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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (9): 1681-1686    DOI: 10.3785/j.issn.1008-973X.2010.09.009
    
Estimating walking distance based on single accelerometer
YANG Qing, CHEN Ling, CHEN Gen-cai
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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Abstract  

A method based on single accelerometer was proposed to estimate walking distance. With a 3x accelerometer attached to users crus, the method divided continuous accelerations into steps in terms of the state of crus, stance or swing, and then integrated the accelerations during swing phase twice to get the walking distance. A new selfadaptive step detection algorithm, based on the threshold step detection algorithm, could adjust walking parameters in terms of the walkers current statue (e.g. rate, stance etc). Experimental results indicated that the proposed algorithm was robust, and the effects of initial thresholds  were limited. The average step error of the selfadaptive step detection algorithm was one pace. And the average walking distance errors of the selfadaptive step detection algorithm were 1518% with constant pace and 2234% with variable pace. While the average walking distance errors of the threshold step detection algorithm under the two situations were  3108% and 4982% respectively. Experimental results show that the selfadaptive step detection algorithm is more exact and robust.

Published: 01 September 2010
CLC:  TP 311  
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Cite this article:

YANG Qing, CHEN Ling, CHEN Gen-Cai. Estimating walking distance based on single accelerometer. J4, 2010, 44(9): 1681-1686.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2010.09.009     OR     http://www.zjujournals.com/eng/Y2010/V44/I9/1681


基于单加速度传感器的行走距离估计

针对行走距离估计问题,提出基于单加速度传感器的方法.将单个三轴加速度传感器固定在步行者小腿上,根据腿部状态(静止或运动)将读到的连续加速度值进行分步,并重积分运动状态下的加速度值获得行走距离.在原有阈值分步法基础上采用新的分步方法——自适应分步法进行分步计算,它根据步行者当前行走状态(步速、姿态等)对分步参数进行自适应调整.数据显示自适应分步受初始阈值影响小,具有较好鲁棒性,其平均分步误差为1步,平均距离误差在近匀速运动和变速运动情况下分别为15-18%和22-34%；而阈值分步的平均距离误差在近匀速运动和变速运动下则分别为3108%和4982%.实验表明：自适应分步法的结果更加准确且鲁棒性强.

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