Enhanced Transportation and Locomotion Mode Recognition through Difference and Variance Analysis in Inertial Sensing Data

This paper presents the approach and results of Team Bun-Bo for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge focusing on human activity detection. The objective of the challenge is to classify 8 modes of transportation - standing still, walking, running, biking, car, bus, train, and subway - using data collected from Inertial Measurement Units (IMUs). Our approach involved extensive feature extraction from raw and processed kinematic indices, including acceleration, gyroscope, and magnetic data. Specific features included total acceleration across 3 axes and the horizontal plane, angles between different sensor axes, and successive differences of kinematic indices. We applied three ensemble boosting models under varying training and validation scenarios. Our findings highlight the Extreme Gradient Boosting Classifier (XGB), which achieved superior performance with an accuracy of 92% and an F1-score of 93%, outperforming other classifiers by margins ranging from 5% to 15%. This study underscores the effectiveness of advanced feature extraction techniques and ensemble learning models in enhancing the accuracy of transportation mode recognition systems based on IMU data.