Next Article in Journal
On the Performance of Underlay Device-to-Device Communications
Next Article in Special Issue
Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market
Previous Article in Journal
Drone Detection and Defense Systems: Survey and a Software-Defined Radio-Based Solution
Previous Article in Special Issue
The Validity of Wireless Earbud-Type Wearable Sensors for Head Angle Estimation and the Relationships of Head with Trunk, Pelvis, Hip, and Knee during Workouts
 
 
Article

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Iosa
Sensors 2022, 22(4), 1454; https://doi.org/10.3390/s22041454
Received: 21 December 2021 / Revised: 5 February 2022 / Accepted: 11 February 2022 / Published: 14 February 2022
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20–46 and 24–46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject’s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects’ data consumption. View Full-Text
Keywords: human activity recognition; wearable sensors; wearable sensor data; machine learning human activity recognition; wearable sensors; wearable sensor data; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Elshafei, M.; Costa, D.E.; Shihab, E. Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd. Sensors 2022, 22, 1454. https://doi.org/10.3390/s22041454

AMA Style

Elshafei M, Costa DE, Shihab E. Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd. Sensors. 2022; 22(4):1454. https://doi.org/10.3390/s22041454

Chicago/Turabian Style

Elshafei, Mohamed, Diego Elias Costa, and Emad Shihab. 2022. "Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd" Sensors 22, no. 4: 1454. https://doi.org/10.3390/s22041454

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