Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network

TL;DR

This paper introduces an attention-based multiple-instance learning network that effectively combines low-rate acceleration and location signals for transportation mode recognition, reducing battery consumption while maintaining high accuracy.

Contribution

It proposes a novel multi-input neural network with separate processing for acceleration and location signals, embedding them into a shared space for robust transportation mode classification.

Findings

Effective recognition with low sampling rates

Outperforms existing algorithms on public datasets

Reduces battery consumption in mobile devices

Abstract

Transportation mode recognition (TMR) is a critical component of human activity recognition (HAR) that focuses on understanding and identifying how people move within transportation systems. It is commonly based on leveraging inertial, location, or both types of signals, captured by modern smartphone devices. Each type has benefits (such as increased effectiveness) and drawbacks (such as increased battery consumption) depending on the transportation mode (TM). Combining the two types is challenging as they exhibit significant differences such as very different sampling rates. This paper focuses on the TMR task and proposes an approach for combining the two types of signals in an effective and robust classifier. Our network includes two sub-networks for processing acceleration and location signals separately, using different window sizes for each signal. The two sub-networks are designed to also embed the two types of signals into the same space so that we can then apply an attention-based multiple-instance learning classifier to recognize TM. We use very low sampling rates for both signal types to reduce battery consumption. We evaluate the proposed methodology on a publicly available dataset and compare against other well known algorithms.