Sampling rate dependency in pedestrian walking speed estimation using DualCNN-LSTM

There are many inertial sensor based indoor localization methods for smartphone, for example, SINS and PDR. However, most of the MEMS sensors of smartphones are not precise enough for these methods. We proposed end-to-end walking speed estimation method using deep learning to perform robust walking speed estimation with a low-precision sensor. Currently, we use the input data with a fixed format of 200 samples at 100 Hz. However, the sampling rate and sequence length should be changed appropriately depending on the required accuracy and terminal performance. They are critical factors when using our method for a long time on a terminal because continuous processing of a large amount of data leads to shorter battery life. In this paper, we evaluate the accuracy of the estimated speed by our method when changing the sampling rate and sequence length. As a result, using 5 patterns of combinations, the estimation accuracy hardly changed.