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A method of personal positioning for indoor customer tracking utilizing wearable inertial sensors

机译:一种利用可穿戴惯性传感器进行室内客户跟踪的个人定位方法

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摘要

This paper presents a method for indoor personal positioning especially addressing on a simple gait measurement using a body-mounted inertial sensor, a walk path estimation algorithm, and a trajectory identification technique. Considering utility of on-site customer traffic tracking, the method was designed to be autonomous needless of neither external measures nor cumbersome installations to the environment. The inertial sensor combining three-dimensional accelerometer, angular rate sensors, and geomagnetic sensors was used to assess walking dynamics and to obtain changes of heading directions. To avoid an inherent problem of the walk trajectory estimation by conventional dead-reckoning algorithm, an advanced probabilistic map matching method which employs a Particle filter was developed. The experiment with healthy male adults was evaluated to confirm the utility of the method in a condition of small retail store. The result showed that the proposed method demonstrated sufficiently feasible tacking which delineated personal trajectories and positioning while shopping. The method showed possible availability with the portable instrument and improved estimation accuracy by the advanced probabilistic map matching which identifies proper routings on the basis of the maximum likelihood, The method will be useful to enhance ambulatory personal tracking technique in daily indoor environment.
机译:本文提出了一种用于室内个人定位的方法,尤其是利用安装在人体上的惯性传感器,步行路径估计算法和轨迹识别技术对步态进行简单测量的方法。考虑到现场客户流量跟踪的实用性,该方法被设计为自主的,无需外部措施也无需繁琐的环境安装。结合了三维加速度计,角速度传感器和地磁传感器的惯性传感器用于评估步行动力学并获得航向的变化。为了避免传统的死区推算算法所产生的行走轨迹估计的固有问题,开发了一种采用粒子滤波的先进的概率图匹配方法。对健康男性成年人的实验进行了评估,以确认该方法在小型零售商店中的实用性。结果表明,该方法具有足够的可行性,可以勾勒出购物时的个人轨迹和位置。该方法通过先进的概率图匹配显示了便携式仪器的可能可用性,并提高了估计精度,该概率图匹配基于最大似然来识别正确的路线。该方法将有助于增强日常室内环境中的动态个人跟踪技术。

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