Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, EPFL STI IBI LMAM, Station 9, 1015, Lausanne, Switzerland.
Electronics and Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, EPFL STI IMT ESPLAB, Rue de la Maladière 71B, Case postale 526, 2002, Neuchâtel, Switzerland.
Med Biol Eng Comput. 2017 Oct;55(10):1773-1785. doi: 10.1007/s11517-017-1621-2. Epub 2017 Feb 14.
In daily life, a person's gait-an important marker for his/her health status-is usually assessed using inertial sensors fixed to lower limbs or trunk. Such sensor locations are not well suited for continuous and long duration measurements. A better location would be the wrist but with the drawback of the presence of perturbative movements independent of walking. The aim of this study was to devise and validate an algorithm able to accurately estimate walking cadence and speed for daily life walking in various environments based on acceleration measured at the wrist. To this end, a cadence likelihood measure was designed, automatically filtering out perturbative movements and amplifying the periodic wrist movement characteristic of walking. Speed was estimated using a piecewise linear model. The algorithm was validated for outdoor walking in various and challenging environments (e.g., trail, uphill, downhill). Cadence and speed were successfully estimated for all conditions. Overall median (interquartile range) relative errors were -0.13% (-1.72 2.04%) for instantaneous cadence and -0.67% (-6.52 6.23%) for instantaneous speed. The performance was comparable to existing algorithms for trunk- or lower limb-fixed sensors. The algorithm's low complexity would also allow a real-time implementation in a watch.
在日常生活中，通常使用固定在下肢或躯干上的惯性传感器来评估一个人的步态——这是其健康状况的一个重要指标。然而，这种传感器的位置并不适合进行连续和长时间的测量。手腕是一个更好的位置，但缺点是存在与行走无关的扰动运动。本研究的目的是设计和验证一种算法，该算法能够基于手腕处测量的加速度，准确估计日常生活中各种环境下的行走步频和速度。为此，设计了一种步频似然度量，自动过滤掉扰动运动，并放大与行走特征相关的周期性手腕运动。速度则使用分段线性模型进行估计。该算法在各种具有挑战性的户外环境（例如小径、上坡、下坡）中进行了行走验证。对于所有条件，该算法都成功地估计了步频和速度。总体中位数（四分位间距）的瞬时步频相对误差为-0.13%（-1.72 2.04%），瞬时速度的相对误差为-0.67%（-6.52 6.23%）。该算法的性能与固定在躯干或下肢上的传感器的现有算法相当。该算法的低复杂性也允许在手表中进行实时实现。
