A Surface Emg Driven Musculoskeletal Model of the Elbow Flexion-Extension Movement in Normal Subjects and in Subjects With Spasticity

Abstract

Spasticity often interferes with function, limits independence and may cause considerable disability. Elbow joint movement is involved in many daily living activities. A surface EMG driven musculoskeletal model was developed to predict joint trajectory and to compare the differences in the model parameters between the normal and spastic subjects. Three musculotendon actuators whose EMG could be assessed by surface electrodes (biceps, brachioradialis and triceps) were included in this musculoskeletal model. The proposed model took several sets of parameters (anthropometric parameters of the skeleton and muscle parameters) as inputs. Surface EMG signals of the three muscle groups were rectified, moving-averaged, scaled and converted to active states. These active states together with the initial angular position and velocity of the joint were also used as inputs for the model. The outputs were muscle forces and the trajectory of the elbow joint. Two groups of parameters, namely, maximal isometric muscle stress and electromechanical delay were estimated using the trajectory fitting algorithm. Results indicated that the model was successful in using the surface EMG as input signals in the prediction of elbow joint trajectory. The spastic subjects showed a lower maximum isometric muscle stress and longer electromechanical delay.

Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow

“…Therefore, indirect methods based on mathematical models are widely used. Four major approaches including static optimization [3], dynamic optimization [50], neural network [54], and EMG assisted method [18,38] have been used to estimate individual muscle forces. For the static optimization, an objective function such as minimizing the muscle stress or muscle activation is employed at each instant of a motor task to partition the net joint moments calculated by inverse dynamics into respective muscular components.…”

“…Using EMG signals as inputs to a musculoskeletal model to estimate individual muscle forces have been used by several groups for either isometric or dynamic tasks at different anatomical locations such as elbow [7,16,18,31,55], shoulder [35], knee [38][39][40]49,56], ankle [25][26][27][28][29]49], jaw [52], lower back [12,22,42,43,48], and wrist [9]. The theoretical basis behind EMG driven model is that if the EMG signals can be measured precisely and processed adequately to reflect the activation of each muscle crossing the joint and if the activation can be modulated properly by anatomical and musculotendon models, it is possible to accurately estimate individual muscle forces over a wide range of tasks and contraction modes.…”

Upper limb musculo-skeletal model for biomechanical investigation of elbow flexion movement

“…Various models of the whole or partial upper extremity have been developed over the last decade. These models have been used to study the static effects of muscle action and there are four major approaches including static optimization [2] , dynamic optimization [3] , neural network [4] , and electromyography (EMG) [5][6] assisted method to estimate the individual muscle forces. Using EMG signals as inputs to a musculoskeletal model to estimate the individual muscle forces has recently been used by several groups at different anatomical locations such as elbow [7] , shoulder, knee, ankle, jaw, lower back and wrist.…”

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.…”