Arm Action Research Articles

Design of Public Physical Education Curriculum Quality Evaluation System Based on the “5V” Characteristics of Computer Big Data

In order to improve the teaching effect of public physical education courses to the greatest extent and lay the foundation for the process of teaching reform, this paper puts forward the design of a public physical education course quality evaluation system based on the “5V” characteristics of computer big data. Analyze the characteristics of big data “5V” and study its application in public physical education courses; analyze the action estimation, prior, and update, failure recovery of public physical education course teaching, and get the three-dimensional visual positioning result of public physical education course teaching; collect the upper arm action image, extract the upper arm action image contour feature through edge contour detection, analyze the contour feature error characteristics, obtain the pixel component weighted value of the contour feature, further obtain the fuzzy error value of the upper arm action image contour feature through calculation, and according to the fuzzy error value, the design of public physical education course quality evaluation system is completed. The experimental results show that the average error recognition accuracy of the research method is 99.63%. The class hours are adjusted according to the proportion of disciplines, and the corresponding teaching plan is formulated. The accuracy of recommending internship posts for students is relatively high.

Can 5 Minutes of Finger Actions Boost Creative Incubation?

AbstractPrevious studies suggest that the activation of the motor system – either via action, motor imagery, or brain stimulation – may increase subsequent performance on divergent thinking tasks (e.g., the alternate uses task; AUT). We tested this idea in a within-subjects design by administering the AUT using four different target objects and four different 5-min incubation tasks that differed in terms of arm and finger movements. In between-subjects designs, 0-back incubation has been shown to yield more creative responses than rest. Additionally, we included two new incubations that both involved arm actions, but differed in the amount of finger actions (Iranian dance, ballet dance). Incubation tasks involving finger actions (Iranian dance, 0-back) were predicted to increase creativity for objects that are typically manipulated with the fingers. There was a main effect of object. Alternate uses given to the paperclip were rated as more creative than those given to the other objects. With our within-subjects design, we could not replicate the previously described difference between 0-back and rest incubations. However, hypothesis-driven comparisons showed that, although the interaction of object and incubation was not significant, Iranian dance yielded more creative usages for paperclip than for sheet of paper, cup and brick, and all other incubations yielded more creative usages for paperclip than for brick. Iranian dance also generated marginally more creative usages than ballet. Our results suggest that if the hypothesized effects exist, they are likely to be small. Overall, AUT performance seems more influenced by the AUT object than by type of incubation.

Dust growth and pebble formation in the initial stages of protoplanetary disk evolution

Aims. The initial stages of planet formation may start concurrently with the formation of a gas-dust protoplanetary disk. This makes the study of the earliest stages of protoplanetary disk formation crucially important. Here we focus on dust growth and pebble formation in a protoplanetary disk that is still accreting from a parental cloud core. Methods. We have developed an original three-dimensional numerical hydrodynamics code, which computes the collapse of rotating clouds and disk formation on nested meshes using a novel hybrid Coarray Fortran-OpenMP approach for distributed and shared memory parallelization. Dust dynamics and growth are also included in the simulations. Results. We found that the dust growth from ~1 µm to 1–10 mm already occurs in the initial few thousand years of disk evolution but the Stokes number hardly exceeds 0.1 because of higher disk densities and temperatures compared to the minimum mass Solar nebular. The ratio of the dust-to-gas vertical scale heights remains rather modest, 0.2–0.5, which may be explained by the perturbing action of spiral arms that develop in the disk soon after its formation. The dust-to-gas mass ratio in the disk midplane is highly nonhomogeneous throughout the disk extent and is in general enhanced by a factor of several compared to the fiducial 1:100 value. Low St hinders strong dust accumulation in the spiral arms compared to the rest of the disk and the nonsteady nature of the spirals is also an obstacle. The spatial distribution of pebbles in the disk midplane exhibits a highly nonhomogeneous and patchy character. The total mass of pebbles in the disk increases with time and reaches a few tens of Earth masses after a few tens of thousand years of disk evolution. Conclusions. We found that protoplanetary disks with an age ≤20 kyr can possess notable amounts of pebbles and feature dust-togas density enhancements in the disk midplane. Hence, these young disks can already be ripe for the planet formation process to start. Multidimensional numerical models of disk formation that consider the coevolution of gas and dust including dust growth are important to improve our understanding of planet formation.

Visual sensitivity at the service of action control in posterior parietal cortex.

