Task-oriented control of muscle coordination during cycling

Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck

Silent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.

Addition of a Cognitive Task During Walking Alters Lower Body Muscle Activity

This study compared electromyography of five leg muscles during a single walking task (WALK) to a dual task (walking + cognitive task; COG) in 40 individuals (20 M and 20 F) using a wavelet analysis technique. It was hypothesized that muscle activation during the dual task would differ significantly from the walking task with respect to both timing (H1) and frequency (H2). The mean overall intensity for the COG trials was 4.1% lower for the tibialis anterior and 5.5% higher for the gastrocnemius medialis than in the WALK trials. The changes between the WALK and COG trials were short 50 ms bursts that occurred within 100 ms of heel strike in the tibialis anterior, and longer activation periods during the stance phase in the gastrocnemius medialis. No changes in overall intensity were observed in the peroneus longus, gastrocnemius lateralis, or soleus. Furthermore, no clear frequency bands within the signal could further characterize the overall changes in muscle activity during the COG task. This advances our understanding of how the division of attentional resources affects muscle activity in a healthy population of adults.

Implications of Optimal Feedback Control Theory for Sport Coaching and Motor Learning: A Systematic Review

Best practice in skill acquisition has been informed by motor control theories. The main aim of this study is to screen existing literature on a relatively novel theory, Optimal Feedback Control Theory (OFCT), and to assess how OFCT concepts can be applied in sports and motor learning research. Based on 51 included studies with on average a high methodological quality, we found that different types of training seem to appeal to different control processes within OFCT. The minimum intervention principle (founded in OFCT) was used in many of the reviewed studies, and further investigation might lead to further improvements in sport skill acquisition. However, considering the homogenous nature of the tasks included in the reviewed studies, these ideas and their generalizability should be tested in future studies.

Comparison of Joint Kinematics in Transition Running and Isolated Running in Elite Triathletes in Overground Conditions

Triathletes often experience incoordination at the start of a transition run (TR); this is possibly reflected by altered joint kinematics. In this study, the first 20 steps of a run after a warm-up run (WR) and TR (following a 90 min cycling session) of 16 elite, male, long-distance triathletes (31.3 ± 5.4 years old) were compared. Measurements were executed on the competition course of the Ironman Frankfurt in Germany. Pacing and slipstream were provided by a cyclist in front of the runner. Kinematic data of the trunk and leg joints, step length, and step rate were obtained using the MVN Link inertial motion capture system by Xsens. Statistical parametric mapping was used to compare the active leg (AL) and passive leg (PL) phases of the WR and TR. In the TR, more spinal extension (~0.5–1°; p = 0.001) and rotation (~0.2–0.5°; p = 0.001–0.004), increases in hip flexion (~3°; ~65% AL−~55% PL; p = 0.001–0.004), internal hip rotation (~2.5°; AL + ~0–30% PL; p = 0.001–0.024), more knee adduction (~1°; ~80–95% AL; p = 0.001), and complex altered knee flexion patterns (~2–4°; AL + PL; p = 0.001–0.01) occurred. Complex kinematic differences between a WR and a TR were detected. This contributes to a better understanding of the incoordination in transition running.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes' performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes' performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

How Do the Mechanical Demands of Cycling Affect the Information Content of the EMG?

PURPOSE

The persistence of phase-related information in EMG signals can be quantified by its entropic half-life (EnHL). It has been proposed that the EnHL would increase with the demands of a movement task, and thus increase as the pedaling power increased during cycling. However, simulation work on the properties of EMG signals suggests that the EnHL depends on burst duration and duty cycle in the EMG that may not be related to task demands. This study aimed to distinguish between these alternate hypotheses.

METHODS

The EnHL was characterized for 10 muscles from nine cyclists cycling at a range of powers (35 to 260 W) and cadences (60-140 rpm) for the raw EMG, phase-randomized surrogate EMG, EMG intensity, and the principal components describing the muscle coordination patterns.

RESULTS

There was phase-related information in the raw EMG signals and EMG intensities that was related to the EMG burst duration, duty cycle pedaling cadence, and power. The EnHL for the EMG intensities of the individual muscles (excluding quadriceps) and for the coordination patterns decreased as cycling power and cadence increased.

CONCLUSIONS

The EnHL provide information on the structure of the motor control signals and their constituent motor unit action potentials, both within and between muscles, rather than on the mechanical demands of the cycling task per se.

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human-Machine Interface Control

Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.

Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review

In the last years, several studies have been focused on understanding how the central nervous system controls muscles to perform a specific motor task. Although it still remains an open question, muscle synergies have come to be an appealing theory to explain the modular organization of the central nervous system. Even though the neural encoding of muscle synergies remains controversial, a large number of papers demonstrated that muscle synergies are robust across different tested conditions, which are within a day, between days, within a single subject, and between subjects that have similar demographic characteristics. Thus, muscle synergy theory has been largely used in several research fields, such as clinics, robotics, and sports. The present systematical review aims at providing an overview on the applications of muscle synergy theory in clinics, robotics, and sports; in particular, the review is focused on the papers that provide tangible information for (i) diagnosis or pathology assessment in clinics, (ii) robot-control design in robotics, and (iii) athletes' performance assessment or training guidelines in sports.

