一种基于可穿戴式肌电传感器运动识别算法的行人航位推算方法

Qian Wang, Yuwei Chen, Xiang Chen, Xu Zhang, Ruizhi Chen, Wei Chen
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引用次数: 7

摘要

导航应用程序和基于位置的服务目前正在成为内置GPS接收器的智能手机的标准功能。然而,在全球卫星导航系统(GNSS)退化和拒绝环境下,实现移动用户随时随地定位的无所不在导航解决方案仍然是不可行的。采用不同的运动传感器和角度传感器来增强这种环境下的定位解决方案。本文采用肌电传感器(electromyography, EMG)测量人体肌肉收缩产生的电位,检测人体运动过程中的肌肉活动,并捕获人体行走动态,用于行人Dead Reckoning (PDR)解决方案中的运动识别和步长检测。本文中介绍的工作是我们使用可穿戴式肌电图传感器开发新颖且强大的PDR解决方案的试点研究的连续步骤。PDR解决方案包括站立和行走识别、步长检测、步幅估计以及带有头角传感器的位置计算。通过基于样本熵特征的隐马尔可夫模型分类器,从具有站立和行走动态的行走过程中采集的肌电信号中识别出静止状态。这种预分类处理降低了步进检测的误检率。在步长检测之后,研究了两种步长估计方法。首先,研究了一种基于统计模型的线性步长估计方法,以提高PDR解的精度。其次,采用基于特定肌电特征的运动识别算法识别五种不同的步行动作,并为每种步行动作设置固定的步幅,以传播位置;为了验证上述方法的有效性和实用性,进行了一些现场试验。测试结果表明,该方案在露天环境下的室外测试性能可与商用GPS接收机相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Pedestrian Dead Reckoning Solution Using Motion Recognition Algorithm with Wearable EMG Sensors
Navigation applications and location-based services are currently becoming standard features in smart phones with built-in GPS receivers. However, a ubiquitous navigation solution which locates a mobile user anytime anywhere is still not available, especially in Global Navigation Satellite System (GNSS) degraded and denied environments. Different motion sensors and angular sensors have been adopted for augmenting the positioning solutions for such environments. An electromyography (EMG) sensor, which measures electrical potentials generated by muscle contractions from human body, is employed in this paper to detect the muscle activities during human locomotion and captures the human walking dynamics for motion recognition and step detection in a Pedestrian Dead Reckoning (PDR) solution. The work presented in this paper is a consecutive step of our pilot studies in developing a novel and robust PDR solution using wearable EMG sensors. The PDR solution includes standing and walking identification, step detection, stride length estimation, and a position calculation with a heading angular sensor. A situation of standing still is identified from the EMG signals collected from a walking process, which has standing and walking dynamics, via a hidden Markov model classifier fed by sample entropy features. Such pre-classified processing reduces the misdetection rate of step detection. After step detection, two stride length estimation methods are investigated for the PDR solution. Firstly, a linear stride length estimation method based on statistic models is investigated to improve the accuracy of the PDR solution. Secondly, five different walking motions are recognized by a motion recognition algorithm based on some particular EMG features, and a fixed stride length is then set for each walking motion to propagate the position. To validate the effectiveness and practicability of the methods mentioned above, some field tests were conducted by a few testers. The test results indicate that the performance of the proposed PDR solution is comparable to that of a commercial GPS receiver in outdoor test under an open-sky environment.
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