A classification approach for the sports behavior data with random forest

Xianzhi Yao; Qinghai Wang

doi:10.1117/12.2687417

28 August 2023 A classification approach for the sports behavior data with random forest

Xianzhi Yao, Qinghai Wang

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241J (2023) https://doi.org/10.1117/12.2687417
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Currently, a large number of studies analyzing the impact of genes and food intake on hypertension have been conducted using genomics, metabolomics, and multi-omics. However, when it comes to human activity, machine learning models face difficulty in adapting to the dimension, length, and complex activity combinations of exercise data. As a result, current research only focuses on quantitatively analyzing specific exercise behaviors. There is a lack of research on using deep learning methods to learn the relationship between hypertension and exercise from accelerometer data. We propose an optimal sequence mapping algorithm based on a machine learning model that uses a hidden semi-Markov model to downscale and encodes accelerometer data. We then propose an activity combination module to characterize complex activities. Finally, we use this method to train a random forest classification model and evaluate our experimental results. Our experimental results demonstrate that our improved sequence mapping algorithm has significantly reduced the length of effective wear time for accelerometer data by approximately 64.77%, rendering it well-suited for use with DL models. Furthermore, our findings show that the RF model with a module size of 3 outperformed both single-activity modules and other activity combination modules. Our research demonstrates that the HSMM improved sequence mapping algorithm enables personalized selection of the optimal activity threshold. Our encoding method effectively reduces the length of accelerometer data sequences while preserving key features. Moreover, our activity combination module accurately characterizes complex human activities, significantly enhancing the predictive power of the RF model.

Citation Download Citation

Xianzhi Yao and Qinghai Wang "A classification approach for the sports behavior data with random forest", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241J (28 August 2023); https://doi.org/10.1117/12.2687417

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Accelerometers

Machine learning

Random forests

Blood pressure

Feature extraction

Mathematical optimization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years