Visual Feedback System Research Articles

Lower extremity coordination during walking in persons who are blind and sighted controls: A preliminary report

Walking is the most common mode of physical activity for individuals who are blind. However, this population tends to be physically inactive, possibly due to alterations in coordination patterns during walking. Therefore, the purpose of this study was to examine lower extremity coordination patterns during walking in persons who are blind, and age-, sex-, and body mass index–matched sighted controls. Five persons who are blind performed level walking independently (with a cane) and with a human guide. Sighted controls walked at matched speeds for both conditions. A 10-camera motion capture system was used to record segmental kinematics during both walking conditions. Angle–angle plots and modified vector coding was used to present inter-limb (left/right thigh) and intra-limb (ankle–hip, ankle–knee, and knee–hip) couplings across both walking conditions for each group. Frequency of coupling patterns was compared between groups using Mann–Whitney’s U tests. Inter- and intra-limb coordination patterns were similar between both groups during independent and guided walking conditions (all p > .05). Angle–angle plots depict reduced segmental and joint motion in persons who are blind compared with sighted controls. Although the visual feedback system is integral for coordination during complex tasks, persons who are blind perform level walking with similar lower extremity coordination patterns to sighted controls. Reductions in spatiotemporal and range of motion are likely linked to a more hesitant stepping pattern due to unfamiliarity with the environment.

A two-pronged approach to reduce noise levels in the neonatal intensive care unit

ObjectivesThe aim of this study was to assess the efficacy of a visual noise feedback system and “quiet time” in reducing noise levels in the neonatal intensive care unit (NICU). DesignA prospective cross-sectional study was performed in a combined level II/III NICU at a Canadian tertiary care hospital. Noise levels were recorded continuously for three weeks without and then three weeks with visual noise feedback system. Noise levels were compared after one year of using visual feedback, and subsequently with the addition of two “quiet times.” ResultsVisual feedback reduced noise levels from 54.2 dB (95% CI 53.8–54.7 dB) to 49.4 dB (95% CI 48.9–49.8 dB; P < 0.0001) and increased the amount of time spent under 45 dB from 0 to 25% (P < 0.0001) after three weeks of use. However, this effect was not sustained at one year of visual feedback, with noise levels at 54.7 dB (95% CI 54.5–55.0 dB, P = 0.55). Quiet Time did not further reduce daily noise in the NICU (average noise levels 54.7, 95% CI 54.4–55.0 dB, P = 0.836). ConclusionsWhile visual noise feedback system reduced noise levels in the short term, these effects were not sustainable at one year and could not be remediated with the addition of a Quiet Time initiative. Continuing education regarding the detrimental effects of noise is paramount to ensure persistent noise reduction in the NICU.

Manual ventilation quality is improved with a real-time visual feedback system during simulated resuscitation

IntroductionManual ventilations during cardiac arrest are frequently performed outside of recommended guidelines. Real-time feedback has been shown to improve chest compression quality, but the use of feedback to guide ventilation volume and rate has not been studied. The purpose of this study was to determine whether the use of a real-time visual feedback system for ventilation volume and rate improves manual ventilation quality during simulated cardiac arrest.MethodsTeams of 2 emergency medical technicians (EMTs) performed two 8-min rounds of cardiopulmonary resuscitation (CPR) on a manikin during a simulated cardiac arrest scenario with one EMT performing ventilations while the other performed compressions. The EMTs switched roles every 2 min. During the first round of CPR, ventilation and chest compression feedback was disabled on a monitor/defibrillator. Following a 20-min rest period and a brief session to familiarize the EMTs with the feedback technology, the trial was repeated with feedback enabled. The primary outcome variables for the study were ventilations and chest compressions within target. Ventilation rate (target, 8–10 breaths/minute) and tidal volume (target, 425–575 ml) were measured using a novel differential pressure-based flow sensor. Data were analyzed using paired t tests.ResultsTen teams of 2 EMTs completed the study. Mean percentages of ventilations performed in target for rate (41% vs. 71%, p < 0.01), for volume (31% vs. 79%, p < 0.01), and for rate and volume together (10% vs. 63%, p < 0.01) were significantly greater with feedback.ConclusionThe use of a novel visual feedback system for ventilation quality increased the percentage of ventilations in target for rate and volume during simulated CPR. Real-time feedback to perform ventilations within recommended guidelines during cardiac arrest should be further investigated in human resuscitation.