The posterior parietal cortex (PPC) serves as a crucial hub for the integration of sensory with motor cues related to voluntary actions. Visual input is used in different ways along the dorsomedial and the dorsolateral visual pathways. Here we focus on the dorsomedial pathway and recognize a visual representation at the service of action control. Employing different experimental paradigms applied to behaving monkeys while single neural activity is recorded from the medial PPC (area V6A), we show how plastic visual representation can be, matching the different contexts in which the same object is proposed. We also present data on the exchange between vision and arm actions and highlight how this rich interplay can be used to weight different sensory inputs in order to monitor and correct arm actions online. Indeed, neural activity during reaching or reach-to-grasp actions can be excited or inhibited by visual information, suggesting that the visual perception of action, rather than object recognition, is the most effective factor for area V6A. Also, three-dimensional object shape is encoded dynamically by the neural population, according to the behavioral context of the monkey. Along this line, mirror neuron discharges in V6A indicate the plasticity of visual representation of the graspable objects, that changes according to the context and peaks when the object is the target of one's own action. In other words, object encoding in V6A is a visual encoding for action.

Golden ratio and self-similarity in swimming: breast-stroke and the back-stroke.

Dynamics-on-graph concepts and generalized finite-length Fibonacci sequences have been used to characterize, from a temporal point of view, both human walking & running at a comfortable speed and front-crawl & butterfly swimming strokes at a middle/long distance pace. Such sequences, in which the golden ratio plays a crucial role to describe self-similar patterns, have been found to be subtly experimentally exhibited by healthy (but not pathological) walking subjects and elite swimmers, in terms of durations of gait/stroke-subphases with a clear physical meaning. Corresponding quantitative indices have been able to unveil the resulting hidden time-harmonic and self-similar structures. In this study, we meaningfully extend such latest findings to the remaining two swimming strokes, namely, the breast-stroke and the back-stroke: breast-stroke, just like butterfly swimming, is highly technical and involves the complex coordination of the arm and leg actions, while back-stroke is definitely similar to front-crawl swimming. An experimental validation with reference to international-level swimmers is included.

Экспериментальная оценка и обоснование мероприятий по обеспечению безопасности и устойчивости функционирования критических элементов гидротехнических сооружений

The most important scientific and practical task is the design and creation of safe, reliable, and durable hydraulic structures, which during operation can be subjected to various external influences. It is especially difficult to predict dynamic loads resulting from the unauthorized activities of third parties. Such incidents can interfere with the functioning of structures, lead to their destruction and, as a result, to catastrophic consequences for the economy, the environment, and the population. The results are considered related to the experimental studies of the penetrating action of small arms bullets into various materials. The studies were conducted for verifying the reliability of the results of a theoretical assessment of the penetrating effect of bullets. The possibility was considered related to the manufacturing protective screens in the field conditions, which guarantees non-penetration of critical elements of hydraulic structures by a bullet. As the main striking element, a bullet of a rifle cartridge 7,62×54R is considered. Experimental studies included a series of experiments with different targets. The number of shots on one target did not exceed 27, while the absence of through holes made it possible to speak of a 90 % confidence probability of non-penetration of the tested protective structure. Results of the experimental study showed that to ensure impenetrability, it is sufficient to use steel 2–3 mm thick, wood (pine) 18 mm or 250 mm of sand. In theoretical calculations, these values are obtained by 20–25 % more. Perhaps such discrepancies are due to the fact that the indenter in the model is represented by an absolutely solid body, while the bullets with a deformable steel core were used in the experiments. The algorithm proposed by the authors allows to choose a rational composition of the protective structure.

Searching in Justification for the Intensification of Upward-Kicking in Dolphin Swimming

Abstract Scientific opinions about the importance of dolphin upward leg movements for swimming performance are still divided. Research on the efficiency of propulsion, similarly generated by a relatively larger the monofin surface, showed that quality of the upward movement phase is a measure of swimming performance. Therefore, the forces generated as a result of upward and downward kicks in butterfly swimming were analysed. The ability of the swimmers to kinaesthetically control of the upward dolphin kicking was also researched. Ten international level butterfly swimmers (mean 18,2±1,4 years) took part in the research. They performed three trials on 25m distance, at maximum speed using the monofin device (The plate of standard monofin was removed and two strain gauges were affixed in the middle, between the toe). They randomly swam: 1) butterfly stroke (BS), 2) dolphin-kicking only (LK) and 3) swam being focused on activation of upward-kicking (AUK). Time dependent signals of the force sagging the fin in reaction to the water resistance were registered. The impulses of force sagging the fin in each stroke were lower in upbeat than in downbeat. The lowest difference was in AUK trial. First, Wilks's test and then Duncan’s post hoc tests for each of the trial showed (at p≤0,05) that: downward impulses for LK was significantly higher than for BS. Upward impulses did not differ between the trials. Downward times for AUK, the same as total trial times, was significantly longer than for BS and for LK Upward times for AUK was significantly longer than for BS. The results demonstrated, that butterfly arms action leads to intensification of both kicking phases. The swimmers were well skilled for kinaesthetic controlling of the dolphin upbeat. They did not obtain the equal proportion between propulsion effect of both kicking phases, but it has been suggested, that this level of skill mastering is out of the human motor abilities. Opportunities for conscious controlling of the efficiency of the upward movement are seen in the modification of the time structure of this phase. These results could be used for improving the leg-kicking performance in butterfly swimming, as well as after stars and turns.

