@article{ref1,
title="Analyzing the importance of sensors for mode of transportation classification",
journal="Sensors (Basel)",
year="2021",
author="Friedrich, Björn and Lübbe, Carolin and Hein, Andreas",
volume="21",
number="1",
pages="e176-e176",
abstract="The broad availability of smartphones and Inertial Measurement Units in particular brings them into the focus of recent research. Inertial Measurement Unit data is  used for a variety of tasks. One important task is the classification of the mode of  transportation. In the first step, we present a deep-learning-based algorithm that  combines long-short-term-memory (LSTM) layer and convolutional layer to classify  eight different modes of transportation on the Sussex-Huawei  Locomotion-Transportation (SHL) dataset. The inputs of our model are the  accelerometer, gyroscope, linear acceleration, magnetometer, gravity and pressure  values as well as the orientation information. In the second step, we analyze the  contribution of each sensor modality to the classification score and to the  different modes of transportation. For this analysis, we subtract the baseline  confusion matrix from a confusion matrix of a network trained with a left-out sensor  modality (difference confusion matrix) and we visualize the low-level features from  the LSTM layers. This approach provides useful insights into the properties of the  deep-learning algorithm and indicates the presence of redundant sensor modalities.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010176",
url="http://dx.doi.org/10.3390/s21010176"
}