Catherine Disselhorst-Klug

Purpose: This work analyzes how the Central Nervous System (CNS) adapts its control strategies—muscle synergies and muscular coactivation—during unfamiliar elbow flexion/extension tasks at different velocities. Methods: Twenty healthy participants (10 male; 10 female; age 31 ± 10,2 years) were recruited. Muscular activation of the biceps brachii, brachioradialis, and triceps brachii was recorded using surface electromyography. Elbow movements were tracked using a motion-capture system and an upper body biomechanical model. To represent an unfamiliar task, participants performed the movement in the transverse plane, while the familiar task was performed in the sagittal plane to allow for comparison. Movements were executed at different angular velocities to assess their effect. Muscle synergies were identified using the Non-Negative Matrix Factorization method. Results: The results indicate that the CNS adapts to unfamiliar movements primarily by increasing muscular coactivation to control position and movement velocity (p < 0.001, comparing familiar versus unfamiliar tasks). In contrast, during familiar tasks, the CNS achieves the stability required for faster movements through a higher contribution of muscle synergies (p < 0.05, comparing slowest versus fastest velocity). The statistical results revealed no significant interaction between task familiarity and movement velocity, suggesting that the effect of task familiarity on muscular activation remains consistent across all angular velocities. Conclusion: This work provides valuable insights into how muscle synergies and muscular coactivation complement each other. For an unfamiliar elbow flexion/extension task, the CNS primarily adapts by increasing the activation of all muscles acting on the joint to control position and movement velocity.

For half a century, the Mallet Scale (MS) has been utilized to assess upper limb function in patients with obstetric brachial plexus palsy (OBPP). However, the correct use of the MS requires trained personnel and the MS does not measure compensatory movements. For this reason, new methods are needed to compensate for these weaknesses. This study introduces an innovative method for objective functional motion analysis using accelerometers to measure upper limb movements in thirty patients with obstetric brachial plexus lesions. Five triaxial accelerometers were positioned on the chest and each upper limb. They recorded acceleration signals during repetitive everyday tasks: hand-to-mouth (HM), hand-to-neck (HN), and hand-to-spine (HS). From these signals, 54 features were extracted and subjected to linear correlation tests to identify 5 suitable features. An algorithm was then developed to categorize patients into five groups and compute an individual movement performance score (iMPScore) assessing the patient’s upper extremity function. By using the iMPScore more than 75% of all participants have been classified correctly with respect to their MS category. Identification of MS I category patients in general and assessing upper extremity function of MS I to III in HS tasks were most challenging. We conclude that the introduced approach is a valuable tool for gauging movement limitation of upper limbs in patients with obstetric brachial plexus palsy. Compared to other clinically established methods, it becomes possible to record and even quantify the extent of compensatory movements. In this way, an objective, user- and patient-friendly method is offered, which supports significantly physicians and therapists in their evaluation of OBPP.

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

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.

Wearable devices play an increasing role in the rehabilitation of patients with movement disorders. Although information about muscular activation is highly interesting, no approach exists that allows reliable collection of this information when the sensor is applied autonomously by the patient. This paper aims to demonstrate the proof-of-principle of an innovative sEMG sensor system, which can be used intuitively by patients while detecting their muscular activation with sufficient accuracy. The sEMG sensor system utilizes a multichannel approach based on 16 sEMG leads arranged circularly around the limb. Its design enables a stable contact between the skin surface and the system’s dry electrodes, fulfills the SENIAM recommendations regarding the electrode size and inter-electrode distance and facilitates a high temporal resolution. The proof-of-principle was demonstrated by elbow flexion/extension movements of 10 subjects, proving that it has root mean square values and a signal-to-noise ratio comparable to commercial systems based on pre-gelled electrodes. Furthermore, it can be easily placed and removed by patients with reduced arm function and without detailed knowledge about the exact positioning of the sEMG electrodes. With its features, the demonstration of the sEMG sensor system’s proof-of-principle positions it as a wearable device that has the potential to monitor muscular activation in home and community settings.

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.