Motion based adaptive step length estimation using smartphone
The 18th IEEE International Symposium on Consumer Electronics …, 2014•ieeexplore.ieee.org
This paper presents a motion recognition based step length estimation algorithm using
smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT),
Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of
linear combination based step length model are adapted based on the result motion
recognition. In order to verify the proposed algorithm, we performed experiments on 5
subjects and showed accuracy of step length estimation as RMSE.
smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT),
Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of
linear combination based step length model are adapted based on the result motion
recognition. In order to verify the proposed algorithm, we performed experiments on 5
subjects and showed accuracy of step length estimation as RMSE.
This paper presents a motion recognition based step length estimation algorithm using smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of linear combination based step length model are adapted based on the result motion recognition. In order to verify the proposed algorithm, we performed experiments on 5 subjects and showed accuracy of step length estimation as RMSE.
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