Advances in EMG measurement techniques, analysis procedures, and the impact of muscle mechanics on future requirements for the methodology.

Muscular coordination enables locomotion and interaction with the environment. For more than 50 years electromyography (EMG) has provided insights into the central nervous system control of individual muscles or muscle groups, enabling both fine and gross motor functions. This information is available either at individual motor units (Mus) level or on a more global level from the coordination of different muscles or muscle groups. In particular, non-invasive EMG methods such as surface EMG (sEMG) or, more recently, spatial mapping methods (High-Density EMG - HDsEMG) have found their place in research into biomechanics, sport and exercise, ergonomics, rehabilitation, diagnostics, and increasingly for the control of technical devices. With further technical advances and a growing understanding of the relationship between EMG and movement task execution, it is expected that with time, especially non-invasive EMG methods will become increasingly important in movement sciences. However, while the total number of publications per year on non-invasive EMG methods is growing exponentially, the number of publications on this topic in journals with a scope in movement sciences has stagnated in the last decade. This review paper contextualizes non-invasive EMG development over the last 50 years, highlighting methodological progress. Changes in research topics related to non-invasive EMG were identified. Today non-invasive EMG procedures are increasingly used to control technical devices, where muscle mechanics have a minor influence. In movement science, however, the effect of muscle mechanics on the EMG signal cannot be neglected. This explains why non-invasive EMG's relevance in movement sciences has not developed as expected.

Detection of Typical Compensatory Movements during Autonomously Performed Exercises Preventing Low Back Pain (LBP).

With the growing number of people seeking medical advice due to low back pain (LBP), individualised physiotherapeutic rehabilitation is becoming increasingly relevant. Thirty volunteers were asked to perform three typical LBP rehabilitation exercises (Prone-Rocking, Bird-Dog and Rowing) in two categories: clinically prescribed exercise (CPE) and typical compensatory movement (TCM). Three inertial sensors were used to detect the movement of the back during exercise performance and thus generate a dataset that is used to develop an algorithm that detects typical compensatory movements in autonomously performed LBP exercises. The best feature combinations out of 50 derived features displaying the highest capacity to differentiate between CPE and TCM in each exercise were determined. For classifying exercise movements as CPE or TCM, a binary decision tree was trained with the best performing features. The results showed that the trained classifier is able to distinguish CPE from TCM in Bird-Dog, Prone-Rocking and Rowing with up to 97.7% (Head Sensor, one feature), 98.9% (Upper back Sensor, one feature) and 80.5% (Upper back Sensor, two features) using only one sensor. Thus, as a proof-of-concept, the introduced classification models can be used to detect typical compensatory movements in autonomously performed LBP exercises.

Non-invasive assessment of motor unit activation in relation to motor neuron level and lesion location in stroke and spinal muscular atrophy.

BACKGROUND: Neuromuscular disorders e.g. spinal muscular atrophy and stroke have a negative impact on functional movement capability. These disorders affect lower and upper motor neurons respectively. METHODS: In this study high spatial resolution electromyography was used to record the motor unit activity in 3 groups: healthy subjects, a spinal muscular atrophy group and a stroke group. 7 clinically sensitive parameters were used to analyze the activation patterns of a few motor units. FINDINGS: In the case of spinal muscular atrophy there was no effect on motor unit activation but on their number. Stroke was characterized by fewer active motor units and a significantly reduced firing rate with low variability. INTERPRETATION: The results suggest, that for stroke, information from the brain is modified thereby resulting in motor units firing at their natural frequency. Thus, high spatial resolution electromyography and the chosen parameters facilitate non-invasive, objective differentiation and analysis of the activation patterns of motor units in neuromuscular disorders.

Surface Electromyography Meets Biomechanics: Correct Interpretation of sEMG-Signals in Neuro-Rehabilitation Needs Biomechanical Input.

