Work

Ensemble Learning Approach for Human Activity Recognition Involving Missing Sensor Data

Year: 2024

Type: conference-paper

Abstract: 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%.

DOI: 10.1145/3675094.3678458

Authors: Hiroshi Minowa, Koki Yamashita, Ryoichi Sekiguchi, Masaki Kawakatsu

Institution: Tokyo Denki University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4402721960

FWCI: 0.458

Cites: 10

Cited by: 2

Related to: 10