Joint Coordinates Research Articles

The impact of transportation and information infrastructure on urban productivity: Evidence from 256 cities in China

Improving total factor productivity is an important driving force for achieving high-quality economic development. Based on the panel data of 256 cities in China from 2009 to 2019, this paper investigates the effect of transportation and information infrastructure on urban productivity. The results show that transportation and information infrastructure promote urban productivity, and the effect in eastern regions and megacities is more obvious. The mechanism analysis shows that transportation infrastructure enhances urban productivity by promoting economic agglomeration, and information infrastructure enhances urban productivity by promoting labor mobility, economic agglomeration, financial agglomeration and industrial agglomeration. The spatial spillover effect shows positive spatial autocorrelation of urban productivity. Transportation and information infrastructure have significant positive spatial spillover effects on urban productivity. China should continue to promote the construction of transportation infrastructure, strengthen investment in information infrastructure, and promote the joint construction, sharing, coordination and integration of transportation and information infrastructure.

Relationships Between Task Constraints, Visual Constraints, Joint Coordination and Football-Specific Performance in Talented Youth Athletes: An Ecological Dynamics Approach.

Individual performance in team sports is a multifactorial reflection of how well a player can cope and accomplish tasks in varied playing situations. Thus, performance analysis should not only focus on outcomes, but also on underlying mechanisms of those outcomes. We adopted principles of the ecological dynamics approach (EDA) to investigate the effect of introducing constraints on players' joint coordination responses for a football-specific performance drill outcome. Seventeen talented youth football (soccer) players performed a football-specific drill under different conditions: basic constraints, additional defender dummies, stroboscopic glasses, and a combination of the latter two constraints. We recorded these players' execution time, passing accuracy, and lower extremity joint kinematics. We calculated joint coordination for hip-knee, knee-ankle, and trunk-hip couplings. The added constraints negatively affected execution time and passing accuracy, and caused changes in joint coordination. Furthermore, we identified a relationship between execution time and joint coordination. This study serves as an example how the EDA can be adopted to investigate mechanisms that underlie individual performance in team sports.

Leg and Joint Stiffness of the Supporting Leg during Side-Foot Kicking in Soccer Players with Chronic Ankle Instability.

Soccer players with chronic ankle instability (CAI) may stabilize their supporting leg by the proximal joint to compensate for the ankle instability during kicking motion. This study aimed to investigate the characteristics of leg and joint stiffness of the supporting leg during side-foot kicking in soccer players with CAI. Twenty-four male collegiate-level soccer players with and without CAI participated in this study. The kinematic and kinetic data were obtained using a three-dimensional motion analysis system. Leg stiffness and joint (hip, knee, and ankle) stiffness in the sagittal and frontal planes were calculated and analyzed. The results clarified that soccer players with CAI (0.106 ± 0.053 Nm/°) had greater knee stiffness in knee adduction during the kicking cycle compared to those without CAI (0.066 ± 0.030 Nm/°; p = 0.046), whereas no characteristic differences were observed in knee stiffness in knee flexion and hip and ankle stiffness (p > 0.05). Knee stiffness is believed to occur to compensate for ankle joint instability in the supporting leg. Therefore, adjusting knee stiffness to accommodate ankle joint instability is crucial for maintaining kicking performance. Based on results of this study, it may be important to consider training and exercises focused on joint coordination to improve knee stiffness in soccer players with CAI.

Dynamics analysis of a crane with consideration of a load geometry and a rope sling system

In the paper, the authors present a general mathematical model of an open-loop kinematic chain with auxiliary sub-chains in application to the dynamics analysis of an exemplary boom crane. The developed model takes into account the influence of the load geometry (with particular attention to all its inertial moments), the rope sling system, and the flexibility of the drives and the selected link of the crane on the dynamic response of the crane and the load. The dynamics equations are derived using a formalism of joint coordinates, homogeneous transformation matrices, and Lagrange equations of the second kind. The mathematical model has been successfully validated using the commercial MSC.Adams program and the ANSYS-MSC.Adams program interface and then the influence of the aforementioned factors on the behavior of the load and the crane drives is assessed using the kinetic energy, positioning, and RMS indicators.

