Ensemble Learning Approach for Human Activity Recognition Involving Missing Sensor Data

In this paper, we describe the human activity recognition method of team TDU_BSA for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2024. Using ensemble learning, deep learning, XGBoost, and LightGBM algorithms, we obtained high accuracy in estimating the activities even when one of the following data sensors was missing: accelerometer, gyroscope, or magnetometer. Using deep learning, eight activities were classified in a stepwise approach. XGBoost and LightGBM were used for activity estimation based on selected features obtained from sensor data. The calculated F1 score of the SHL validation set was 82.5%.