Marcotte Robert T, Bachman Shelby L, Zhai Yaya, Clay Ieuan, Lyden Kate
Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.
VivoSense, Inc., Newport Coast, CA, USA.
Digit Biomark. 2024 Dec 11;9(1):10-22. doi: 10.1159/000542850. eCollection 2025 Jan-Dec.
Wrist-worn accelerometers can capture stepping behavior passively, continuously, and remotely. Methods utilizing peak detection, threshold crossing, and frequency analysis have been used to detect steps from wrist-worn accelerometer data, but it remains unclear how different approaches perform across a range of walking speeds and free-living activities. In this study, we evaluated the performance of four open-source methods for deriving step counts from wrist-worn accelerometry data, when applied to data from a range of structured locomotion and free-living activities. In addition, we assessed how modifying the parameters of these methods would affect their performance.
Twenty-one participants (ages 20-33) wore an ActiGraph CentrePoint Insight Watch (Actigraph, LLC) on their non-dominant wrist while completing structured locomotion activities in a motion capture laboratory and during a free-living period in a mock apartment. Criterion step counts were determined from motion capture heel-strike events and from StepWatch 3 (Modus Health, LLC) during the free-living period. Four open-source methods implementing different algorithmic approaches were applied to CPIW data to derive step counts. The quantity and timing of method-derived and criterion steps during each type of activity were then compared.
In terms of performance during structured locomotion, methods that relied on a single parameter, such as peak detection or threshold crossing, demonstrated the lowest bias among those investigated. Furthermore, three of the four investigated methods overestimated step counts during slow walking and underestimated step counts during fast walking, while the last method consistently underestimated at least half of the recorded steps across all speeds. During free-living activities, the method relying on frequency analysis exhibited the lowest percent error of all methods. Finally, we found that the incorporation of a locomotion classifier, wherein steps were only estimated during identified locomotion periods, reduced error for two methods when applied to data across structured and free-living settings.
In studying the performance of different step-counting approaches across different settings, we found a tradeoff between performance during structured walking and that during free-living activities. These findings highlight the opportunity for novel, context-aware methods for accurate step counting across real-world settings.
腕部佩戴的加速度计可以被动、连续且远程地捕捉步行行为。利用峰值检测、阈值穿越和频率分析的方法已被用于从腕部佩戴的加速度计数据中检测步数，但在一系列步行速度和自由生活活动中，不同方法的表现仍不清楚。在本研究中，我们评估了四种从腕部佩戴的加速度计数据中得出步数的开源方法在应用于一系列结构化运动和自由生活活动数据时的性能。此外，我们评估了修改这些方法的参数将如何影响其性能。
21名参与者（年龄20 - 33岁）在非优势手腕上佩戴ActiGraph CentrePoint Insight手表，同时在动作捕捉实验室完成结构化运动活动，并在模拟公寓的自由生活期间佩戴。在自由生活期间，通过动作捕捉足跟撞击事件和StepWatch 3（Modus Health有限责任公司）确定标准步数。将四种实施不同算法方法的开源方法应用于CPIW数据以得出步数。然后比较每种活动类型中方法得出的步数和标准步数的数量及时间。
在结构化运动期间的性能方面，依赖单个参数的方法，如峰值检测或阈值穿越，在所研究的方法中偏差最低。此外，四种研究方法中的三种在慢走时高估步数，在快走时低估步数，而最后一种方法在所有速度下始终至少低估记录步数的一半。在自由生活活动期间，依赖频率分析的方法在所有方法中百分比误差最低。最后，我们发现纳入运动分类器，即在识别出的运动期间才估计步数，当应用于结构化和自由生活环境的数据时，两种方法的误差降低。
在研究不同步数计算方法在不同环境中的性能时，我们发现在结构化步行和自由生活活动期间的性能之间存在权衡。这些发现凸显了开发新颖的、情境感知方法以在现实世界环境中准确计算步数的机会。
