Enhancing XGBoost with Heuristic Smoothing for Transportation Mode and Activity Recognition

Lidia Alecci, Leonardo Alchieri, Nouran Abdalazim, Pietro Barbiero, Silvia Santini, Martin Gjoreski

Abstract: We address the problem of the automatic recognition of transportation mode from sensor data collected using personal devices. We leverage a dataset that includes data from motion and location sensors embedded in smartphones carried by three different users at different body positions – hand, hips, torso and bag. The data was labelled by the users using one of eight activities: still, walking, run, bike, car, bus, train, subway. This dataset was made available in the context of the SHL recognition challenge 2023. We, team MUSIC, propose DecayXGBoost, an enhanced version of the classic XGBoost classifier. DecayXGBoost leverages statistical and frequency-domain features to discriminate among the eight activities and adds a post-inference smoothing stage that encodes several heuristics into the classification. Our results show that DecayXGBoost achieves a F1 score of 84.5%, demonstrating its effectiveness in accurately predicting transportation modes from motion and location data.