Real-time Fuzzy Image Processing Method for Visual feedback of a Follower Robot

In this paper, an adaptive visual feedback system and controller has been designed and implemented in real-time to control the movements of a line follower robot to be smoother and faster. The robot consists of a couple of motorized wheels, the real-time controller and a CMOS camera as the only sensor for detecting line and feedback. The measurement based on real-time image processing and motor drive feedback used in this robot makes it robust to the obstacles and surface disturbances that may deviate robot. The image processing algorithm is adaptive to the line’s color and width too. Image processing techniques have been implemented in real-time to detect the line in the image frame and extract the necessary information (like line’s edge, coordinates and angle). A NI myRIO module is used as a stand-alone hardware unit and RT (Real-Time) target for implementation of controllers and image processing in LabVIEW environment. Both results of real-time and non-real-time implementation of controllers have been compared. To show the performance of real-time image processing in the control of this robot, three types of controllers (i.e. P, PI and Fuzzy controllers) have been implemented for line following tests and the results have been compared. At the end, it was found that the fuzzy controller controls the robot movements smoother, faster, with less errors and quicker response time compare to the other controllers

Sellick maneuver assisted real-time to achieve target force range in simulated environment-A prospective observational cross-sectional study on manikin.

A force sensor system was developed to give real-time visual feedback on a range of force. In a prospective observational cross-section study, twenty-two anaesthesia nurses applied cricoid pressure at a target range of 30–40 Newtons for 60 seconds in three sequential steps on manikin: Group A (step 1 blinded, no sensor), Group B (step 2 blinded sensor), Group C (step 3 sensor feedback). A weighing scale was placed below the manikin. This procedure was repeated once again at least 1 week apart. The feedback system used 3 different colours to indicate the force range achieved as below target, achieve target, above target. Significantly higher proportion of target cricoid pressure was achieved with the use of sensor feedback in Group C; 85.9% (95%CI: 82.7%-88.7%) compared to when blinded from sensor in Group B; 31.3% (95%CI: 27.4–35.4%). Cricoid force achieved blind (Group B) exceeded force achieved with feedback (Group C) by a mean of 8.0 (95%CI: 5.9–10.2, p<0.0001) and 6.2 (95%CI:4.1–8.3, p< 0.0001) Newtons in round 1 and 2 respectively. Weighing scale read lower than corresponding force sensor by a mean of 8.4 Newtons (95% CI: 7.1–9.7, p<0.0001) in group B and 5.8 Newtons (95% CI: 4.5–7.1, p<0.0001) in Group C. Force sensor visual feedback system enabled application of reproducible target cricoid pressure with less variability and has potential value in clinical use. Using weighing scale to quantify and train cricoid pressure requires a review. Understanding the force applied is the first step to make cricoid pressure a safe procedure.

Operation Planning and Closed-Loop Control of a Soft Robotic Table for Simultaneous Multiple-Object Manipulation

This article presents the design, development, and validation of a closed-loop control system for a soft robotic table. The soft table can provide independent manipulation to multiple objects simultaneously, which is an advantage over traditional devices such as robotic arms and belt conveyor systems. The table also provides intrinsic soft handling for delicate objects. In order to execute automation tasks, a vision feedback system and an operation controller are developed and incorporated into the existing table prototype to form a closed-loop control. The vision feedback system implements image processing techniques to identify the object location from captured images. The operation controller is integrated from three sets of algorithms, including movement planning, actuator selection, and actuation planning. Two sets of experiments were conducted to validate the closed-loop system and the capability of the soft table. The first set validated the manipulation task of transporting an object to a desired location with autonomous planning and execution. The second set validated the table’s ability to manipulate multiple objects simultaneously and independently. Videos of both experiments have been supplied. Note to Practitioners —A soft robotic table is developed for the manipulation of delicate objects on a surface. The table is intended for automated sorting tasks in an industrial environment. Multiple objects placed on the table surface can be simultaneously and independently transported and orientated on the $XY$ horizontal plane. The deformable surface of the table provides soft handling for delicate objects. These are the advantages over existing manipulators such as robotic arms and belt conveyor systems. Based on previously conducted experiments, it was found that the open-loop control is ineffective to execute automation tasks due to the variability in object movements with soft interactions. To solve this problem, a closed-loop control system for autonomous object manipulation is developed and its detailed design and implementation are presented in this article. A vision feedback system and a controller are developed to form the proposed closed-loop control. Two sets of experiments involving common manipulation tasks were conducted. The control system is proven to be effective at handling the uncertainties in object movements and autonomous object manipulation by the soft robotic table is validated. The performance of this control system can be improved with better predictions of both the deformations on the table surface and the resultant object movements. Embedding deformable sensors into the table surface is a potential approach to achieve such improvement and this can be investigated in future works.

