Assessment of IMU Configurations for Human Activity Recognition using Ensemble Learning

Samuel J. Murphy; Rachel V. Vitali

doi:10.1109/ACCESS.2024.3440215

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Assessment of IMU Configurations for Human Activity Recognition using Ensemble Learning

Journal article

Open access

Peer reviewed

Assessment of IMU Configurations for Human Activity Recognition using Ensemble Learning

Samuel J. Murphy and Rachel V. Vitali

IEEE access, Vol.12, pp.111433-111442

08/06/2024

DOI: 10.1109/ACCESS.2024.3440215

Appears in UI Libraries Support Open Access

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https://doi.org/10.1109/ACCESS.2024.3440215View

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Abstract

Populations ranging from elite athletes to the elderly can benefit from unobtrusive activity monitoring. Inertial motion capture, which utilizes inertial measurement units (IMUs), is a relatively inexpensive and common technology that is uniquely advantageous when employed to achieve this goal. This study considers how varying IMU configurations in human activity recognition influences performance metrics. Ensemble learning was used for classification with support vector machine and k-nearest neighbor machine learning models utilized as inputs for a shallow neural network. The accuracy and F1 score for the neural network were compared between the four sensor configurations (full set, necessary only, person only, and tool only). The full set and necessary only performed the best with accuracy and F1 score above 97% and 0.95 respectively. The improved performance of these sets over the person only and tool only showed the benefits of a mixed composition of sensors.

Machine Learning

Accuracy

Activity Classification

Data collection

Deep learning

Ensemble Learning

Feature extraction

Human activity recognition

Human Activity Recognition (HAR)

Inertial Measurement Units

Monitoring

Task analysis

Wearable Sensors

UIOWA OA Agreement

Details

Title: Subtitle: Assessment of IMU Configurations for Human Activity Recognition using Ensemble Learning
Creators: Samuel J. Murphy - Mechanical Engineering Department, University of Iowa, Iowa City, IA, USA
Rachel V. Vitali - University of Iowa
Resource Type: Journal article
Publication Details: IEEE access, Vol.12, pp.111433-111442
Publisher: IEEE
DOI: 10.1109/ACCESS.2024.3440215
ISSN: 2169-3536
eISSN: 2169-3536
Grant note: ED#P116S210005 / U.S. Department of Education (10.13039/100000138)
Language: English
Date published: 08/06/2024
Academic Unit: Mechanical Engineering; Roy J. Carver Department of Biomedical Engineering
Record Identifier: 9984696782102771

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