Movement Constraints Research Articles

Hand constraint reduces brain activity and affects the speed of verbal responses on semantic tasks

According to the theory of embodied cognition, semantic processing is closely coupled with body movements. For example, constraining hand movements inhibits memory for objects that can be manipulated with the hands. However, it has not been confirmed whether body constraint reduces brain activity related to semantics. We measured the effect of hand constraint on semantic processing in the parietal lobe using functional near-infrared spectroscopy. A pair of words representing the names of hand-manipulable (e.g., cup or pencil) or nonmanipulable (e.g., windmill or fountain) objects were presented, and participants were asked to identify which object was larger. The reaction time (RT) in the judgment task and the activation of the left intraparietal sulcus (LIPS) and left inferior parietal lobule (LIPL), including the supramarginal gyrus and angular gyrus, were analyzed. We found that constraint of hand movement suppressed brain activity in the LIPS toward hand-manipulable objects and affected RT in the size judgment task. These results indicate that body constraint reduces the activity of brain regions involved in semantics. Hand constraint might inhibit motor simulation, which, in turn, would inhibit body-related semantic processing.

Energy-efficient tram speed trajectory optimization considering the influence of the traffic light

The tram typically adopts the form of semi-independent right of way, which is affected by the traffic light at junctions. The parking and wait time of the tram due to red lights will increase the energy consumption and reduce the passenger comfort. This paper proposes a speed trajectory optimization approach for the tram without parking when encountering a red light. This approach is realized by reasonably adjusting the running time and speed of the tram between two adjacent sections bounded by the traffic light at the junction, on the premise that the total travel time of the whole section remains unchanged. In the model, the constraints of tram movement, speed limits, and line conditions are considered. Moreover, considering the influence of the traffic light, the comfort and traffic light constraints are added to establish the energy-efficient tram speed trajectory optimization model. The nonlinear constraints in the optimization model are converted to linear constraints using the segmental approximation method and adding constraints. As a result, the optimization model is reformulated into a mixed-integer linear programming model, which could be solved by a commercial solver like CPLEX. Numerical experiments are conducted with a 3,660 m long section of the tram line with the traffic light at the junction. The experimental results show that the speed trajectory optimization approach considering the influence of the traffic light proposed in this paper can reduce the total energy consumption by 23.46% and improve passenger comfort.

Trajectory Tracking Control of Rowing Pectoral Fin-Actuated Robotic Fish

Robotic fish has received increasing attention in the last few decades, as they hold strong promise in a myriad of applications. Efficient and precise control of these robots, particularly accurate trajectory control, has become essential in many of these applications. This article proposes a dual-loop backstepping-based trajectory tracking control approach for a robotic fish actuated by rowing pectoral fins. While rowing pectoral fin-based locomotion is important for maneuvering, the range constraints of fin movement pose significant challenges in the control of robotic fish, including potentially preventing the robot from generating the thrust needed to maneuver in a desired direction. To overcome these challenges, we propose a dual-loop controller, designed based on an averaged dynamic model of the robot. In particular, an outer-loop backstepping-based controller finds the needed force and moment inputs for the robot to track the desired trajectory, while the inner loop determines the optimal fin-beat parameters such that the resulting fin-generated forces and moment are close to their desired values. Experimental results are presented to show the efficacy of the proposed control scheme, where the robot is commanded to track a trajectory with variable linear and angular velocities. Comparing to a well-tuned proportional–integral–derivative controller, the proposed control scheme shows a distinct advantage in tracking desired orientations in addition to tracking desired positions.

Effects of Task Constraints on Countermovement Jump Kinematics Following a Short-Term Training

In previous studies, the acute effects of movement constraints on the countermovement jump performance are investigated; however, the longitudinal effects of implementing the constraint in a training regimen are not well studied. The purpose of this study was to examine the phenomenon of motor learning development due to application of task constraint in a training regimen following 6 weeks of training. Forty-five healthy adult males were randomly assigned to a control or one of two experimental constraint training groups (i.e., no arm swing or restricted knee flexion). Pre- and posttraining jump height and kinematic variables of six maximum effort countermovement jumps were compared longitudinally within the groups, and also compared between the groups. The findings of this study indicated that jump height significantly increased in all the groups while in the unrestricted control group it was increased greater than the experimental conditions (21% compared with 12% and 5.5%). However, the applied task constraints significantly improved some of the contributors to jump performance, establishing specific adaptation of kinematic variables to the constraint training. Therefore, constraint training approach could be suggested in case of demanding specific adaptation of kinematic variables of countermovement jump in a training regimen.

