Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland; ETH Zuirich, Department of Health Sciences and Technology, Zurich, Switzerland.
ETH Zuirich, Department of Health Sciences and Technology, Zurich, Switzerland.
Gait Posture. 2022 Feb;92:206-211. doi: 10.1016/j.gaitpost.2021.11.035. Epub 2021 Nov 27.
Accurate quantification of daily steps in a cardiovascular patient population is of high importance for primary and secondary prevention. While sensor derived step counts have been sufficiently validated for hip-worn devices and commercial wrist-worn devices, there is a lack of knowledge on validity of freely available step counting algorithms for raw acceleration data collected at the wrist.
How accurate are step-counting algorithms for wrist worn tri-axial accelerometers in a cardiac rehabilitation training setting?
Two step counting algorithms (Windowed Peak Detection, Autocorrelation) for tri-axial accelerometers (Axivity AX-3), were tested. Steps were recorded by chest-mounted GoPro video cameras as gold standard. Cardiovascular patients without neurological impairments enrolled in an ambulatory rehabilitation program were recruited. Recordings were performed during one 45-90 min outdoor physical therapy session of which 5-min segments of six movement categories, namely Walking, Running, Nordic, Stairs, Arm Movement [AM] With [+] and Without [-] Walking [W] were identified and analyzed. Mean absolute difference and mean absolute percentage error [MAPE] with regard to true steps measured from video are reported to report accuracy.
Training sessions of 22 patients were recorded and analyzed. Steps were overestimated during AM-W and underestimated during Walking, Running and Stairs. Windowed Peak Detection algorithm was more accurate during AM+W and AM-W and Autocorrelation performed better during Nordic. A MAPE of close or below 10% was achieved by both algorithms for the categories: Walking, Running, Stairs and Nordic.
Both algorithms provided accurate results for estimation of step counts in a controlled setting of a cardiovascular patient population. The quantification of daily number of steps recorded by wrist-worn accelerometers delivering raw data analyzed by freely available algorithms is a cost-effective option for research studies.
准确量化心血管病患者的日常步数对一级和二级预防非常重要。虽然基于传感器的计步数据已经通过髋部佩戴设备和商用腕部佩戴设备得到了充分验证，但对于从腕部采集的原始加速度数据所使用的免费计步算法的有效性，我们知之甚少。
在心脏康复训练环境中，三轴腕部加速度计的计步算法有多准确？
我们测试了两种三轴加速度计（Axivity AX-3）的计步算法（窗口峰值检测、自相关）。以佩戴在胸部的 GoPro 摄像机拍摄的视频作为金标准记录步数。我们招募了无神经损伤、参加门诊康复计划的心血管病患者。在一次 45-90 分钟的户外物理治疗过程中进行记录，确定并分析了 6 种运动类别的 5 分钟段，分别是行走、跑步、北欧式健走、爬楼梯、手臂运动（带或不带行走）。报告了针对视频中真实步数的平均绝对差和平均绝对百分比误差（MAPE），以评估准确性。
共记录和分析了 22 名患者的训练课程。在手臂运动（带或不带行走）时计步过多，在行走、跑步和爬楼梯时计步过少。窗口峰值检测算法在手臂运动（带行走和不带行走）时更准确，而自相关算法在北欧式健走时表现更好。两种算法在行走、跑步、爬楼梯和北欧式健走等类别中都达到了接近或低于 10%的 MAPE。
两种算法在心血管病患者人群的受控环境中均能提供准确的计步结果。使用腕部佩戴加速度计记录日常步数，然后对提供的原始数据进行分析，采用免费的算法，是一种具有成本效益的研究选择。
