# Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode Detection

arXiv: https://arxiv.org/abs/2310.11087 · HTML: https://mlsanity.com/paper/2310.11087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition](https://mlsanity.com/paper/2004.13918/) (2020)

## Built on by (most cited first, 2 of 2)

- [Model Context Protocols in Adaptive Transport Systems: A Survey](https://mlsanity.com/paper/2508.19239/) (2025)
- [Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network](https://mlsanity.com/paper/2404.15323/) (2024)

## Abstract (arXiv)

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.