Electromyography Assessment of Forearm Muscles: Towards the Control of Exoskeleton Hand

Abstract

Abstract-Hand plays an important role in a human's life by offering physical interaction and grasping capabilities. In most stroke cases, the hand is the most vulnerable part of the body that has a high chance of suffering. This has led to the development of a numerous wearable robotic devices such as exoskeleton hands. The exoskeleton hands can provide physical assistance for stroke survivors to regain their abilities in performing basic activities of daily living and to improve their quality of life. The key challenges in developing such a device do not only lie in designing its mechanical but also in designing its controller. In controlling the exoskeleton hand, the principal criterion is to work according to the user's motion intention. It can be done by utilizing the electromyogram (EMG) signals generated by forearm muscles contributed from the movement and/or grasping abilities of the hand. In this paper, electromyography assessment of forearm muscles towards the control of an exoskeleton hand is presented. The EMG signals are collected non-invasively using multi-channel surface EMG sensors. The contractions of the muscles are detected from several forearm (flexion and extensor) muscles and the data is processed through several pattern recognition steps, before being mapped to various pinching/gripping forces and angular joints. The adaptability and learning process is done through a neural network. The experimental results show separable classes of features and significant range of control inputs that represent the inter-relation between forearm EMG signals, various pinching/gripping forces and angular joints for exoskeleton hand control.

Dynamic Modelling of Hand Grasping and Wrist Exoskeleton: An EMG-based Approach

“…For the flexion hand movement, the measured and estimated force values obviously demonstrated a higher signal level compared to their MVC in the other section. For the extension section, the hand wrist angle was pulled away from the human body, causing the EDC muscle to generate a greater sEMG value than the FCR muscle [3], [39]. As shown in Fig.…”

Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits—A Systematic Review

“…The myoelectric control systems of upper limb exoskeletons are based on surface electromyography (EMG) signals, which are the electric potentials directly measured from skeletal muscles and that are generated from motor unit activation [ 3 ]. The generation of EMG signals is controlled by the human brain through motion intention, and is regulated by motor neurons in the spinal cord ( Figure 1 ), which offers a means for detecting the human motion intention before initiating a motion [ 4 ]. Compared to other control systems, the critical advantage of myoelectric control is its timely detection of the user’s motion intention leveraging electromechanical delay; the onset of motion intention can be detected about 50–100 ms earlier than the physical motion [ 5 , 6 ], giving time to control the upper limb wearable robotic exoskeletons to allow for more adaptive and intelligent human–robot interactions [ 7 ].…”