subclass of steps called shifts were identified as those occurring at the beginning and end of regular strides, during gait changes, and during pivots changing the direction of motion. While shifts comprised only .03% of steps in the regular stride activity, they comprised 10–25% of steps in the semi-regular and unstructured activities. We believe these motions should be identified separately, as they provide different accelerometer signals, and likely result in different amounts of energy expenditure. The proposed dataset will be the first to specifically allow for pedometer algorithms to be evaluated on unstructured gaits that more closely model natural activities.