Multi-dimensional sensorimotor grounding of concrete and abstract categories.

Semantic categories, and the concepts belonging to them, have commonly been defined by their relative concreteness, that is, their reliance on perception. However, sensorimotor grounding must be regarded as going beyond the basic five senses and incorporate a multi-dimensional variety of perceptual and action experience. We present a series of exploratory analyses examining the sensorimotor grounding of participant-produced member concepts for 117 categories, spanning concrete (e.g. animal and furniture) and highly abstract (e.g. unit of time and science) categories. We found that both concrete and abstract categories are strongly grounded in multi-dimensional sensorimotor experience. Both domains were dominated by vision and, to a lesser extent, head movements, but concrete categories were more grounded in touch and hand-arm action, while abstract categories were more grounded in hearing and interoception. Importantly, this pattern of grounding was not uniform, and subdomains of concrete (e.g. ingestibles, animates, natural categories and artefacts) and abstract (e.g. internal, social and non-social) categories were grounded in different profiles of sensorimotor experience. Overall, these findings suggest that the distinction between abstract and concrete categories is not as clearcut as ontological assumptions might suggest, and that the strength and diversity of sensorimotor grounding in abstract categories must not be underestimated. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Design and development of agricultural spraying system

The majority of the sprayer pumps available in the current market are back-mounted, hand pumps that are used to spray pesticides. Although tractor-mounted sprayers are presently available on market are seen to use 10 sets of nozzles to spray the pesticide is very demanding on pesticide consumption leading to pesticide wastage, and subsequent soil and water pollution. This paper focused on the development of an agriculture sprayer with an appropriate pesticide application mechanism that would apply the pesticide directly to the plant foliage, and fruits with minimal wastage thereby making effective pest control and so also reducing pesticide consumption which will aid to reduce spraying cost per acre and more importantly reduce the probability of soil and water pollution. Oscillating Nozzle Agriculture Sprayer is an innovative solution to the above-mentioned problem where four nozzles with oscillating action cover maximum plant foliage area, with effect reach, application of Italian nozzles adds to proper atomization of spray and effective application is attained through the combination of the nozzle and oscillating arm action. The system has been designed, manufactured, and tested in actual conditions. Field test results showed that the cost of spray saved annually is around 25–30% of the pesticide cost for existing tractor-mount spraying mechanisms.

Research on SVPWM control algorithm based on dual three-phase permanent magnet synchronous motor

Compared with the three-phase permanent magnet synchronous motor, the dual-three-phase permanent magnet synchronous motor has the characteristics of high reliability, small torque fluctuation, and strong fault tolerance, which is suitable for application in the fields of ships, aerospace, and electric vehicles. However, due to the presence of harmonic sub-spaces in dual-three-phase permanent magnet synchronous motors, small voltage distortions can lead to excessive harmonic currents. In this paper, the dual-three-phase permanent magnet synchronous motor is used as a research object, and a control algorithm is designed to suppress its harmonic current. The traditional 48-sector SVPWM does not take into account the simultaneous action of the three-phase bridge arm of the same group of inverters during sector conversion, so it generates a large harmonic current when sector conversion. Therefore, this paper proposes an improved 48-sector SVPWM, which makes a partial adjustment to the vector selection of each sector based on the traditional 48-sector SVPWM, so that the zero vector selection of each sector is the same, avoiding the situation that the same group of three-phase bridge arms acts simultaneously when the sector is converted. The basic principles of improving the 48-sector SVPWM are introduced in detail, and the simulation analysis is carried out in MATLAB, which verifies the correctness of the theoretical analysis in this paper. The results show that this algorithm not only inherits the advantages of the traditional 48-sector SVPWM that is easy to implement in hardware, but also further reduces the harmonic current of the stator.

Development of a dual-arm rapid grape-harvesting robot for horizontal trellis cultivation.