Algorithms for Large Scale Additive Manufacturing in a Free-Flying Environment

Large scale additive manufacturing (LSAM) refers to the fabrication of structures that exceed the build volume of conventional additive manufacturing setups. This can be used for in-space manufacturing (ISM), facilitating the production of large functional structures in space which cannot fit within the payload fairing of a launcher system. In this paper, a new approach for a continuous fabrication process of structural elements is presented, combining the reach of a free-flying satellite and a robotic arm, utilizing a fused filament fabrication (FFF) 3D-printing setup. A motion planning algorithm was developed which calculates energy-efficient movement trajectories based on a truss design input combined with the movement constraints of the satellite and robotic arm. Using this printing paradigm, a long support-free truss was manufactured. This approach was further elevated by extending the truss structure along the second planar axis, thus facilitating the manufacture of structures larger than generally possible through a layer-by-layer approach. Subsequently, combining the segmented and continuous printing approach, a planar truss structure was produced.

An Evolutionary Adolescent Idiopathic Scoliosis Etiology Spine-Limbs Links, Inspiration and Laterality as Basic Factors

Moving on an evolutionary sight, this article individualizes three basic human causative factors in adolescent idiopathic scoliosis developing inspiration, bipedal locomotion and lateralities. The integrative approach considers the neuromechanical coupling in respiration, introducing the concept of pneumofascial competition (section one) as the loop that occurs between inspired air and pleural fasciae and then global myofascia. Affordances (possibilities) and constraints of respiratory system match positions and movements of whole body, where the links between spine and periphery (limbs and head) play a determinant part: these connections, the one that involves the spinal transverse plane, are worked out in section two. Therefore, section two appears a decisive step in trying to solve some questions about human bipedal locomotion and its proficiency, again in a neuromechanical coupling view, with totally original considerations about the role of scapula and about the spine as a treble torsion spring. Finally, it is possible to trace out a new adolescent idiopathic scoliosis etiology (section three), adding a third factor: laterality. What is only human is the hyper stressed lateralization (s) by means of cultural requirements. The motor hyper specialization is another form of energy saving (automatism). Thus, all the article is about the evolutionary principle of costs minimizing. Adolescent idiopathic scoliosis is here seen as an evolutionary mismatch disease between human nature and human culture and every curve can be explained in an integrative way, where causes and effects develop a mutual self-powering cycle.

Forced Displacement in Libya and its Health, Psychological and Social Dimensions: The Case of the Displaced Tawergha

Exposure to the physiological, social, and psychological situations of those relocated from Tawergha is a relatively new issue in the study of Libyan forced migration. This inquiry aims to learn more about the social, psychological, and health situations of the people who have been displaced from Tawergha. Residents of two large camps that housed the Tawergha refugees were given a questionnaire. There were 435 displaced people in this research. The findings revealed substantial discrepancies between various characteristics (economic challenges, security situation, living in a disease-spreading environment, mingling with people) and the displaced people's health and psychological problems. The variable of attack exposure only affected the displaced's psychological difficulties. Also, at the 0.05 significance level, other factors such as a lack of housing, the absence of a family member, having adequate food, feeling rejected, movement constraints, and loneliness do not affect psychological difficulties and health conditions. In light of these findings, the study recommends implementing social measures to protect Libya's displaced people. In addition to the need for psychological support programs and the activation of social work for professional assistance with displacement issues, there is also a need for professional help with displacement concerns.
 
 Received: 19 December 2021 / Accepted: 17 April 2022 / Published: 5 July 2022

Automatic collective motion tuning using actor-critic deep reinforcement learning

Collective behaviours such as swarm formation of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, tuning the behaviour of a group of agents so that they swarm, is difficult. Behaviour-bootstrapping algorithms permit agents to self-tune behaviour adapted for their physical form and associated movement constraints. This paper proposes a reinforcement learning framework to tune collective motion behaviours from random behaviours. The learning process is guided by a novel reward function capable of autonomously detecting generic collective motion behaviours from sensor data about the relative velocity and position of neighbouring agents. Our reward function is designed using a meta-learner trained on a human-labelled collective motion dataset. We demonstrate that our reinforcement learner can tune the behaviour of randomly moving groups so that structured collective motion emerges. We compare our framework to an existing developmental evolutionary framework for this purpose. Our results demonstrate that the proposed learning framework can generate behaviours with different collective motion characteristics more quickly than existing approaches. In addition, the trained reinforcement learner can tune the behaviour of robots with movement characteristics that it has not been trained on.

