基于自适应双阈值的计步算法

针对现有计步算法对不同运动状态适应性差的问题，设计了一种基于自适应阈值的计步算法。该算法首先通过智能手环的内置三轴加速度传感器采集用户在慢走、快走以及跑步三种步行频率的加速度数据，经过五点滤波预处理，在自适应时间窗内检测波峰波谷，再将波峰均值和波谷均值的平均值作为上阈值，波谷均值作为下阈值，通过动态阈值判定步数，最后根据行走振幅和行走频率存在的规律性进行假步检测。测试表明，该算法对于不同的用户在三种不同频率下平均计步精度可以达到91.88%以上。

Pedometer method based on self-adaptive double threshold algorithm

To solve the problem that the existing step counting algorithms have poor adaptability to different motion states, this paper proposed a step counting algorithm based on self-adaptive threshold. By using the 3-axis accelerometer sensor in smart bracelet, the proposed algorithm firstly collect the acceleration data when users walked at three frequencies including slow walking, fast walking and running. After five-point filter preprocessing, this algorithm detected peaks and valleys in the self-adaptive time window. Then it took the average of the peak mean and valley mean as the upper threshold, and used the valley mean as the lower threshold. Based on this, the algorithm adopted a dynamic threshold analysis to achieve the step counting. Finally, in this algorithm, false steps could also be detected according to the regularity of walking amplitude and frequency. The test shows that the achievable average step counting accuracy of the proposed algorithm is above 91.88% for different users at three different walking frequencies.