Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA.
Med Eng Phys. 2014 Jun;36(6):659-69. doi: 10.1016/j.medengphy.2014.02.006. Epub 2014 Mar 20.
A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 to 4.8m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.
需要一种在所有步行速度下都具有高精度水平的特定于主题的计步方法，以评估受损患者的功能水平。本研究旨在通过与动态活动中的视频数据进行比较，验证加速度数据的计步和步频计算。在 11 名健康成年人的脚踝、大腿和腰部各放置一个包含三轴加速度计的定制活动监测器。所有计步的视频内部观察者间可靠性的 ICC 值均大于 0.98。活动监测系统（AMS）算法在步态速度为 0.1 至 4.8m/s 的步行/慢跑试验中与视觉观察结果具有 92%（8%）的中位数（四分位距[IQR]）一致性，而 Fitbits（脚踝和腰部）和 Nike Fuelband（手腕）的一致性分别为 92%（36%）、93%（22%）和 33%（35%）。算法结果显示，在基于实验室的模拟自由生活协议中，具有较高的中位数（IQR）计步检测灵敏度（95%（2%））、阳性预测值（PPV）（99%（1%））和一致性（97%（3%））。该算法还显示出较高的中位数（IQR）灵敏度、PPV 和识别步行步（91%（5%）、98%（4%）和 96%（5%））、慢跑步（97%（6%）、100%（1%）和 95%（6%））的一致性，以及步频计算中平均误差小于 3%。