Coordinated activation of muscles is the basis for human locomotion. Impaired muscular activation is related to poor movement performance and disability. To restore movement performance, information about the subject's individual muscular activation is of high relevance. Surface electromyography (sEMG) allows the pain-free assessment of muscular activation and many ready-to-use technologies are available. They enable the usage of sEMG measurements in several applications. However, due to the fact that in most rehabilitation applications dynamic conditions are analyzed, the correct interpretation of sEMG signals remains difficult which hinders the spread of sEMG in clinical applications. From biomechanics it is well-known that the sEMG signal depends on muscle fiber length, contraction velocity, contraction type and on the muscle's biomechanical moment. In non-isometric conditions these biomechanical factors have to be considered when analyzing sEMG signals. Additionally, the central nervous system control strategies used to activate synergistic and antagonistic muscles have to be taken into consideration. These central nervous system activation strategies are rarely known in physiology and are hard to manage in pathology. In this perspective report we discuss how the consideration of biomechanical factors leads to more reliable information extraction from sEMG signals and how the limitations of sEMG can be overcome in dynamic conditions. This is a prerequisite if the use of sEMG in rehabilitation applications is to extend. Examples will be given showing how the integration of biomechanical knowledge into the interpretation of sEMG helps to identify the central nervous system activation strategies involved and leads to relevant clinical information.

Comparison of Muscular Activity and Movement Performance in Robot-Assisted and Freely Performed Exercises.

End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus the recovery of movement can only be guaranteed if the physiological muscular activation and movement performance are influenced as little as possible by the robot itself. Yet, this relation has not been investigated in the literature. Therefore, 20 healthy subjects performed free and robot-assisted exercises under different control settings supported by an end-effector-based system. The control settings differed concerning changes in the end-effector velocity and the stiffness of the robot joints. During the exercises, data from inertial measurement unit sensors, robot kinematics, and surface electromyography were collected for the upper limbs. The results showed an increase in muscular activity during robot-assisted movements compared to freely performed movements and also differences in movement performance. The change of the control setting influenced the muscular activation, but not the movement performance. The results of the study revealed that the robot could not be regarded as only a passive element. This should be kept in mind in future robotic rehabilitation systems in order to reduce the influences of the robot itself and thus to optimize the therapy.

Robot-based methodology for a kinematic and kinetic analysis of unconstrained, but reproducible upper extremity movement.

Although arm movements play an important role in everyday life, there is still a lack of procedures for the analysis of upper extremity movement. The main problems for standardizing the procedure are the variety of arm movements and the difficult assessment of external hand forces. The first problem requires the predefinition of motions, and the second one is the prerequisite for calculation of net joint forces and torques arising during motion. A new methodology for measuring external forces during prespecified, reproducible upper extremity movement has been introduced and validated. A robot-arm has been used to define the motion and 6 degrees of freedom (DoF) force sensor has been attached to it for acquiring the external loads acting on the arm. Additionally, force feedback has been used to help keeping external loads constant. Intra-individual reproducibility of joint angles was estimated by using correlation coefficients to compare a goal-directed movement with robot-guided task. Inter-individual reproducibility has been evaluated by using the mean standard deviation of joint angles for both types of movement. The results showed that both inter- and intra-individual reproducibility have significantly improved by using the robot. Also, the effectiveness of using force feedback for keeping a constant external load has been shown. This makes it possible to estimate net joint forces and torques which are important biomechanical information in motion analysis.

Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.

Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.

An upper body model for the kinematical analysis of the joint chain of the human arm.

The diagnosis and treatment of many orthopaedic and neurological disorders can benefit from movement analysis of the upper extremities by facilitating an objective and quantitative assessment of the compensatory movement strategies of patients. In this paper, a procedure for upper extremity movement analysis is introduced, which allows the simultaneous measurement of movement in all anatomical axes of the kinematical joint chain of the upper body. In first clinical applications it was shown that the procedure facilitates the detection of pathological movement patterns and therefore, adds significantly to the understanding of upper extremity movement strategies.