Liuyi Ling^1,2,3, Yiwen Wang^1,*, Fan Ding⁴, Li Jin¹, Bin Feng³, Weixiao Li³, Chengjun Wang¹, Xianhua Li¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383 - 26 December 2023

Abstract Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion More >

Muhammad Irfan¹, Khalil Ullah², Fazal Muhammad^3,*, Salman Khan³, Faisal Althobiani⁴, Muhammad Usman⁵, Mohammed Alshareef⁴, Shadi Alghaffari⁴, Saifur Rahman¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337 - 29 September 2022

Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features… More >

Zhengyuan Xu^1,2,#, Junxiao Yu^1,#, Wentao Xiang¹, Songsheng Zhu¹, Mubashir Hussain³, Bin Liu^1,*, Jianqing Li^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 157-177, 2023, DOI:10.32604/cmes.2022.020035 - 24 August 2022

Abstract In this article, to reduce the complexity and improve the generalization ability of current gesture recognition systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to perform More >

Jae Myung Kim^1,2, Gyu Ho Choi², Min-Gu Kim², Sung Bum Pan^1,2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1213-1227, 2022, DOI:10.32604/cmc.2022.025213 - 24 February 2022

Abstract Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes… More >

Anita Gehlot¹, Rajesh Singh¹, Sweety Siwach², Shaik Vaseem Akram¹, Khalid Alsubhi³, Aman Singh^4,*, Irene Delgado Noya^4,5, Sushabhan Choudhury²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 999-1015, 2022, DOI:10.32604/cmc.2022.023861 - 24 February 2022

Abstract Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized… More >

R. Joshua Samuel Raj^1,*, J. Prince Antony Joel², Salem Alelyani³, Mohammed Saleh Alsaqer³, C. Anand Deva Durai⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1171-1188, 2022, DOI:10.32604/cmc.2022.021667 - 03 November 2021

Abstract Human Adaptive Mechatronics (HAM) includes human and computer system in a closed loop. Elderly person with disabilities, normally carry out their daily routines with some assistance to move their limbs. With the short fall of human care takers, mechatronics devices are used with the likes of exoskeleton and exosuits to assist them. The rehabilitation and occupational therapy equipments utilize the electromyography (EMG) signals to measure the muscle activity potential. This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of… More >

Hassan Ashraf¹, Asim Waris^1,*, Syed Omer Gilani¹, Muhammad Umair Tariq¹, Hani Alquhayz²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 799-815, 2021, DOI:10.32604/iasc.2021.014765 - 01 March 2021

Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding… More >

Jiabin Wang^*, Kai Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 345-363, 2020, DOI:10.32604/cmes.2020.011046 - 18 September 2020

Abstract In many existing multi-view gait recognition methods based on images or video sequences, gait sequences are usually used to superimpose and synthesize images and construct energy-like template. However, information may be lost during the process of compositing image and capture EMG signals. Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection. To better solve the problems, a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed. Firstly, the sliding time window method is used to capture EMG signals. Then, the… More >

Davide Piovesan^{1, *}, Roberto Bortoletto²

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 23-47, 2020, DOI:10.32604/cmes.2020.09231 - 01 April 2020

Abstract Exoskeletons are designed to control the forces exerted during the physical coupling between the human and the machine. Since the human is an active system, the control of an exoskeleton requires coordinated action between the machine and the load so to obtain a reciprocal adaptation. Humans in the control loop can be modeled as active mechanical loads whose stiffness is continuously changing. The direct measurement of human stiffness is difficult to obtain in real-time, thus posing a significant limitation to the design of wearable robotics controllers. Electromyographic (EMG) recordings can provide an indirect estimation of… More >

Tushar Kulkarni¹, Dr. Rashmi Uddanwadiker²

Molecular & Cellular Biomechanics, Vol.13, No.2, pp. 87-104, 2016, DOI:10.3970/mcb.2016.013.099

Abstract This paper aims to determine the force required for holding the objects by human hand. A static analysis is performed on mathematical models to obtain holding force considering lower arm as class three lever and by varying the joint angles. Three mathematical models are discussed to quantify the force required to hold any object, for different weight of the object and the joint angles. A noninvasive experimentation using surface electromyogram was performed to determine the forces required by human hand for the same objects used in the mathematical modeling. Twenty-one male subjects participated in this… More >