A Visual Servo Based Predictive Control with Echo State Gaussian Process for Soft Bending Actuator

This article designed a soft bending actuator (SBA) and presented a neural-network-based tracking control strategy for such an actuator. To achieve high-precision control, a visual feedback system was established by using a high-speed camera to dynamically compute the corresponding central angle which described the curvature of the SBA. Considering its nonlinear features, the echo state Gaussian process, a fusion of echo state network and Gaussian process, was used to approximate the SBA's dynamics with a nonlinear autoregressive exogenous model. On this basis, a single-layer neural network was trained to calculate the control signal in light of the idea of predictive control, due to its simplicity and good approximation capabilities. The stability of the closed-loop system was guaranteed, and experimental results indicated that the proposed strategy showed relatively high tracking accuracy under various reference trajectories.

Improving the Attention Performance in High-Functioning Autistics during Memorization Lesson through Neurofeedback Training Approaches

Most students with high-functioning autism have difficulties in paying attention that may affect in memorizing lesson material which will impede their learning progress. Several researches have shown that neurofeedback training (NFT) is successful in training the autistics’ attention performance. Thus, the purpose of this study is to measure and compare the effectiveness of two versions of an invention using different NFT approaches on the attention performance of high-functioning autistic (HFA) during memorization of the lesson material. The results from this study showed that the ratio of students those categorized in high attention increased from 3:10 to 4:5 when using the version 2 learning tool which resulted in more effective in improving the HFAs’ attention performance. The findings of this study suggested that the invention of memorization tool was effective for the purpose of enhancing the HFAs’ attention performance during memorization lesson with the adaptation of NFT approaches; visual feedback, audio feedback, verbal response, and reward system.

High-Speed Visual Feedback Control of Miniature Rotating Mirror System Using a Micro Ultrasonic Motor

We propose a miniature rotating mirror system using a micro-ultrasonic motor that employs a 45° angle mirror as its rotor for enlarging the angle of view of cameras. The motor assy, measuring 2 mm in diameter and 4 mm in length, is one of the smallest motors and is expected for medical applications such as endoscopes. In this paper, we build a micromirror assembly, install it into a visual feedback system with an optical system, and demonstrate a motor control with a quick and wide angle-of-view observation. The high-speed camera vision embedded in the system measures the quick motion of the motor in real-time and enables accurate positioning of the mirror angle by p-control using burst waves. The proposed system demonstrates the visual feedback control of the miniature rotating mirror to the angle information obtained by the high-speed camera. The feasibility of the mirror system driven by the micro-ultrasonic motor is shown via demonstration.

Providing low-dimensional feedback of a high-dimensional movement allows for improved performance of a skilled walking task

Learning a skilled movement often requires changing multiple dimensions of movement in a coordinated manner. Serial training is one common approach to learning a new movement pattern, where each feature is learned in isolation from the others. Once one feature is learned, we move on to the next. However, when learning a complex movement pattern, serial training is not only laborious but can also be ineffective. Often, movement features are linked such that they cannot simply be added together as we progress through training. Thus, the ability to learn multiple features in parallel could make training faster and more effective. When using visual feedback as the tool for changing movement, however, such parallel training may increase the attentional load of training and impair performance. Here, we developed a novel visual feedback system that uses principal component analysis to weight four features of movement to create a simple one-dimensional ‘summary’ of performance. We used this feedback to teach healthy, young participants a modified walking pattern and compared their performance to those who received four concurrent streams of visual information to learn the same goal walking pattern. We demonstrated that those who used the principal component-based visual feedback improved their performance faster and to a greater extent compared to those who received concurrent feedback of all features. These results suggest that our novel principal component-based visual feedback provides a method for altering multiple features of movement toward a prescribed goal in an intuitive, low-dimensional manner.

