Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

    Qi Liu1, Zihao Wu1,*, Xiaodong Liu2

    Journal on Internet of Things, Vol.4, No.3, pp. 169-181, 2022, DOI:10.32604/jiot.2022.040368 - 12 June 2023

    Abstract This study proposed a lightweight but high-performance convolution network for accurately classifying five upper limb movements of arm, involving forearm flexion and rotation, arm extension, lumbar touch and no reaction state, aiming to monitoring patient’s rehabilitation process and assist the therapist in elevating patient compliance with treatment. To achieve this goal, a lightweight convolution neural network TMCA-Net (Time Multiscale Channel Attention Convolutional Neural Network) is designed, which combines attention mechanism, uses multi-branched convolution structure to automatically extract feature information at different scales from sensor data, and filters feature information based on attention mechanism. In particular,… More >

  • Open Access

    ARTICLE

    Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

    R. Joshua Samuel Raj1,*, 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 >

  • Open Access

    ARTICLE

    Characteristics of Surface Electromyography of Forehand Smash of Badminton Players

    Chen Zhang*

    Molecular & Cellular Biomechanics, Vol.18, No.1, pp. 33-40, 2021, DOI:10.32604/mcb.2021.014352 - 26 January 2021

    Abstract To understand the characteristics of the forehand smash of badminton player and improve their performance, this study took eight badminton players as the subject, obtained the kinematics data through the Qualisys infrared high-speed camera, obtained the electromyography (EMG) data through the ME-6000 surface EMG test system, and compared and analyzed their forehand smash action. The results showed that the greater the angle and speed of different joints in the forehand smash was, the greater the speed and strength of hitting the ball was; the discharge amount of biceps brachii (BB) was the smallest, followed by More >

  • Open Access

    ARTICLE

    A Geometrical Approach to Compute Upper Limb Joint Stiffness

    Davide Piovesan1, *, 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 >

Displaying 1-10 on page 1 of 4. Per Page