Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

Mokri, Chiako; Bamdad, Mahdi; Abolghasemi, Vahid

doi:10.1007/s11517-021-02466-z

Cited by 34 publications

(19 citation statements)

References 49 publications

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“…We used sEMG to demonstrate the positive effects of yoga on balance, which was rare in previous studies. Previous studies showed that sEMG was able to accurately measure muscle activity through bioelectrical changes [50, 51, 52]. TA played an important role in the stabilization of OLS [53], and the stability of the sEMG signal amplitude during the process of maintaining balance meant the improvement of balance efficiency.…”

Section: Discussionmentioning

confidence: 99%

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Combined effects of 12-week yoga warm-up on athletic performance in male high school track and field athletes

Wei

2022

Preprint

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Background: Practicing yoga could improve balance and flexibility, but its positive significance as a long-term warm-up for formal training was uncertain. We hypothesized that practicing yoga during warm-up might positively affect balance, flexibility, and speed performance in male high school track and field athletes. Methods: Over a 12-week period, athletes in a yoga group (YG) (n=10) practiced yoga for 15 min 4 times a week during warm-up, while athletes in a dynamic stretching group (DSG) (n=10) practiced 15 min of dynamic stretching. Except for the warm-up activities, the training content of the two groups of students was the same. we tested performance indicators immediately before and after the intervention, including lower extremity flexibility test (right hip active flexion range), lower extremity balance test [using surface electromyography (sEMG) to measure right leg tibialis anterior (TA) activation during one-leg stance (OLS) with eyes closed], and speed performance test (100-meter and 800-meter tests). Results: We performed between-group and within-group comparisons for indicators of two groups by using SPSS (version 26.0). Within-group comparisons showed a significant improvement in flexibility (P=0.002) and balance (P=0.003) in YG, but no significant change in DSG, after the 12-week intervention. In addition, speed performance of both YG (100m, P=0.026; 800m, P=0.045) and DSG (100m, P=0.029; 800m, P=0.006) was significantly improved. Between-group comparison showed that YG had a significant advantage in 800m (P=0.045) and flexibility (P=0.031). Conclusions: These data suggested that practicing yoga as a long-term warm-up could help male high school track and field athletes improve lower body flexibility and 800m speed. In addition, yoga had a certain positive effect on balance, but it was not significant overall.

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Section: Discussionmentioning

confidence: 99%

Section: Effects Of Practicing Yoga As a Long-term Warm-up On Balancementioning

confidence: 99%

Combined effects of 12-week yoga warm-up on athletic performance in male high school track and field athletes

Wei

2022

Preprint

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“…It could be that the number of involved muscles in the upper-limb dataset was less than that of the lower-limb dataset. Moreover, unlike black-box models such as ANN or SVM (Mokri et al, 2022), our method is a grey-box model, in which the model interpretation is possible (Figures 9,10). The activity of each muscle could be provided for each active muscle during recording to provide insights into load-sharing problems (Rojas-Martínez et al, 2019).…”

Section: The Grey-box Structurementioning

confidence: 99%

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Shirzadi

Marateb²,

Rojas-Martínez³

et al. 2023

Front. Physiol.

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Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

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“…It was seen that different muscles responded differently to varying speeds of walking. Camila et al used kinematic and kinetic data as inputs to musculoskeletal model to simulate muscle activation for walking and running for lower limb [9]. Machine learning techniques were also implemented to predict force from EMG, along with IMU and load cells [10].…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Characterization of Human GAIT Using EMG Parameters

Das

Nagarajan

Poonguzhali

et al. 2022

J. Phys.: Conf. Ser.

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Predicting and analysing individual muscle forces during walking can give a good perspective of anatomical, physiological, and neurological characteristics of human movement. It can help analyse neuromuscular impairments of skeletal system and provide an understanding on how lower limb assistive devices affect wearer’s body, as these assistive devices are vital to assist people with disabilities to carry their daily activities with ease. Estimating force from EMG allows us to assess the contribution of an individual muscle to the over-all force applied by a group of muscles. This finding helps in understanding muscle dynamics during walking, which can serve as input for assistive devices. Thus, electromyography signals (EMG) can be an excellent choice for force estimation in kinesiological studies. This study aims to predict individual muscle force from EMG during walking. The right gastrocnemius lateralis muscle of a 23-year-old-male subject with no neurological/muscular disorder was analysed at normal walking. Two approaches were used to predict forces from EMG using MATLAB. The forces obtained were compared with force predicted using OpenSim. The main parameters used for prediction were muscle length, muscle velocity, pennation angle, and isometric force, along with EMG.

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Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

Cited by 34 publications

References 49 publications

Combined effects of 12-week yoga warm-up on athletic performance in male high school track and field athletes

Combined effects of 12-week yoga warm-up on athletic performance in male high school track and field athletes

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Biomechanical Characterization of Human GAIT Using EMG Parameters

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