Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode
  Detection
Qinrui Tang Hao Cheng
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.