Electrophysiological Biomarkers for the Assessment of Motor Efficiency in Sport

Many disciplines have approached the study of human motor behavior. The motor learning theory based on information processing proposes a learning loop through interaction between the external environment and the central nervous system. Different neuroscience fields and technological advances provide a new perspective for the intensive study of the intrinsic processes of motor behavior, which modify the most visible aspect: motor efficiency. The aim of the present review was to determine which cortical and muscular electrophysiological biomarkers available in the literature could be representative for the study and quantification of motor efficiency. In this review, a survey of the literature related to motor production has been performed. The continuous development of biological signal monitoring techniques has allowed to understand part of the communication methods of the central nervous system, the integration of neural networks, and the interaction between different anatomic structures through rhythmic patterns of discharge known as brain waves. Motor production has been characterized by detecting electrophysiological biomarkers, taking into account the connectivity that can be represented by the corticomuscular and intermuscular coherence indices in different frequency bands. The present work proposes an approach to use these biomarkers on beta-band (for muscle stability synergies) and gamma-band (for mobility synergies). These indices will allow establishing quantitative parameters for motor efficiency, which could improve the precision of sports assessment.

Transcranial magnetic stimulation of the occipital cortex interferes with foot movements in blind individuals

Plasticity after visual loss is a remarkable characteristic of the brain. Previous studies in blind individuals have shown that the occipital cortex, which corresponds to the visual cortex in sighted individuals, can be reorganized and repurposed for nonvisual perception and cognitive functions. To our knowledge, however, no studies have directly examined its involvement in motor production. Here we show that a rhythmic foot movement performed by acquired blind participants can be disrupted by transcranial magnetic stimulation (TMS) to their primary and secondary visual cortex (V1/V2). Variability of this foot movement increased when we applied TMS to the acquired blind participants. This effect of TMS was absent for both sighted and congenitally blind participants. These results suggest that the visual cortex of blind individuals is involved in motor production, but its involvement requires prior visual experience. Our finding indicates that functional repurposing of the visual cortex may not be restricted to perception and cognitive functions, but also extended to motor function. Motor function may emerge in the visual cortex of blind individuals as a consequence of the reorganization of the visuomotor network, which has been developed before visual loss.

Wavelet-based audio features of DC motor sound

The usage of wavelets is widespread in many fields nowadays, especially in signal processing. Their nature provides some advantages in comparison to the Fourier transform, and therefore many applications rely on wavelets rather than on other methods. The decomposition of wavelets into detail and approximation coefficients is one of the methods to extract representative audio features. They can be used in signal analysis and further classification. This paper investigates the usage of various wavelet families in the wavelet decomposition to extract audio features of direct current (DC) motor sounds recorded in the production environment. The purpose of feature representation and analysis is the detection of DC motor failures in motor production. The effects of applying different wavelet families and parameters in the decomposition process are studied using sounds of more than 60 motors. Time and frequency analysis is also done for the tested DC motor sounds.

Comparison Of Voltage Measurements on DC Gearbox Motor and PWM Voltage Based On Arduino Uno

In this research, a DC motor tested using the L298 motor driver which is controlled by Arduino as the motor speed regulator and using the PWM method as a speed control signal generator for the DC motor. This research has several stages, namely literature study, arduino-based dc motor design, Arduino-based dc motor production, Arduino-based dc motor testing, then analysis of research results and conclusions are drawn. Testing on this Arduino-bas, ed DC motor was done with several experiments, by adjust the motor speed by setting the pwm value in the program listing section then measuring the pwm output on pin 9 of the Arduino Uno board. Then measurements were made on the right and left side of the motor. This experiment was carried out by setting the pwm value from 15 to 255 to determine the difference in the increase in the PWM output voltage and determine the voltage on the motor and determine the condition of the DC motor. The test results show that the initial motor moves when the set value of PWM = 45, PWM voltage = 0.83 V, right motor voltage = 1.53 V, and left motor = 1.75 V.

The human primary somatosensory cortex encodes imagined movement in the absence of sensory information

Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory–motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems.

Dopamine D1 receptor activation drives plasticity in the songbird auditory pallium

Vocal learning species must form and extensively hone associations between sounds and social contingencies. In songbirds, dopamine signaling guides song motor-production, variability, and motivation, but it is unclear how dopamine regulates fundamental auditory associations for learning new sounds. We hypothesized that dopamine regulates learning in the auditory pallium, in part by interacting with local neuroestradiol signaling. Here, we show that zebra finch auditory neurons frequently coexpress D1 receptor (D1R) protein, neuroestradiol-synthase, GABA, and parvalbumin. Auditory classical conditioning increased neuroplasticity gene induction in D1R-positive neurons. In vitro, D1R pharmacological activation reduced the amplitude of GABAergic and glutamatergic currents, and increased the latter’s frequency. In vivo, D1R activation reduced the firing of putative interneurons, increased the firing of putative excitatory neurons, and made both neuronal types unable to adapt to novel stimuli. Together, these data support the hypothesis that dopamine acting via D1Rs modulates learning and memory in the songbird sensory cortex.

The Use of Big Data for Sustainable Development in Motor Production Line Issues

This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from the company’s testing equipment were statistically analyzed. The current study focused on secondary data and expert interview results to further define the relevant statistical dimensions. Only 14 of the original 88 detection parameters were required for monitoring the production line. The relationships between these parameters and the relevant motor components were established to indicate how an abnormal reading may be interpreted to quickly resolve an issue. Thus, a theoretical model for the monitoring of the motor production line was proposed. Further implications and practical suggestions are also offered to improve the production lines. This study explores big data analysis and smart manufacturing and demonstrates the promise of these technologies in improving production line efficiency and reducing waste to promote sustainable production goals. Big data thus constitute the core technology for advancing production lines into Industry 4.0 and promoting industry sustainability.

Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

In this study, the factors that affect the implementation of intelligent systems in motor production lines are analyzed. A motor production line located in Vietnam is used as the research object. The research methods include secondary data collection, field study, and interviews. This study demonstrates the following: firstly, the implementation of intelligent systems in motor production lines is heading toward Industry 4.0. Secondly, it is proposed that three functional systems—robot arm, image recognition, and big data analysis—can be introduced in the motor production line. This study analyzes the process involved in coil and motor production lines and attempts to combine intelligent system functions. It is expected that in the future, manpower will be reduced, production line productivity will increase, and intelligent production lines will be proposed. The factors that affect the introduction of intelligent systems in motor production lines are improved, and the importance of intelligent systems, which has been rarely considered in previous studies, is highlighted. In the implementation criteria of the intelligent system in the process management of the motor production line, this study provides some suggestions (to coil and motor assembly line) for the production process management. These suggestions can be provided as a reference for production lines that acquaint with intelligent systems.