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On the Impact of Biceps Muscle Fatigue in Human Activity Recognition.

Author(s): Elshafei M, Costa DE, Shihab E

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance. I...

Article GUID: 33557239
Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.

Author(s): Elshafei M, Shihab E

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various inj...

Article GUID: 33498702
Finite Element Modelling of Bandgap Engineered Graphene FET with the Application in Sensing Methanethiol Biomarker.

Author(s): Singh P, Abedini Sohi P, Kahrizi M

In this work, we have designed and simulated a graphene field effect transistor (GFET) with the purpose of developing a sensitive biosensor for methanethiol, a biomarker for bacterial infections. The surface of a graphene layer is functionalized by manipula...

Article GUID: 33467459
A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects.

Author(s): Khannouz M; Glatard T;

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usua...

Article GUID: 33202905
Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes.

Author(s): Lessard-Tremblay M, Weeks J, Morelli L, Cowan G, Gagnon G, Zednik RJ

Traditional capacitive electrocardiogram (cECG) electrodes suffer from limited patient comfort, difficulty of disinfection and low signal-to-noise ratio in addition to the challenge of integrating them in wearables. A novel hybrid flexible cECG electrode wa...

Article GUID: 32927651
Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City.

Author(s): Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Sensors (Basel). 2020 Apr 16;20(8): Authors: Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Article GUID: 32316356
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.

Author(s): Dehghani A, Sarbishei O, Glatard T, Shihab E

Sensors (Basel). 2019 Nov 18;19(22): Authors: Dehghani A, Sarbishei O, Glatard T, Shihab E

Article GUID: 31752158
Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development.

Author(s): Alsaig A, Alagar V, Chammaa Z, Shiri N

Sensors (Basel). 2019 May 28;19(11): Authors: Alsaig A, Alagar V, Chammaa Z, Shiri N

Article GUID: 31141899
A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Author(s): Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Sensors (Basel). 2018 May 15;18(5): Authors: Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Article GUID: 29762493
Fast Feature-Preserving Approach to Carpal Bone Surface Denoising.

Author(s): Salim I, Hamza AB

Sensors (Basel). 2018 Jul 21;18(7): Authors: Salim I, Hamza AB

Article GUID: 30037109
Big Data-Driven Cellular Information Detection and Coverage Identification.

Author(s): Wang H, Xie S, Li K, Ahmad MO

Sensors (Basel). 2019 Feb 22;19(4): Authors: Wang H, Xie S, Li K, Ahmad MO

Article GUID: 30813353
Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing.

Author(s): Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Sensors (Basel). 2017 Sep 14;17(9): Authors: Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Article GUID: 28906434
Title:Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.
Authors:Elshafei M,  Shihab E
Link:https://www.ncbi.nlm.nih.gov/pubmed/33498702
DOI:10.3390/s21030759
Category:Sensors (Basel)
PMID:33498702
Dept Affiliation: ENCS
1 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Description:

Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.

Sensors (Basel). 2021 Jan 23; 21(3):

Authors: Elshafei M, Shihab E

Abstract

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various injuries that may require up to 22 weeks of treatment. In this work, we adopt a wearable approach to detect biceps muscle fatigue during a bicep concentration curl exercise as an example of a gym activity. Our dataset consists of 3000 bicep curls from twenty middle-aged volunteers at ages between 27 to 30 and Body Mass Index (BMI) ranging between 18 to 28. All volunteers have been gym-goers for at least 1 year with no records of chronic diseases, muscle, or bone surgeries. We encountered two main challenges while collecting our dataset. The first challenge was the dumbbell's suitability, where we found that a dumbbell weight (4.5 kg) provides the best tradeoff between longer recording sessions and the occurrence of fatigue on exercises. The second challenge is the subjectivity of RPE, where we average the reported RPE with the measured heart rate converted to RPE. We observed from our data that fatigue reduces the biceps' angular velocity; therefore, it increases the completion time for later sets. We extracted a total of 33 features from our dataset, which have been reduced to 16 features. These features are the most overall representative and correlated with bicep curl movement, yet they are fatigue-specific features. We utilized these features in five machine learning models, which are Generalized Linear Models (GLM), Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Feedforward Neural Networks (FNN). We found that using a two-layer FNN achieves an accuracy of 98% and 88% for subject-specific and cross-subject models, respectively. The results presented in this work are useful and represent a solid start for moving into a real-world application for detecting the fatigue level in bicep muscles using wearable sensors as we advise athletes to take fatigue into consideration to avoid fatigue-induced injuries.

PMID: 33498702 [PubMed - in process]