  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.
