School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel). 2022 Feb 19;22(4):1640. doi: 10.3390/s22041640.
In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.
本文基于无线信道特性和接收信号强度指示（RSSI）方法估计人体步长。使用我们开发的带有嵌入式微控制器的可穿戴收发器，在四种场景下（室内步行、室外步行、室内慢跑和室外慢跑）测量到的 RSSI 转换为测试者脚踝之间的路径损耗。为简洁起见，我们称之为脚踝上的路径损耗。脚踝上路径损耗的直方图清楚地表明存在两个峰，第二个峰与最大路径损耗密切相关，而最大路径损耗又与步长相对应。这个直方图可以很好地用一个双项高斯拟合曲线模型来逼近。基于实验数据的直方图和双项高斯拟合曲线，我们提出了一种新的滤波技术来滤除路径损耗异常值，这有助于建立用于步长估计的路径损耗值的上下阈值。特别是，上阈值位于第二个高斯峰的右侧，其值是第二个高斯峰的平均值和标准差的函数。同时，下阈值位于第二个峰的左侧，位于测量数据的存活率下降到 0.68 的点处，即累积分布函数（CDF）接近 0.32。实验数据表明，所提出的滤波技术在步长估计中具有很高的准确性，室内步行的误差仅为 10.15mm，室内慢跑的误差为 4.40mm，室外步行的误差为 4.81mm，室外慢跑的误差为 10.84mm。