Individualized feedback to change multiple gait deficits in chronic stroke

BackgroundWalking deficits in people post-stroke are often multiple and idiosyncratic in nature. Limited patient and therapist resources necessitate prioritization of deficits such that some may be left unaddressed. More efficient delivery of therapy may alleviate this challenge. Here, we look to determine the utility of a novel principal component-based visual feedback system that targets multiple, patient-specific features of gait in people post-stroke.MethodsTen individuals with stroke received two sessions of visual feedback to attain a walking goal. This goal consisted of bilateral knee and hip joint angles of a typical ‘healthy’ walking pattern. The feedback system uses principal component analysis (PCA) to algorithmically weight each of the input features so that participants received one stream of performance feedback. In the first session, participants had to explore different patterns to achieve the goal, and in the second session they were informed of the goal walking pattern. Ten healthy, age-matched individuals received the same paradigm, but with a hemiparetic goal (i.e. to produce the pattern of an exemplar stroke participant). This was to distinguish the extent to which performance limitations in stroke were due neurological injury or the PCA based visual feedback itself.ResultsPrincipal component-based visual feedback can differentially bias multiple features of walking toward a prescribed goal. On average, individuals with stroke typically improved performance via increased paretic knee and hip flexion, and did not perform better with explicit instruction. In contrast, healthy people performed better (i.e. could produce the desired exemplar stroke pattern) in both sessions, and were best with explicit instruction. Importantly, the feedback for stroke participants accommodated a heterogeneous set of walking deficits by individually weighting each feature based on baseline walking.ConclusionsPeople with and without stroke are able to use this novel visual feedback to train multiple, specific features of gait. Important for stroke, the PCA feedback allowed for targeting of patient-specific deficits. This feedback is flexible to any feature of walking in any plane of movement, thus providing a potential tool for therapists to simultaneously target multiple aberrant features of gait.

Multi-DoF continuous estimation for wrist torques using stacked autoencoder

Human machine interface (HMI) based on surface electromyography (sEMG) promises to provide an intuitive and noninvasive way to interact with peripheral equipments, such as prostheses, exoskeletons, and robots. Most recently, advances in machine learning, especially in deep learning algorithms, present the capabilities in constructing complicated mapping functions. In this study, we construct a stacked autoencoder-based deep neural network (SAE-DNN) to continuously estimate multiple degrees-of-freedom (DoFs) kinetics of wrist from sEMG signals. During the experiments, high-density sEMG signals and multi-DoF wrist torques were simultaneously acquired under the guidance of a visual feedback system, with eight healthy subjects and an amputee recruited. Moreover, the estimation performance of SAE-DNN was compared with two of commonly used conventional regressors, linear regression (LR) and support vector regression (SVR). As a consequence, the results demonstrate the feasibility of this scheme and significant superiority of SAE-DNN over LR and SVR with higher R2 values across all DoFs (SAE-DNN: 0.829±0.050, LR: 0.757±0.075, SVR: 0.751±0.079). The outcomes of this study provide us with a perspective and a feasible scheme for simultaneous and proportional control.

Performance enhancement of two-camera robotic system using adaptive gain approach

PurposeThis paper aims to improve the performance of a two-camera robotic feedback system designed for automatic pick and place application by modifying its velocity profile during switching of control.Design/methodology/approachCooperation of global and local vision sensors ensures visibility of the target for a two-camera robotic system. The master camera, monitoring the workspace, guides the robot such that image-based visual servoing (IBVS) by the eye-in-hand camera transcends its inherent shortcomings. A hybrid control law steers the robot until the system switches to IBVS in a region proven for its asymptotic stability and convergence through a qualitative overview of the scheme. Complementary gain factors can ensure a smooth transition in velocity during switching considering the versatility and range of the workspace.FindingsThe proposed strategy is verified through simulation studies and implemented on a 6-DOF industrial robot ABB IRB 1200 to validate the practicality of adaptive gain approach while switching in a hybrid visual feedback system. This approach can be extended to any control problem with uneven switching surfaces or coarse/fine controllers which are subjected to discrete time events.Practical implicationsIn complex workspace where robots operate in parallel with other robots/humans and share workspaces, the supervisory control scheme ensures convergence. This study proves that hybrid control laws are more effective than conventional approaches in unstructured environments and visibility constraints can be overcome by the integration of multiple vision sensors.Originality/valueThe supervisory control is designed to combine the visual feedback data from eye-in-hand and eye-to-hand sensors. A gain adaptive approach smoothens the velocity characteristics of the end-effector while switching the control from master camera to the end-effector camera.

