电子测量与仪器学报

张超翔,陈璟,郑晨辉.基于峰值检测的自适应时间窗口计步算法[J].电子测量与仪器学报,2021,35(4):195-203

基于峰值检测的自适应时间窗口计步算法

Adaptive time window step counting algorithm based on peak detection

DOI：

英文关键词:step counting peak detection adaptive time window peak verification indoor localization

基金项目:江苏省青年科学基金(BK20150159)、江苏省研究生科研与实践创新计划（SJCX20_0778）、校级研究生科研与实践创新计划（1252050205198217）项目资助

作者	单位
张超翔	1. 江南大学人工智能与计算机学院
陈璟	1. 江南大学人工智能与计算机学院，2. 江南大学江苏省模式识别与计算智能工程实验室
郑晨辉	1. 江南大学人工智能与计算机学院

Author	Institution
Zhang Chaoxiang	1. School of Artificial Intelligence and Computer Science, Jiangnan University
Chen Jing	1. School of Artificial Intelligence and Computer Science, Jiangnan University，2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence, Jiangnan University
Zheng Chenhui	1. School of Artificial Intelligence and Computer Science, Jiangnan University

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中文摘要:

针对现有的计步算法无法适应用户行走状态、手机姿态多样性的问题,以及有效解决静止状态下的虚假步态周期的判别问题,提出基于峰值检测的自适应时间窗口计步算法。该算法通过检测加验证的方式进行计步,利用双重滤波对原始加速度进行预处理,根据峰、谷值时间差的自适应时间窗口消除伪峰值,再利用方差与标准差对检测到的峰值进行验证。实验结果表明,该算法相比传统方法(固定窗口峰值检测、条件判断峰值检测)在不同运动状态、不同手机姿态下的平均计步精度分别提高了 7. 7%、3. 4%,并且优于目前流行的商业步数计算应用。

英文摘要:

The existing step counting algorithms cannot effectively solve the problem of the diversity of user walking patterns and mobile phone holding styles as well as the problem of if the detected step is an actual step or a mimicking behavior. Therefore, an adaptive time window step counting algorithm based on peak detection is proposed. The algorithm counted the steps by detecting and verifying, used double filtering to preprocess the original acceleration, and designed an adaptive time window based on the peak and valley time difference to eliminate false peaks, used the variance and standard deviation to verify the peak, finally. The experimental results show that compared with traditional methods (fixed window peak detection, conditional judgment peak detection), the average step-counting accuracy of this algorithm in different motion states and different mobile phone holding styles is increased by 7. 7% and 3. 4%, respectively, and is better than the current popular commercial step detection application.

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