Effect of Robotic-Assisted Gait at Different Levels of Guidance and Body Weight Support on Lower Limb Joint Kinematics and Coordination.

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip-knee (r = 0.67-0.91), hip-ankle (r = 0.66-0.85), and knee-ankle (r = 0.90-0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics.

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.

Ecological tourism as a promising trend of sustainable development in Georgia: topicality, capabilities, peculiaritie

The article analyzes the prospects for the development of ecological tourism in Georgia, in particular, the opportunities and conditions for the development of sustainable or ecotourism for different regions of Georgia are studied. Each region has distinct characteristics of natural environment, economic level, recreational and ecotourism resources. The article emphasizes that the development of ecotourism contributes to its main resources - the diverse nature of the country and the long-term preservation of a rich culture of life. Economic growth in the regions, providing employment for the local population, improving their well-being and, what is most important for the highland regions, solving the current demographic problem. Development of at least one national park visitor management plan that includes environmental impact and capacity assessments. development of appropriate institutional capacity for sound and efficient operation. creation of mechanisms for joint coordination practices in ecotourism management. Development of ecotourism/recreational development plans in forest areas. Development of national ecotourism standards based on existing international criteria and promotion of their implementation.

Member control efficiency and shape control capability of a loaded cable dome

In this paper, the control efficiency of the members and shape control ability of a loaded adaptive cable dome consisting of 16 cables and 4 struts are investigated. The structure uses specially designed cables and electric telescopic struts integrated with an actuation control system, and all members of the structure are length-adjustable. Initially, an intelligent algorithm is employed to solve the structural shape optimization control model, thereby determining the optimal adjustment amounts. Subsequently, active adjustment and active control tests are conducted to evaluate the control efficiency of the members and the shape control capabilities of the cable dome. The results indicate that the ridge and diagonal cables exhibit the most effective control, followed by the upper and lower hoop cables, while the struts show the lowest control efficiency, primarily influencing the displacement of the joints at their two ends. Moreover, the cables' internal force control efficiency is 3–4 times higher than that of the struts. The ridge and diagonal cables' internal force and displacement control efficiency is about 1.5 times higher than that of the upper and lower hoop cables. The loaded test model exhibits the ability to restore the vertical coordinates of upper joints to their target coordinates, while reducing the internal forces of the components by at least 15 %, and the shape control capability of the structure can be enhanced by incorporating various types of active units. The measured values correlate well with the simulation results, validating the accuracy of the simulations.

Towards reliable multi-person pose estimation using Conditional Random Fields

Multi-person pose estimation is the task of estimating the coordinates of body joints and predicting the body poses for multiple people in an images. This problem has made breakthroughs in recent years, but the solutions still suffer from some shortcomings. A serious weakness of the state-of-the-art models is the number of poses detected by these models which is generally much larger than the actual number of human instances in the input image. This makes the existing models unreliable and thus unusable in real-world tasks. In this paper, we propose a more reliable multi-person pose estimation method consisting of three main blocks: a top-down multi-person pose estimation, a human detection, and a pose selection block. The proposed method incorporates the bounding box of the segmented objects to select the best subset of the initial pose set. We formulate the pose selection problem using Conditional Random Fields. First, we introduce a set of potential functions to form a general probability model. Then an inference algorithm is proposed to select the best poses which maximize the probability function. Finally, the proposed solution is implemented by a neural network. The proposed pose selection model is a model-agnostic method that can be easily used in conjunction with other pose estimation and object detection models. Experiments demonstrate that the reliability and precision of the proposed model are higher than those of the state-of-the-art models.