Second Language Acquisition of Constraints on WH-Movement by L2 English Speakers: Evidence for Full-Access to Syntactic Features

This paper presents results from two experiments on the L2 acquisition of wh-features and relevant constraints (Superiority and Subjacency) by L1 Sinhala–L2 English speakers. Our results from a Truth Value Judgment Task and a Grammaticality Judgment Task with 31 English native controls and 38 Sinhala/English bilinguals show that the advanced adult L2 speakers of English we tested have successfully acquired the uninterpretable wh-Q feature and relevant movement constraints in English, despite the lack of overt wh-movement in L1-Sinhala. These results raise questions for Representational Deficit Accounts of second language acquisition and offer evidence that (i) uninterpretable syntactic features are not necessarily subject to an early critical period and (ii) uninterpretable features not instantiated in learners’ L1 can be available for L2 syntactic computation. We take our results as evidence for full access by L2 learners to syntactic properties that are not instantiated in their L1, but that remain accessible due to a cognitive capacity for language (i.e., knowledge of Universal Grammar) independently of the L1.

The Relationship between Accelerometry, Global Navigation Satellite System, and Known Distance: A Correlational Design Study.

Background: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. Method: A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Instrumentation: Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Results: Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960–0.999, R2 = 0.922–0.999; RMSE = 0.047–0.242, p < 0.001), while GNSSD (small circle, R = 0.999, R2 = 0.997, RMSE = 0.047 p < 0.001; large circle, R = 0.999, R2 = 0.999, RMSE = 0.027, p < 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R2 = 0.983, RMSE = 0.112, p < 0.001; large circle, R = 0.997, R2 = 0.995, RMSE = 0.064, p < 0.001) performed best among all models. Conclusions: This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking.

Trajectory Design for UAV-Based Internet of Things Data Collection: A Deep Reinforcement Learning Approach

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) system in a sophisticated 3-D environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified 2-D scenario and the availability of perfect channel state information (CSI), this article considers a practical 3-D urban environment with imperfect CSI, where the UAV’s trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired by the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV’s trajectory and we present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of the UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of the IoT nodes, the UAV’s position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV’s movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional nonlearning-based baseline methods.

Numerical simulation of Faraday waves in a rectangular tank and damping mechanism of internal baffles

Parametric sloshing and damping mechanism of internal baffles were systematically investigated through an in-house numerical model NEWTANK (developed by Xue and Lin, 2011). The internal baffles were modelled by the Virtual Boundary Force method. A good agreement of validation was achieved, then the numerical model was applied to investigate the mode 2 to mode 5 Faraday waves in tanks with various baffles, and the damping mechanism has been uncovered. Four kinds of baffles, including vertical, horizontal, ring and T-shape baffles, have been considered. The wave was divided into two categories: symmetric and asymmetric modes. The effectiveness of those baffles was identified by estimating the waveform, velocity and vortex fields; and the effective baffle configurations for various waveforms were proposed. The key issue was to suppress the vertical motion at the tank sidewalls for the symmetric mode; as for the asymmetric mode, the constraint of the horizontal movement at the wave nodes was proved to be more vital. The ring baffle was effective to suppress the mode 2 Faraday wave but lost efficacy for the mode 3 Faraday wave; the horizontal baffle was applicable for both mode 2 and mode 3 Faraday waves; and the vertical baffle was superior to the T-shape baffle in mitigation of the mode 2 Faraday wave, but was inferior to the latter as for the mode 3 Faraday wave. The effectiveness of the T-shape baffle in/close to the wave nodes was further identified through simulations of higher modal Faraday waves. Finally, the mitigation of the sloshing under coupled resonant surge and unstable heave excitations was presented and discussed.

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images.

