An autonomous wearable system for predicting and detecting localised muscle fatigue

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

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. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.

Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables

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.

Diagnosis and prognosis for exercise-induced muscle injuries: from conventional imaging to emerging point-of-care testing

With the development of modern society, we have witnessed a significant increase of people who join in sport exercises, which also brings significantly increasing exercise-induced muscle injuries, resulting in reduction and even cessation of participation in sports and physical activities. Although severely injured muscles can hardly realize full functional restoration, skeletal muscles subjected to minor muscle injuries (e.g., tears, lacerations, and contusions) hold remarkable regeneration capacity to be healed without therapeutic interventions. However, delayed diagnosis or inappropriate prognosis will cause exacerbation of the injuries. Therefore, timely diagnosis and prognosis of muscle injuries is important to the recovery of injured muscles. Here, in this review, we discuss the definition and classification of exercise-induced muscle injuries, and then analyze their underlying mechanism. Subsequently, we provide detailed introductions to both conventional and emerging techniques for evaluation of exercise-induced muscle injuries with focus on emerging portable and wearable devices for point-of-care testing (POCT). Finally, we point out existing challenges and prospects in this field. We envision that an integrated system that combines physiological and biochemical analyses is anticipated to be realized in the future for assessing muscle injuries.

A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.

Surface Electromyography Analysis of the Lower Extremities of Subjects Participating in Baduanjin Exercises

Purpose

The purpose of this study was to assess the effects of practicing Baduanjin exercises on the lower extremities of subjects using electromyography analysis.

Subjects

110 healthy adults were randomly assigned as subjects to two groups: SG group who received sixteen weeks of Baduanjin training and CG group who received no training.

Methods

The methods used in this study included the use of a sixteen-channel sEMG system to record and measure activity changes in vastus medialis and vastus lateralis.

Results

After 16 weeks of Baduanjin training, the results of this study showed that the SG group had significant increases in RMS (root mean square) (in vastus lateralis, p > 0.05; in vastus medialis, p < 0.05), in AEMG (average electromyographic activity) (in vastus lateralis, p > 0.05; in vastus medialis, p < 0.05), and in IEMG (integrated electromyogram) (in vastus lateralis, p > 0.05; in vastus medialis, p < 0.05). No adverse events from treatment were reported during the whole period of this study.

Conclusion

This study concludes that performing 16 weeks of Baduanjin training can significantly improve strength and the physical function of the lower extremities among healthy adults.

MEASUREMENT OF UPPER LIMB MUSCLE FATIGUE USING DEEP BELIEF NETWORKS

In recent years, a robust increasing interest has been observed in wearable devices featuring smart health, smart fitness, and human–machine interaction applications. While we gained some advances on use of surface electromyography (sEMG) signals recorded from upper extremities for controlling external devices, only limited attempt has been made to track the status of targeted muscles and forecast muscle fatigue onset. In this study, we address use of sEMG signals acquired from upper extremities to predict onset of muscle fatigue using deep belief networks (DBNs) as a learning mechanism. We demonstrate that a deep architecture can learn from raw data and provide comparable performance to feature-based approaches. Experimental results show that the DBNs model investigated in this study achieves an average classification accuracy of 85.3% without any subject-oriented calibration and achieves a best case accuracy of 97.60%. A transient-to-fatigue state is introduced before the fatigue onsets as an early warning state. The aim of this paper is to evaluate the performance of the popular deep models in real fatigue detection applications. The model provides a promising result compared with state-of-art works without any feature selection process, which could potentially generate better features while reducing the requirement for expertise in data.

Super wavelet for sEMG signal extraction during dynamic fatiguing contractions

In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-wavelet function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-wavelet function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo wavelet was tuned using the decomposition of 70% of the sEMG trials. 28 independent pseudo-wavelet evolution were run, after which the best run was selected and then tested on the remaining 30% of the trials to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard wavelet functions (p<0.05), giving an average correct classification of 87.90%.

Novel pseudo-wavelet function for MMG signal extraction during dynamic fatiguing contractions

The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).

Towards whole body fatigue assessment of human movement: a fatigue-tracking system based on combined sEMG and accelerometer signals

This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when the muscle is experiencing fatigue. This assumption is verified with a rigorous statistical analysis. Based on this proven linearity, localized muscular fatigue is simplified as a linear model. Furthermore, localized muscular fatigue is considered a dynamic process and, hence, the localized fatigue levels are tracked by updating the parameters with the most current surface electromyogram (sEMG) measurements. Finally, an overall fatigue level is computed by fusing localized muscular fatigue levels. The developed fatigue-tracking system is evaluated with two fatigue experiments (in which 10 male subjects and seven female subjects participated), including holding self-weight (dip start position training) and lifting weight with one arm (arm curl training).