Assessing the impact of fatigue on gait using inertial sensors

Ameli, Sina; Stirling, David; Naghdy, Fazel; Naghdy, Golshah; Aghmesheh, Morteza

doi:10.1109/aim.2013.6584109

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…IMUs are widely used to estimate lower extremity fatigue in gait [20]. They have been used to distinguish gait patterns between fatigue and non-fatigue conditions using machine learning algorithms obtaining 96% with an SVM classifier [21].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, there are low-cost wearable devices such as Inertial Measurement Units (IMUs), goniometers and Optical Fiber Sensors (OFSs) for MF estimation. The IMUs can be used to evaluate and estimate MF by studying posture characteristics [20], and kinematic changes [21]. Mamam et al collected data from four IMUs (located at the ankle, hip, wrist, and torso) and a heart rate sensor to detect fatigue in different industrial tasks.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Otálora,

Segatto,

Monteiro

et al. 2023

Sensors

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Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Otálora,

Segatto,

Monteiro

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…11 In this study, in order to develop models of subject's ambulatory performance, a set of GMMs modelling the joint angles associated with certain body segments was developed. 32 The details of the inertial data clustering algorithm are presented in the previous study. 19 The RPE score…”

Section: Modellingmentioning

confidence: 99%

Quantitative and non-invasive measurement of exercise-induced fatigue

Ameli

Naghdy

Stirling

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part P:

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Prolonged exercise-induced muscular fatigue adversely affects physical performance. The fatigue increases the risk of sport injuries, whereas early fatigue detection and assessment can prevent injuries. An assessment of the most crucial impacts of fatigue on physical performance leads to the development of an accurate, non-invasive and objective muscular fatigue measurement method. The most common manifestation of exercise-induced fatigue is a significant decline in force produced by the muscles that in turn affects motion characteristics. This directly alters body postural behaviour and decreases the amount of kinetic energy produced by the subject. The current non-invasive and objective methods to measure fatigue and analyse motion characteristics cannot provide a comprehensive information about muscular fatigue because of their limited ability to record different aspects of motion. An objective and non-invasive assessment method of exercise-induced fatigue, in which the variations in postural behaviour and kinetic energy are quantified to determine the fatigue effects on the motion characteristics, is proposed. The inertial parameters associated with 23 body segments of 20 healthy subjects (males and females) were measured during stair climbing tests before and after a fatigue protocol. The degree of fatigue was assessed by a score measuring the total differences in kinetic energy and postural behaviour between the first and second stair climbing tests. A Gaussian mixture model was used to classify the body postures and the total variation in postural behaviour caused by the two stair climbing tests was determined. The validity of the method to measure the degree of fatigue induced by this fatigue protocol was ascertained by benchmarking it against the degree of fatigue experienced by the subjects during fatigue protocol and reported in a questionnaire based on the Rate of Perceived Exertion Scale. A Pearson product-moment correlation measuring the linear correlation between the objective and subjective fatigue scores shows a strong correlation of 0.95 for male subjects and 0.70 for female subjects. The findings demonstrate that inertial motion analysis proposed in this study provides a reliable, non-invasive and accurate fatigue measurement tool.

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“…Fatigue is also known to influence postural control and movement coordination, specifically leading to altered movement kinematics (e.g., range of motion, velocity, and jerk) [40,48,50]. These changes in kinematic parameters with fatigue progression have been tracked using motion capture systems, including wearable sensors and infrared camera systems [43,48,[51][52][53]. For instance, changes in gait kinematics can serve as reliable indicators of fatigue during walking [42,54].…”

Section: Introductionmentioning

confidence: 99%

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Kuber,

Godbole,

Rashedi

2024

Applied Sciences

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Back-Support Industrial Exoskeletons (BSIEs) can be beneficial in reducing the risk of injury due to overexertion during trunk flexion tasks. Most real-world tasks include complex body movements, leading to mixed outcomes that necessitate field-based methods for detecting overall physical demands. Monitoring fatigue can be beneficial in this regard to ensure that benefits of BSIEs are translated to the real world. Our experiment included 14 participants, who performed 30 repetitions of 45° trunk-flexion while assisted by a BSIE, first without fatigue and then at medium-high back fatigue (7/10 in the Borg scale). We extracted 135 features from recorded muscle activity, trunk motion, and whole-body stability across bending, transition, and retraction portions of each trunk-flexion cycle. Four classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were implemented to assess fatigue prediction. XGB (Accuracy: 86.1%, Recall: 86%, Specificity: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (erector spinae) muscle. Meanwhile, stability measures showed high predictability with both RF (92.9%, 91.9%, 94.1%) and XGB (93.5, 94.1%, 93.1%). Findings demonstrate the success of force plates, and when replaced by pressure insoles, they can facilitate real-world fatigue detection during BSIE-assisted trunk-flexion tasks.

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Assessing the impact of fatigue on gait using inertial sensors

Cited by 8 publications

References 34 publications

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Quantitative and non-invasive measurement of exercise-induced fatigue

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

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