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

Case Study: Comparison of Swimsuits and Wetsuits Through Biomechanics and Energetics in Elite Female Open Water Swimmers

The authors investigated how the Arena Powerskin R-EVO Closed Back swimsuit and Arena Carbon Triwetsuit (full-sleeve wetsuit), both approved by the Fédération Internationale de Natation (FINA) regulations, affect biomechanics and energetics of 3 elite female open water (OW) swimmers at maximal and 4 submaximal swimming intensities. Three elite female OW swimmers (OW1 = 24y, 1.64m, 60kg; OW2 = 23y, 1.69m, 65kg; OW3 = 27y, 1.63m, 64.5kg) were tested 1 week prior to a FINA/CNSG (China National Sports Group) Marathon Swim World Series event and 40 days before the 18th FINA World Championships 2019. Each OW swimmer completed 2 identical testing sessions, one with a swimsuit and other with a wetsuit, involving shoulder flexion power output assessed from medicine-ball throw, maximal performance and drag coefficient assessment, and an incremental intermittent swim test at 4 different relative intensities. Estimated peak oxygen uptake was 4.4L·min-1 for OW1, 5.6L·min-1 for OW2, and 5.0L·min-1 for OW3. Despite a distinct behavior observed on index of coordination for OW3, a null index of synchronization, increased stroke rate (mean difference = 2%-8%), reduced drag factor (minimum = -14%; maximum = -30%), lower energy cost (mean difference = -2% to -6%), and faster performance (mean difference = 2% to 3%) were observed with the wetsuit compared with swimsuit for all elite OW swimmers. The wetsuit enhances submaximal swimming performance, and this increase is dependent on the OW swimmer's characteristics. The higher stroke rate and lower stroke length detected with wetsuit could be related to movement constraints imposed by the suit.

Focus particles and negative polarity

In this article I discuss constructions i odin NP and daže odin NP ‘even one NP’ in Russian, which are negative polarity items (NPIs), e.g. Petja *(ne) smog rešit’ i / daže ODNU zadaču ‘Petya didn’t manage to solve even one problem’. Only daže-NPIs are grammatical in non-negative environments of NPI licensing, e.g. Každyj, kto rešil ?daže / ??i ODNU zadaču, polučil začët ‘Everyone who solved even one problem got credit’. The same dependence is observed in several other languages: particles with additive and coordinative functions (like i ‘even, and’) form NPIs with narrower distribution than plain scalar particles (like daže ‘even’). The following question is thus the main topic of the paper: (A) why do i- and daže-NPIs have different distribution? Two auxiliary questions are: (B) what is the semantics of i and daže, and (C) why do they form NPIs when attached to the low endpoint of a scale? I argue that daže triggers only the scalar presupposition and i triggers only the additive presupposition (question B). I show that both scalar and additive presupposition cause ungrammaticality in positive environments (C). Finally, I compare two theories which (aim to) explain why the additive presupposition of i determines a narrower distribution of i-NPIs in comparison to daže-NPIs (A). A simple theory considers ungrammaticality of i-NPIs to be the result of interaction of the additive presupposition with presuppositions of licensing environments. Although it predicts some interpretational differences between i- and daže-NPIs, it fails to explain the confinement of i-NPIs to negative contexts. According to an alternative theory, i-NPIs are subject to standard movement constraints while daže-NPIs take exceptional (i.e. sentential) scope, which explains their different distribution without any additional assumptions. I also show how this theory can explain the weak vs. strong NPIs distinction in other cases.

Individual mobility pattern in Malaysia during COVID-19 Recovery Movement Control Order partial lockdown.

Malaysia Recovery Movement Control Order (RMCO) aims to bring the business, education, tourism and other industry sectors back into operation. Due to movement constraints that result in local economic patterns, individual mobility patterns are expected to occur. However, this matter needs further investigation from people's spatial behaviour during the RMCO. Therefore, this research proposed a new technique for analysing people's spatial behaviour patterns via geo‐tagged data. The data from social media users are gathered using data mining techniques. Geographical Information System (GIS) is used to show the geolocation of social media users and analyse their spatial behaviour. The finding of this analysis shows higher people's movement recorded when the RMCO was enforced; a distinctive pattern where spatial trajectory length is high but spatial area coverage is low. It is noticed that the focal points are concentrated in urban areas and tourism attractions.