Neuromuscular Strategies during Cycling at Different Muscular Demands

PURPOSE

This study investigated muscle coordination while pedaling at 150 and 300 W with a cadence of 90 rpm. Changes in the variability of the electromyographic (EMG) signals were quantified in 14 subjects.

METHODS

Principal component analysis was used to find correlated EMG patterns among seven leg muscles that reflect neuromuscular strategies while pedaling. Sample entropy was used to assess the regularity of the short-term fluctuations of the EMG. Signal structure relates to the autocorrelation and to the information in the phase of the signal. This study used the information encrypted in the phase to quantify neuromuscular control and compared the results to phase-randomized surrogate data.

RESULTS

Although the pattern remained similar, the correlation between individual muscles showed effort-dependent differences. Increased workload altered the overall neuromuscular strategy indicated by changes in the contribution of individual muscles to the movement. Additionally, the executed strategy was characterized by increased structure. Regularity of the short-term fluctuations in the EMG increased significantly with effort level. Both experimental conditions showed more structure in the phase of the EMG compared to the surrogate data.

CONCLUSIONS

This increased structure in the EMG signal may represent a less random and more orderly recruited firing pattern during the pedaling task at higher effort levels.

Intrinsic movement variability at work. How long is the path from motor control to design engineering?

For several years, increasing numbers of studies have highlighted the existence of movement variability. Before that, it was neglected in movement analysis and it is still almost completely ignored in workstation design. This article reviews motor control theories and factors influencing movement execution, and indicates how intrinsic movement variability is part of task completion. These background clarifications should help ergonomists and workstation designers to gain a better understanding of these concepts, which can then be used to improve design tools. We also question which techniques--kinematics, kinetics or muscular activity--and descriptors are most appropriate for describing intrinsic movement variability and for integration into design tools. By this way, simulations generated by designers for workstation design should be closer to the real movements performed by workers. This review emphasises the complexity of identifying, describing and processing intrinsic movement variability in occupational activities.

Changes in cortical activity measured with EEG during a high-intensity cycling exercise

This study investigated the effects of a high-intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from 10 experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time-to-exhaustion trial at 85% of their maximum power output on 2 subsequent days that were separated by ∼48 h. EEG was recorded using a 64-channel system at 500 Hz. Independent component (IC) analysis parsed the EEG scalp data into maximal ICs. An equivalent current dipole model was calculated for each IC, and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (P < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedaling cycle. The results indicate an involvement of the cerebral cortex during the pedaling task that most likely involves executive control function, as well as motor planning and execution.

Coordination among thigh muscles including the vastus intermedius and adductor magnus at different cycling intensities

Although many studies have been focused on muscle synergies in the lower limbs, synergies of the thigh muscles during cycling have not been investigated in detail. We examined synergies of the thigh muscles including the vastus intermedius (VI) and adductor magnus (AM) while cycling. Eight healthy men pedaled at 20%, 40%, 60%, 80% and 100% of maximal aerobic power output at a constant cadence of 60 rpm. Surface electromyography (EMG) recorded signals from the deep VI and the three superficial quadriceps femoris (QF) muscles, the two hamstrings and the AM. The root mean square of the EMG signal was averaged every 2° of crank rotation and normalized by the peak value for each muscle. We used factor analysis to assess normalized EMG recordings while cycling and to identify thigh muscle synergies. The VI, the superficial QF muscles and the AM dominated the first muscle synergy at all power output levels. The AM also formed a second synergy with the two hamstrings at all power output levels. These results suggest that the VI coordinates with the other QF and AM muscles, and that the AM coordinates with the QF and hamstring muscles while cycling.

Extraction of basic movement from whole-body movement, based on gait variability

The aim of this study was to quantify the step-to-step variability (SSV) in speed-variant and speed-invariant movement components of the whole-body gait pattern during running. These separate aspects of variability can be used to gain insight into the neuromuscular control strategies that are engaged during running. Ten healthy, physically active, male recreational athletes performed five treadmill running trials at five different speeds (range: 1.3–4.9 m/sec). The whole-body movement was separated into principal movements (PM) using a principal component analysis. The PMs were split into two groups: a speed-variant group, where the range of motion (amplitude of PMs) changed with running speed; and a speed-invariant group, where the range of motion was constant across various speeds. The step-to-step variability (SSV) of the two groups was then quantified. The absolute SSV was the summed variability across all gait cycles, whereas the relative SSV was the summed variability divided by the magnitude of the movement. The absolute SSV of the speed-variant movements increased with running speed. By contrast, the relative SSV of the speed-variant group (as normalized to the PM amplitude) decreased asymptotically toward a minimal level as running speed increased. Both the absolute and relative SSV of the speed-invariant movements revealed a minimum at 3.1 m/sec. The whole-body gait pattern during running can be subdivided into speed-variant and speed-invariant movements. An interpretation of the SSV based on minimal intervention theory suggests that speed-variant movements are more tightly controlled, as evidenced by a lower degree of variability compared to the speed-invariant movements.