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A new dataset for evaluating pedometer performance

机译：用于评估计步器性能的新数据集

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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.
机译：这项工作描述了一个新的数据集，以改善计步器评估。先前的评估技术着重于使用实验室评估的常规步态，以简化实际步骤的手动计数。我们的目标是分析步态频繁变化或仅在短时间内保持规律的日常生活中发生的更自然条件下的计步器算法。我们录制了30名参与者的视频，这些参与者进行了3种活动：在赛道上行走，在建筑物中行走以及在房间中移动。在轨道上行走会产生规律，一致的步态，代表了计步器评估的传统方法。步行穿过建筑物并在房间周围进行的活动包括不同程度的停顿和步态变化，并且代表着各种各样的正常日常活动。通过观察视频，在加速度计信号中手动标记了每个步骤的地面真实时间。总共记录并注释了60,853个步骤。步态的子类被称为“步态”，它是在规则步幅的开始和结束，步态改变以及枢轴改变运动方向时发生的步态。在正常的步幅活动中，班次仅占0.03％的步幅，而在半定期和非结构化活动中，班次仅占10-25％的步幅。我们认为这些运动应单独识别，因为它们提供不同的加速度计信号，并可能导致不同量的能量消耗。拟议的数据集将是第一个专门允许计步器算法在非结构性步态上进行评估的模型，该步态算法可以更自然地模拟自然活动。

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