Surge of the Innovative Quest in the first lockdown period due to the Pandemic Effect

When the entire world is reeling under the COVID 19 pandemic effect and the tensed human race is struggling to return back to the normalcy of life, the one thing which has become very active is the grey cells of our brain. The pandemic effect has cut down our physical limits due to the movement constraints. But it is thankfully unable to restrict the ticking of the grey cells of the human brain. As is said, “Necessity is the mother of the invention”. Sure enough!! We can be extremely pleased to know that the innovative surge in science and technology continues unabated in this lockdown period. The prime requirement of the pandemic effect is social distancing, less physical contact and keeping ourselves away from infection by corona virus. Keeping this necessity in mind, the doctors, the engineers, the researchers as well as the students’ community are keeping themselves busy in pumping out the solutions to the currently faced problems. The outputs include automatic masks machines, low cost PPE’s, automatic wash basins, suitable ventilators, sanitizer tunnels etc. This review paper looks into the innovative surge already made and what more can be churned out for the effective social safety in this tensed pandemic effect. The most awaited news as of now is the successful implementation of an effective vaccine and cost effective drugs which can help the human beings breathe easy. The pandemic effect has also showed us the way for a cleaner and greener nature. It is now a challenge to the intellectual world to come up with inexpensive, innovative and smart solutions which will make our beautiful planet safer, greener, cleaner and worthier to live in.

Detailed Placement and Global Routing Co-Optimization with Complex Constraints

With several divided stages, placement and routing are the most critical and challenging steps in VLSI physical design. To ensure that physical implementation problems can be manageable and converged in a reasonable runtime, placement/routing problems are usually further split into several sub-problems, which may cause conservative margin reservation and mis-correlation. Therefore, it is desirable to design an algorithm that can accurately and efficiently consider placement and routing simultaneously. In this paper, we propose a detailed placement and global routing co-optimization algorithm while considering complex routing constraints to avoid conservative margin reservation and mis-correlation in placement/routing stages. Firstly, we present a rapidly preprocessing technology based on R-tree to improve the initial routing results. After that, a BFS-based approximate optimal addressing algorithm in 3D is designed to find a proper destination for cell movement. We propose an optimal region selection algorithm based on the partial routing solution to jump out of the local optimal solution. Further, a fast partial net rip-up and rerouted algorithm is used in the process of cell movement. Finally, we adopt an efficient refinement technique to reduce the routing length further. Compared with the top 3 winners according to the 2020 ICCAD CAD contest benchmarks, the experimental results show that our algorithm achieves the best routing length reduction for all cases with a shorter runtime. On average, our algorithm can improve 0.7%, 1.5%, and 1.7% for the first, second, and third place, respectively. In addition, we can still obtain the best results after relaxing the maximum cell movement constraint, which further illustrates the effectiveness of our algorithm.

Gaussian mixture model based fixed-time control for safe proximity to spacecraft with complex shape obstacle

Autonomous spacecraft proximity technology is vital for current and future on-orbit service missions, especially to solve the difficult problem of ultra-close-range safe proximity to complex-shaped spacecraft. This paper designs a novel Gaussian mixture model (GMM) based artificial potential function (APF) to describe the movement constraint, which is generated by the complex shape of the target spacecraft. Next, a novel GMM-FTC controller is designed by combining fixed-time control (FTC) with GMM-APF. The proposed GMM-FTC not only retains the advantages of FTC method, whose stabilization time is independent of the initial state, but also solves the collision avoidance in the presence of complex shape obstacle. Finally, numerical simulation verifies the effectiveness of the developed method.

Micro-expression recognition based on facial action learning with muscle movement constraints

As a spontaneous facial expression, micro-expression reveals the psychological responses of human beings. However, micro-expression recognition (MER) is highly susceptible to noise interference due to the short existing time and low-intensity of facial actions. Research on facial action coding systems explores the correlation between emotional states and facial actions, which provides more discriminative features. Therefore, based on the exploration of correlation information, the goal of our work is to propose a spatiotemporal network that is robust to low-intensity muscle movements for the MER task. Firstly, a multi-scale weighted module is proposed to encode the spatial global context, which is obtained by merging features of different resolutions preserved from the backbone network. Secondly, we propose a multi-task-based facial action learning module using the constraints of the correlation between muscle movement and micro-expressions to encode local action features. Besides, a clustering constraint term is introduced to restrict the feature distribution of similar actions to improve categories’ separability in feature space. Finally, the global context and local action features are stacked as high-quality spatial descriptions to predict micro-expressions by passing through the Convolutional Long Short-Term Memory (ConvLSTM) network. The proposed method is proved to outperform other mainstream methods through comparative experiments on the SMIC, CASME-I, and CASME-II datasets.