It is extremely necessary to achieve the rapid harvesting of table grapes planted with a standard trellis in the grape industry. The design and experimental analysis of a dual-arm high-speed grape-harvesting robot were carried out to address the limitations of low picking efficiency and high grape breakage rate of multijoint robotic arms. Based on the characteristics of the harvesting environment, such as the small gap between grape clusters, standard trellis, and vertical suspension of clusters, the configuration of the dual-arm harvesting robot is reasonably designed and analyzed, and the overall configuration of the machine and the installation position of key components are derived. Robotic arm and camera view analysis of the workspace harvesting process was performed using MATLAB, and it can be concluded that the structural design of this robot meets the grape harvesting requirements with a standard trellis. To improve the harvesting efficiency, some key high-speed harvesting technologies were adopted, such as the harvesting sequence decision based on the “sequential mirroring method” of grape cluster depth information, “one-eye and dual-arm” high-speed visual servo, dual arm action sequence decision, and optimization of the “visual end effector” large tolerance combination in a natural environment. The indoor accuracy experiment shows that when the degree of obscuration of grape clusters by leaves increases, the vision algorithm based on the geometric contours of grape clusters can still meet the demands of harvesting tasks. The motion positioning average errors of the left and right robotic arms were (X: 2.885 mm, Y: 3.972 mm, Z: 2.715 mm) and (X: 2.471 mm, Y: 3.289 mm, Z: 3.775 mm), respectively, and the average dual-arm harvesting time in one grape cluster was 8.45 s. The field performance test verifies that the average harvesting cycle of the robot with both arms reached 9 s/bunch, and the success rate of bunch identification and harvesting success rate reached 88 and 83%, respectively, which were significantly better than those of existing harvesting robots worldwide.

Artificial Intelligence Biosensing System on Hand Gesture Recognition for the Hearing Impaired

AI technologies have the potential to help deaf individuals communicate. Due to the complexity of sign fragmentation and the inadequacy of capturing hand gestures, the authors present a sign language recognition (SLR) system and wearable surface electromyography (sEMG) biosensing device based on a Deep SLR that converts sign language into printed message or speech, allowing people to better understand sign language and hand motions. On the forearms, two armbands containing a biosensor and multi-channel sEMG sensors are mounted to capture quite well arm and finger actions. Deep SLR was tested on an Android and iOS smartphone, and its usefulness was determined by comprehensive testing. Sign Speaker has a considerable limitation in terms of recognising two-handed signs with smartphone and smartwatch. To solve these issues, this research proposes a new real-time end-to-end SLR method. The average word error rate of continuous sentence recognition is 9.6%, and detecting signals and recognising a sentence with six sign words takes less than 0.9 s, demonstrating Deep SLR's recognition.

Defects Reduction in the Robotic Layup Process

Robotic layup is a novel process developed to face the increasing demand for automation, flexibility, repeatability, and achieving high-quality composite materials in relevant industry fields, such as aerospace and automotive. This process is based on laying prepreg tissues on a mold using the action of a robotic arm equipped with a specific end-effector, which is usually composed of rollers and punches. The main drawback of the robotic layup is the occurrence of wrinkles and defects while moving, placing, and processing the pre-impregnated tissues. This issue is particularly evident in the processing of complex-shaped surfaces. The robotic arm cannot replicate exactly the movement of a human operator, following the geometry of the surfaces with a proper angulation like a human wrist. Moreover, operator hands can be set in a different shape just changing the configuration of the fingers, adapting themself in different curvatures. The demand of the industry to improve automation requires that the robotic manufacturing systems replicate as much as possible the gesture of the operator. In this study, an end-effector capable to replicate the human movements during the hand layup of fibrous tissues or fabrics has been conceptualized.

Abnormal actions detection of robotic arm via 3D convolution neural network and support vector data description

Robotic arms are powerful assistants in many industrial production environments, and they run periodically in accordance with preset actions to complete specified operations. However, they may act abnormally when encountering unexpected situation and then lead to unnecessary loss. Recognizing the abnormal actions of robotic arms through surveillance video can automatically help us to understand their operating status and discover possible abnormalities in time. We designed a deep learning architecture based on 3D convolution for abnormal action recognition. The 3D convolutional layer can extract the spatial and temporal features of the robotic arm movements from the video frame difference sequence. The features are compressed and streamlined by the maximum pooling layer to obtain concise and effective robotic arm action features. Finally, the fully connected layer is used to classify the features to recognize the abnormal robotic arm tasks. Support vector data description (SVDD) model is employed to detect abnormal actions of the robotic arm, and the well-trained SVDD model can distinguish the normal actions from the three kinds of abnormal actions with the Area Under Curve (AUC) 99.17%.

