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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 3x accelerometer attached to users 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 selfadaptive step detection algorithm, based on the threshold step detection algorithm, could adjust walking parameters in terms of the walkers 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 selfadaptive step detection algorithm was one pace. And the average walking distance errors of the selfadaptive step detection algorithm were 1518% with constant pace and 2234% with variable pace. While the average walking distance errors of the threshold step detection algorithm under the two situations were 3108% and 4982% respectively. Experimental results show that the selfadaptive step detection algorithm is more exact and robust.
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Published: 01 September 2010
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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3108%和4982%.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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