Improved Classification Scheme Using Fused Wavelet Packet Transform Based Features for Intelligent Myoelectric Prostheses

Electromyography (EMG) signal is gaining popularity to developn intelligent bionics and prosthetic devices using machine learning techniques. Feature extraction is essential step for the EMG pattern recognition based application. In this article, a fused wavelet packet transform based feature extraction approach is proposed for EMG pattern classification. Total nine subjects (six intact and three amputees) are recruited for the data acquisition. Data acquisition is performed by an ADS1298-based system with eight bipolar electrodes. Further 11 activities are performed by each subject at the time of EMG signal recording including lateral grasp, cylindrical grasp, spherical grasp, and grasp with force. The visual feedback system is utilized for EMG signal acquisition of amputees. The comparison of commonly used wavelet transform based features and proposed fused wavelet transform based features is also presented with respect to classification accuracy and time complexity. The proposed method exhibits highest classification accuracy up to 98.32% for the amputees using discriminant analysis classification with marginal variation in time complexity. Similar trends in results are observed when standard dataset (NinaPro) has been utilized. The results validate the enhanced performance of the proposed technique over conventional counterparts.

Technical feasibility of constant-load and high-intensity interval training for cardiopulmonary conditioning using a re-engineered dynamic leg press

BackgroundLeg-press devices are one of the most widely used training tools for musculoskeletal strengthening of the lower-limbs, and have demonstrated important cardiopulmonary benefits for healthy and patient populations. Further engineering development was done on a dynamic leg-press for work-rate estimation by integrating force and motion sensors, power calculation and a visual feedback system for volitional work-rate control. This study aimed to assess the feasibility of the enhanced dynamic leg press for cardiopulmonary exercise training in constant-load training and high-intensity interval training. Five healthy participants aged 31.0±3.9 years (mean ± standard deviation) performed two cardiopulmonary training sessions: constant-load training and high-intensity interval training. Participants carried out the training sessions at a work rate that corresponds to their first ventilatory threshold for constant-load training, and their second ventilatory threshold for high-intensity interval training.ResultsAll participants tolerated both training protocols, and could complete the training sessions with no complications. Substantial cardiopulmonary responses were observed. The difference between mean oxygen uptake and target oxygen uptake was 0.07±0.34 L/min (103 ±17%) during constant-load training, and 0.35±0.66 L/min (113 ±27%) during high-intensity interval training. The difference between mean heart rate and target heart rate was −7±19 bpm (94 ±15%) during constant-load training, and 4.2±16 bpm (103 ±12%) during high-intensity interval training.ConclusionsThe enhanced dynamic leg press was found to be feasible for cardiopulmonary exercise training, and for exercise prescription for different training programmes based on the ventilatory thresholds.

Automatic System for the Blastocyst Embryo Manipulation and Rotation.

Cell manipulation plays a vital role in the success rate and efficiency of the cell microsurgical operations, including biopsy of cell internal organelles such as the embryo biopsy, in which the embryo is manipulated and reoriented safely to a predefined desired position and orientation. In this paper, a simplified approach for the blastocyst embryo reorientation is proposed. It utilizes conventional tools and techniques currently in use in manual approaches in research labs and In Vitro Fertilization clinics, and controls the process using a vision feedback system. An experimental setup is developed to verify the dynamic behavior of the proposed approach, in which a stationary holding micropipette is used to hold the embryo, which is then rotated in two coordinate directions through friction contact with a moving substrate, in our case a glass microscope slide. The embryo rotates on the holding micropipette tip, due to the relatively low friction of this contact. A computer vision algorithm is used to estimate the embryo orientation coordinates, and use this information as a feedback signal to a simple proportional controller to control the embryo rotation angle. Experimental results demonstrate that the system is capable of cell rotation in two independent coordinates, suitable for embryo microsurgical task execution.

Framework for visual-feedback training based on a modified self-organizing map to imitate complex motion

The goal of this research was to develop a visual-feedback system, based on motion sensing and computational technologies, to help athletes and patients imitate desired motor skills. To accomplish this objective, the authors used a self-organizing map to visualize high-dimensional, time-series motion data. The cyclic motion of one expert and five non-experts was captured as they pedaled a bicycle ergometer. A self-organizing map algorithm was used to display the corresponding circular motion trajectories on a two-dimensional motor skills map. The non-experts modified their motion to make their real-time motion trajectory approach that of the expert, thereby training themselves to imitate the expert motion. The root mean square error, which represents the difference between the non-expert motion and the expert motion, was significantly reduced upon using the proposed visual-feedback system. This indicates that the non-expert subjects successfully approximated the expert motion by repeated comparison of their trajectories on the motor skills map with that of the expert. The results demonstrate that the self-organizing map algorithm provides a unique way to visualize human movement and greatly facilitates the task of imitating a desired motion. By capturing the appropriate movements for display in the visual-feedback system, the proposed framework may be adopted for sports training or clinical practice.

