The widespread utilization of smartphones has provided extensive availability to Inertial Measurement Units, providing a wide range of sensory data that can be advantageous for the detection of transportation modes. The objective of this study is to propose a novel end-to-end approach to effectively explore a reduced amount of sensory data collected from a smartphone to achieve accurate mode detection in common daily traveling activities. Our approach, called Feature Pyramid biLSTM (FPbiLSTM), is characterized by its ability to reduce the number of sensors required and processing demands, resulting in a more efficient modeling process without sacrificing the quality of the outcomes than the other current models. FPbiLSTM extends an existing CNN biLSTM model with the Feature Pyramid Network, leveraging the advantages of both shallow layer richness and deeper layer feature resilience for capturing temporal moving patterns in various transportation modes. It exhibits an excellent performance by employing the data collected from only three out of seven sensors, i.e. accelerometers, gyroscopes, and magnetometers, in the 2018 Sussex-Huawei Locomotion (SHL) challenge dataset, attaining a noteworthy accuracy of 95.1% and an F1-score of 94.7% in detecting eight different transportation modes.

翻译：智能手机的广泛普及使得惯性测量单元得以广泛应用，从而提供了丰富的传感器数据，这些数据对交通方式检测具有显著优势。本研究旨在提出一种新颖的端到端方法，以高效利用智能手机采集的少量传感器数据，实现日常出行活动中交通方式的精确检测。我们的方法名为特征金字塔双向LSTM（FPbiLSTM），其特点在于能够减少所需传感器数量及处理需求，相比现有其他模型，在保证输出质量的前提下实现更高效的建模过程。FPbiLSTM通过引入特征金字塔网络对现有CNN双向LSTM模型进行扩展，充分利用浅层丰富的细节信息与深层稳健的特征表达，以捕捉不同交通方式下的时序运动模式。该方法仅使用2018年萨塞克斯-华为运动（SHL）挑战数据集中七个传感器中的三个（即加速度计、陀螺仪和磁力计）所采集的数据，便展现出卓越性能，在检测八种不同交通方式时达到了95.1%的准确率和94.7%的F1分数。