Neuromuscular Interfacing: Establishing an EMG-Driven Model for the Human Elbow Joint

Abstract

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.

Algorithm to demodulate an electromyogram signal modulated by essential tremor

“…The motor is controlled by an electromyogram (EMG) signal from the patients. EMG signals are often used as input signals to control exoskeletons because an amplitude of EMG signals are almost relative to the level of muscle activation [18][19][20][21][22][23][24]. Based on the estimation of the voluntary muscle activation from the amplitude of EMG signal, the motor drives along with the estimated voluntary motion; that is, when the user intends to perform an action, the motor follows the intended action, and when the user intends to maintain a posture, the motor retains its posture and constrains the joint motion.…”

“…Therefore, in this experiment, we defined the muscle torque that was estimated from the EMG signal as the estimated data and defined the muscle torque that was calculated by inverse dynamics as the ground truth data. To evaluate the accuracy of the demodulation process, we used the root mean square error (RMSE) as a parameter; the RMSE is most commonly used to evaluate the error between two signals, and some researchers use the RMSE to evaluate the accuracy of estimation from EMG signals, e.g., [23,[32][33][34]. The RMSE can be calculated using the estimated data vectors {x 1 , x 2 ,…, x N } and the ground truth data vectors {y 1 , y 2 ,…, y N } as follows:…”

Review of EMG-based neuromuscular interfaces for rehabilitation: elbow joint as an example

“…EMG can be used to recognise human movement patterns, especially in the joint motion identification of the upper and lower limb (Pau et al, 2012a;Manal et al, 2002;Lloyd and Besier, 2003;Feng et al, 1999). The recognition results have already been widely used in the control strategy of humanoid mechanical and artificial prosthesis.…”

“…Based on this human biological system and muscle physiology, a skeleton model can be established to predict joint movement. It combines with the muscle physiology model (which uses elastoplastic elements to simulate human muscle, tendon and joints) and skeleton model (which uses rigid body to represent human skeleton structure), so it also been called neuromuscular interface (NI) (Pau et al, 2012a(Pau et al, , 2012b. Since this kind of model is simpler and faster in calculating, researchers choose it to realise real-time possessing.…”

“…This review mainly covers the development of EMG-based neuromuscular interfaces (Pau et al, 2010(Pau et al, , 2012a(Pau et al, , 2012b) and related fields. Section 2 briefly gives an overview of the biological system as a biological underpinning for our research.…”