Next Article in Journal
Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
Next Article in Special Issue
Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit
Previous Article in Journal
Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury
 
 
Article

A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

1
Mechanical and Aerospace Department, Monash University, Melbourne, VIC 3800, Australia
2
Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-0012, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios A. Patikas
Sensors 2021, 21(4), 1499; https://doi.org/10.3390/s21041499
Received: 19 January 2021 / Revised: 6 February 2021 / Accepted: 9 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland–Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue. View Full-Text
Keywords: fatigue estimation; human motion data; deep learning; force plate; IMU; machine learning fatigue estimation; human motion data; deep learning; force plate; IMU; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Jiang, Y.; Hernandez, V.; Venture, G.; Kulić, D.; K. Chen, B. A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate. Sensors 2021, 21, 1499. https://doi.org/10.3390/s21041499

AMA Style

Jiang Y, Hernandez V, Venture G, Kulić D, K. Chen B. A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate. Sensors. 2021; 21(4):1499. https://doi.org/10.3390/s21041499

Chicago/Turabian Style

Jiang, Yanran, Vincent Hernandez, Gentiane Venture, Dana Kulić, and Bernard K. Chen. 2021. "A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate" Sensors 21, no. 4: 1499. https://doi.org/10.3390/s21041499

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop