Convolutional Neural Network Approach for Elbow Torque Estimation during Quasi-dynamic and Dynamic Contractions

Hajian, Gelareh; Morin, Evelyn; Etemad, Ali

doi:10.1109/embc46164.2021.9630287

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…5). These findings are in line with a previous study by Hajian et al [13] that reported increased elbow torque prediction accuracy for isokinetic and dynamic tasks when using joint position and velocity in combination with EMG data. The need for diverse features in torque prediction of more complex movements might imply that the activation of surrounding muscles does not always fully determine joint torque.…”

Section: Discussionsupporting

confidence: 93%

“…Comparable results between both approaches have been shown for ankle torque estimation during the isokinetic movement and gait [10] and knee joint torque estimation during non-weight bearing activities over seven days [11]. In particular, recurrent and convolutional neural networks (CNN) were found to perform well in EMG-informed estimation of biceps brachii muscle force in isometric contraction [12] and elbow joint torque during isotonic, isokinetic and dynamic task [13]. To achieve good results for increasingly more complex movements, such as dynamic tasks, artificial neural networks (ANN) require increasingly large training datasets, often resulting in a drop in estimation accuracy.…”

Section: Introductionmentioning

confidence: 96%

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Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Kizyte,

Lei,

Wang

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and intersession cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 96%

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Kizyte,

Lei,

Wang

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…techniques with success, yet this input is not considered in this study as it, requires specialized equipment [10,11].…”

Section: Estimation Of Maximum Shoulder and Elbow Joint Torques Based...mentioning

confidence: 99%

Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics

O’Sullivan

Menolotto

O’Flynn

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Repetitive movements that involve a significant shift of the body's center of mass can lead to shoulder and elbow fatigue, which are linked to injury and musculoskeletal disorders if not addressed in time. Research has been conducted on the joint torque individuals can produce, a quantity that indicates the ability of the person to carry out such repetitive movements. Most of the studies surround gait analysis, rehabilitation, the assessment of athletic performance, and robotics. The aim of this study is to develop a model that estimates the maximum shoulder and elbow joint torque an individual can produce based on anthropometrics and demographics without taking a manual measurement with a force gauge (dynamometer). Nineteen subjects took part in the study which recorded maximum shoulder and elbow joint torques using a dynamometer. Sex, age, body composition parameters, and anthropometric data were recorded, and relevant parameters which significantly contributed to joint torque were identified using regression techniques. Of the parameters measured, body mass index and upper forearm volume predominantly contribute to maximum torque for shoulder and elbow joints; coefficient of determination values were between 0.6 and 0.7 for the independent variables and were significant for maximum shoulder joint torque (P<0.001) and maximum elbow joint torque (P<0.005) models. Two expressions illustrated the impact of the relevant independent variables on maximum shoulder joint torque and maximum elbow joint torque, using multiple linear regression. Coefficient of determination values for the models were between 0.6 and 0.7. The models developed enable joint torque estimation for individuals using measurements that are quick and easy to acquire, without the use of a dynamometer. This information is useful for those employing joint torque data in biomechanics in the areas of health, rehabilitation, ergonomics, occupational safety, and robotics.Clinical Relevance-The rapid estimation of arm joint torque without the direct force measurement can help occupational safety with the prevention of injury and musculoskeletal disorders in several working scenarios.

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“…Therefore, all subsequent references to 'EMG' specifically pertain to surface EMG. The majority of work thus far has focused on intra-subject force modeling, where user-specific models are developed using burdensome amounts of user-supplied data [2]- [9]. Recently, some studies have pooled data from multiple users to provide more data for deep inter-subject models [10]- [14], but the same users used to test the models were included during training.…”

Section: Introductionmentioning

confidence: 99%

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

Hajian,

Campbell,

Ansari

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the enduser using a small amount of new data (only 10%, 20%, and 40% of the new user data). Using a deep multimodal convolutional neural network, consisting of two CNN models, one with highdensity (HD) EMG and one with motion data recorded by an Inertial Measurement Unit (IMU), our proposed TL technique significantly improved force modeling compared to leave-one-subjectout (LOSO) and even intra-subject scenarios. The TL approach increased the average R squared values of the force modeling task by 60.81%, 190.53%, and 199.79% compared to the LOSO case, and by 13.4%, 36.88%, and 45.51% compared to the intra-subject case for isotonic, isokinetic and dynamic conditions, respectively. These results show that it is possible to adapt to a new user with minimal data while improving performance significantly compared to the intra-subject scenario. We also show that TL can be used to generalize on a new experimental condition for a new user.

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Convolutional Neural Network Approach for Elbow Torque Estimation during Quasi-dynamic and Dynamic Contractions

Cited by 6 publications

References 12 publications

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

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