Combining LSTM and CNN for mode of transportation classification from smartphone sensors

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI:10.1145/3410530.3414350

Björn Friedrich, Carolin Lübbe, A. Hein

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引用次数: 10

Abstract

The broad availability of smartphones and Inertial Measurement Units in particular brings them into 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 this paper, 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. We achieve a F1 score of 98.96 % on our private test set. We participated as team 103114102106|8 in the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge.

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结合LSTM和CNN对智能手机传感器的交通方式进行分类

智能手机和惯性测量单元的广泛使用使它们成为最近研究的焦点。惯性测量单元的数据用于各种任务。一项重要的任务是运输方式的分类。在本文中，我们提出了一种基于深度学习的算法，该算法结合了长短期记忆(LSTM)层和卷积层，对sussexhuawei locomosiontransportation (SHL)数据集上的八种不同的运输方式进行分类。模型的输入是加速度计、陀螺仪、线加速度、磁力计、重力和压力值以及方向信息。我们在我们的私有测试集上获得了98.96%的F1分数。我们以103114102106|8团队的身份参加了sussexhuawei Locomotion-Transportation (SHL)识别挑战赛。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

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