Modeling and Analysis of a Visual Feedback System to Support Efficient Object Grasping of an EMG-Controlled Prosthetic Hand

Abstract Millions of people around the world have lost their upper limbs mainly due to accidents and wars. Recently in the Middle East, the demand for prosthetic limbs has increased dramatically due to ongoing wars in the region. Commercially available prosthetic limbs are expensive while the most economical method available for controlling prosthetic limbs is the Electromyography (EMG). Researchers on EMG-controlled prosthetic limbs are facing several challenges, which include efficiency problems in terms of functionality especially in prosthetic hands. A major issue that needs to be solved is the fact that currently available low-cost EMG-controlled prosthetic hands cannot enable the user to grasp various types of objects in various shapes, and cannot provide the efficient use of the object by deciding the necessary hand gesture. In this paper, a computer vision-based mechanism is proposed with the purpose of detecting and recognizing objects and applying optimal hand gesture through visual feedback. The objects are classified into groups and the optimal hand gesture to grasp and use the targeted object that is most efficient for the user is implemented. A simulation model of the human hand kinematics is developed for simulation tests to reveal the efficacy of the proposed method. 80 different types of objects are detected, recognized, and classified for simulation tests, which can be realized by using two electrodes supplying the input to perform the action. Simulation results reveal the performance of proposed EMG-controlled prosthetic hand in maintaining optimal hand gestures in computer environment. Results are promising to help disabled people handle and use objects more efficiently without higher costs.

Visual kinematic feedback enhances the execution of a novel knee flexion gait pattern in children and adolescents

BackgroundAltered knee motion is one of the most common gait deviations in pediatric populations with gait disorders. The potential for pediatric gait retraining using visual feedback based on knee kinematic patterns is under-explored. Research questionThis study investigated whether pediatric participants could successfully modify knee flexion patterns in response to a visual kinematic feedback system (VKFS). MethodsKnee flexion angles from twelve typically developing children and adolescents (6 M, 6 F; 11.9 ± 2.7 years) were calculated using wearable inertial measurement units. Participants were tested while walking on a treadmill using pattern based visual feedback (FB). Four novel target patterns which amplified or attenuated swing phase peak knee flexion were tested. No feedback (NFB) tests assessed the participant’s ability to independently reproduce the patterns. Mean absolute cycle error (MACE) and magnitude of peak knee flexion error (PK) were calculated during the last 10 strides of FB and NFB trials. Pre-exposure reference values (R) were also calculated. Results and significancePK-FB was significantly smaller (p < 0.05) than PK-R for all targets. Average values for PK-NFB were higher than for PK-FB, although PK-NFB remained significantly lower than PK-R for two targets. Contrary to one of the study’s hypotheses, MACE-FB and MACE-NFB were larger than MACE-R.The study provided evidence that pediatric participants were able to modify peak knee flexion during gait in the sense targeted by the VKFS. Analysis suggested that MACE increases were explained by increases in gait cycle deviation outside of the changed region.

Evaluation of a Sound Quality Visual Feedback System for Bow Learning Technique in Violin Beginners: An EEG Study.

Current music technologies can assist in the process of learning to play a musical instrument and provide objective measures for evaluating the improvement of music students in concrete music tasks. In this paper, we investigated the effects of a sound quality visual feedback system (SQVFS) in violin learning. In particular, we studied the EEG activity of a group of participants with no previous violin playing experience while they learned to produce a stable sound (regarding pitch, dynamics, and timbre) in order to find motor learning biomarkers in a music task. Eighteen subjects with no prior experience in violin playing were divided into two groups: participants in the first group (experimental group, N = 9) practiced with instructional videos and offline feedback from the SQVFS provided in alternation with their performance, while participants in a second group (control group, N = 9) practiced with the instructional videos only. A third group of violin experts (players with more than 6 years of experience) performed the same task for comparative purposes (N = 7). All participants were asked to perform 20 trials (4 blocks of 5 trials) consisting of a violin bowing exercise while their EEG activity and their produced sound was recorded. Significant sound quality improvements along the session were found in all participants with the exception of participants in the expert group. In addition, participants in the experimental group showed increased interest in the learning process and significant improvement after the second block not present in the control group. A significant correlation between the levels of frontal gamma band power and the sound improvement along the task was found in both the experimental and control group. This result is consistent with the temporal binding model which associates gamma band power with the role of integrating (binding) information processed in distributed cortical areas. Task complexity demands more cognitive resources, more binding and thus, gamma band power enhancement, which may be reduced as the demanded task begins to be automated as it is likely to be the case in both beginners groups.