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Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

journal contribution
posted on 23.05.2022, 08:10 authored by Junyi Wang, Xuezheng Jiang, Qinggang MengQinggang Meng, Mohamad SaadaMohamad Saada, Haibin CaiHaibin Cai
Real-time walking behavior monitoring is essential in ensuring safety and improving people’s physical conditions with mobility difficulties. In this paper, a real-time walking motion detection system based on the intelligent walking stick, mobile phone and multi-label imbalance classification method combining focal loss and LightGBM (MFGBoost) is proposed. The Internet of Things (IoT) technology is utilized for communicating between the walking stick and mobile phone. The new MFGBoost is embedded into the Raspberry Pi to classify human motions. MFGBoost is scalable, and other boosting models, such as XGBoost, could also be used as its base classifier. An improved derivation method of the multi-classification focal loss function is proposed in this paper, which is the key to the combination of multi-classification focal loss and Boosting algorithms. We propose a novel denoise method based on window matrix and COPOD algorithm (W-OD). The window matrix is designed to extract data features and smooth noise, and COPOD could output the noise level of the model. A weighted loss function is designed to adjust the model’s attention to different samples based on the W-OD algorithm. We evaluate the latest classification model from multiple perspectives on multiple benchmark datasets and demonstrate that MFGBoost and W-OD-MFGBoost could improve classification performance and decision-making efficiency. Experiments conducted on human motion datasets show that W-OD-MFGBoost could achieve more than 97 percent classification accuracy.

Funding

National Natural Science Foundation of China (61903075)

Natural Science Foundation of Liaoning Province (2019-KF-03-02)

YOBAN Project under Newton Fund/Innovate UK (102871)

Fundamental Research Funds for the Central Universities (N2026003), the Chunhui Plan Cooperative Project of Ministry of Education (LN2019006)

History

School

  • Science

Department

  • Computer Science

Published in

Applied Intelligence

Publisher

Springer

Version

AM (Accepted Manuscript)

Rights holder

©TheAuthor(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

Publisher statement

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10489-022-03264-2

Acceptance date

18/01/2022

Publication date

2022-03-23

Copyright date

2022

ISSN

0924-669X

eISSN

1573-7497

Language

en

Depositor

Prof Qinggang Meng. Deposit date: 20 May 2022