Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The 2019
DOI: 10.1145/3341162.3343763
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Bike type identification using smartphone sensors

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Cited by 4 publications
(1 citation statement)
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“…Considering that existing works [15], [34], [35] suggest that deep learning is more suitable to analyze time-series of sensor data, our approach is based on CNNs as well. The works [34] compared CNN, LSTM, perceptron, and random forest and conclude that CNN provides the best results. The existing works [35] use the same sensors on a pattern recognition task and compare a deep learning approach with random forest classifiers in the experiment.…”
Section: E Classifier Design and Deep Learning Model Trainingmentioning
confidence: 99%
“…Considering that existing works [15], [34], [35] suggest that deep learning is more suitable to analyze time-series of sensor data, our approach is based on CNNs as well. The works [34] compared CNN, LSTM, perceptron, and random forest and conclude that CNN provides the best results. The existing works [35] use the same sensors on a pattern recognition task and compare a deep learning approach with random forest classifiers in the experiment.…”
Section: E Classifier Design and Deep Learning Model Trainingmentioning
confidence: 99%