Abstract
Step length estimation is an important issue in areas such as gait analysis, sport training, or pedestrian localization. In this article, we estimate the step length of walking using a waist-worn wearable computer named eButton. Motion sensors within this device are used to record body movement from the trunk instead of extremities. Two signal-processing techniques are applied to our algorithm design. The direction cosine matrix transforms vertical acceleration from the device coordinates to the topocentric coordinates. The empirical mode decomposition is used to remove the zero- and first-order skew effects resulting from an integration process. Our experimental results show that our algorithm performs well in step length estimation. The effectiveness of the direction cosine matrix algorithm is improved from 1.69% to 3.56% while the walking speed increased.
Highlights
Step length estimation is important in a number of applications such as pedestrian navigation,[1,2,3] gait analysis,[4] medical rehabilitation, and sports training
To eliminate the accumulation error caused by the baseline drifting and other factors in the integrated result, we propose a novel application of the empirical mode decomposition (EMD) technique.[18]
The estimated walking distance is the sum of each step length, which is calculated by equation (10)
Summary
Step length estimation is important in a number of applications such as pedestrian navigation,[1,2,3] gait analysis,[4] medical rehabilitation, and sports training. Promoting precision of the step length estimation is essential for a better pedestrian navigation service In other fields, such as gait analysis, medical rehabilitation, and sports training, both the step length and the changing rate of step length are important parameters for assessing health or motion intensity. Some researches concentrated on the empirical equations between the step length and other parameters such as walking frequency or vertical acceleration.[5,6,7,8,9,10,11,12] On the other hand, researches estimated step length based on biological model.
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