A Robotic Arm Based Design Method for Modular Building in Cold Region

The robotic arm has emerged as an essential tool for the rapid construction of high-quality buildings due to its ability to repeat instructions, achieve precise positioning and fine operations. The robotic arm can also effectively replace workers to complete building construction under low temperature and short daylight conditions. Thus, it can be forward-looking to construct modular buildings in cold region, but how to realize the construction of modular buildings through human-machine coordination and remote operation is a significant issue. This article discusses the feasibility of robotic arm assembly design methods in the field of architecture by simulating the complete design and construction process of modular buildings. According to parameterized module design, a three-dimensional computer model is transformed into an electronic file that directs the action of the robotic arm to complete assembly via a custom processing program. This article details a theoretical and practical exploration of the construction of modular manipulators and has certain guiding significance for the intelligent design and construction of future buildings.

FNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

The Impact of Bodily Behaviors of Sales Representatives on Charisma Evaluations by Consumers: A Time-Series Perspective

Significant resources are spent each year on sales forces and the means by which to enhance their effectiveness during a sales interaction or presentation. Specifically, studies point to the importance of charismatic nonverbal cues (for example, facial expressions, gestures) in impression formation. However, these behaviors are mainly perceived in an unconscious manner, making behavior measurement a difficult task. Moreover, existing research is dominated by post-exposure measures and neglects customers’ processing of impressions over time. This research addresses the outlined gaps and introduces continuous measurement of sales presentations based on different data sources. First, we provide novel insights by applying high-precision coding of 141 nonverbal behaviors of 22 videotaped sales presentations using body actions and posture coding procedures. Second, this study uses an innovative approach to capture customer impressions of sales representatives’ charisma in real-time by means of a program analyzer, which allows evaluative measurements while concurrently being exposed to sales presentations. This time-series evaluation approach contributes to the understanding of impression formation and allows for linking nonverbal sales behaviors to customers’ evaluations over the course of time. Findings from a large sample experimental study (n = 663) show that negative opinions are formed somewhat faster than positive ones. In addition, body movements (e.g., head/trunk/leg/knee movements, arm actions) driving these impressions are the same for the first few seconds and for longer periods.

Analysis on the mechanical properties of crawler doubles - arm degree-dividing bar car bolt drill

In view of the low efficiency of roadway support in coal mine, a crawler double arm bolts drill with indexing function is proposed, in view of the low efficiency of roadway support in coal mines, a crawler double-arm bolt drill with indexing function is proposed. The mechanical characteristics of its key components in the telescopic action of the spindle arm are calculated and analyzed, and the binding force of the spindle arm and the variation rule of the telescopic acceleration and rotation Angle parameters are obtained. And the torque of the spindle arms decreases slightly with the decrease of the rotation Angle of the spindle arm, and increases greatly with the increase of the length of the spindle arm. The results show that the maximum torque is 6914Mpa when the length of the spindle arm is 2.4m and the rotation Angle is 45°, which lays a foundation for the subsequent design of the drill car and the improvement of the efficiency of anchor guard operation.

Inertial measurement unit–based cricket stroke improviser using polynomial kernel support vector machines

Wearable devices have now become virtual assistants, and the sports industry also aims in technological integration. The objective of this research article is to introduce a wearable device to detect and record the movement of a cricket player during his training session. The designed system collects the displacement and rotational information through a combination of accelerometer and gyroscope placed on the cricket bat. We propose a data-driven machine learning model which takes raw analog data as input for classifying the strokes. The algorithm used is the polynomial support vector machine, a supervised classification algorithm with 300 independent variables to enable accurate and real-time stroke classification. The system has a dedicated user interface for accessing these real-time details. This wearable embedded system does not require any cloud services as the complex analyses are performed in the processor itself. The player and the coach can get visual reference support, and the mistakes can be corrected during the training period itself. The device can detect the arm action of a cricket player with a success rate of 97%. The hardware is powered using a 10,000 mAh rechargeable battery.

Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning

The use and application of robotic arms in helping the aged and vulnerable persons are increasing gradually. In order to achieve safer and reliable human-robot interaction and its wider adoption, the requirements for the humanoid motion of robotic arms are becoming more stringent. This paper presents a humanoid motion planning method for a robotic arm based on the physics of human arm and reinforcement learning. Firstly, the humanoid motion rules are extracted by analyzing and learning the action data of human arm, which is collected using the VICON optical motion capture system. Then, according to the acquired features and rules, the corresponding reward functions are proposed and the humanoid motion training of the robotic arm is carried out by using the reinforcement learning based on Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) algorithm. Finally, the experiments are carried out to verify whether the robotic arm motions planned by the proposed approach are humanoid, and the observed results show its feasibility and effectiveness in planning the humanoid motion of the robotic arm.