A general constraint-based programming framework for multi-robot applications

Classic task programming methods based on the specification of desired Cartesian frames can easily generate overconstrained task specifications, reducing the motion capabilities of the involved robot(s) and increasing the total programming effort. This paper presents a general constraint-based programming framework for the specification of a task as minimum set of constraints and the automatic generation of motion optimization problems. The framework can handle constraints involving both robot joint and Cartesian coordinates, as well as including explicit time dependency. The proposed formalism naturally scales to robotic applications with multiple robots, on which multiple frames might be of interest. Additionally, the paper proposes a theoretical comparison with already existing constraint-based programming methods. Finally, the validity and the effectiveness of the proposed approach is numerically supported by illustrative examples, as well as by case studies mocking real industrial setups.

Coordinating upper limbs for octave playing on the piano via neuro-musculoskeletal modeling

Understanding the coordination of multiple biomechanical degrees of freedom in biological organisms is crucial for unraveling the neurophysiological control of sophisticated motor tasks. This study focuses on the cooperative behavior of upper-limb motor movements in the context of octave playing on the piano. While the vertebrate locomotor system has been extensively investigated, the coherence and precision timing of rhythmic movements in the upper-limb system remain incompletely understood. Inspired by the spinal cord neuronal circuits (central pattern generator, CPG), a computational neuro-musculoskeletal model is proposed to explore the coordination of upper-limb motor movements during octave playing across varying tempos and volumes. The proposed model incorporates a CPG-based nervous system, a physiologically-informed mechanical body, and a piano environment to mimic human joint coordination and expressiveness. The model integrates neural rhythm generation, spinal reflex circuits, and biomechanical muscle dynamics while considering piano playing quality and energy expenditure. Based on real-world human subject experiments, the model has been refined to study tempo transitions and volume control during piano playing. This computational approach offers insights into the neurophysiological basis of upper-limb motor coordination in piano playing and its relation to expressive features.

A neuromusculoskeletal modelling approach to bilateral hip mechanics due to unexpected lateral perturbations during overground walking

BackgroundCurrent studies on how external perturbations impact gait dynamics have primarily focused on the changes in the body's center of mass (CoM) during treadmill walking. The biomechanical responses, in particular to the multi-planar hip joint coordination, following perturbations in overground walking conditions are not completely known.MethodsIn this study, a customized gait-perturbing device was designed to impose controlled lateral forces onto the subject’s pelvis during overground walking. The biomechanical responses of bilateral hips were simulated by subject-specific neuromusculoskeletal models (NMS) driven by in-vivo motion data, which were further evaluated by statistical parameter mapping (SPM) and muscle coactivation index (CI) analysis. The validity of the subject-specific NMS was confirmed through comparison with measured surface electromyographic signals.ResultsFollowing perturbations, the sagittal-plane hip motions were reduced for the leading leg by 18.39° and for the trailing leg by 8.23°, while motions in the frontal and transverse plane were increased, with increased hip abduction for the leading leg by 10.71° and external rotation by 9.06°, respectively. For the hip kinetics, both the bilateral hip joints showed increased abductor moments during midstance (20%-30% gait cycle) and decreased values during terminal stance (38%-48%). Muscle CI in both sagittal and frontal planes was significantly decreased for perturbed walking (p < 0.05), except for the leading leg in the sagittal plane.ConclusionThe distinctive phase-dependent biomechanical response of the hip demonstrated its coordinated control strategy for balance recovery due to gait perturbations. And the changes in muscle CI suggested a potential mechanism for rapid and precise control of foot placement through modulation of joint stiffness properties. These findings obtained during actual overground perturbation conditions could have implications for the improved design of wearable robotic devices for balance assistance.

Occlusion-Aware Graph Neural Networks for Skeleton Action Recognition

Due to its important applications in industry, the research of action recognition has attracted many attention. In particular, skeleton-based human action recognition methods have evolved to become more and more competitive. Nevertheless, occlusion is still a very challenging task in human action recognition as yet. Off-the-shelf works are usually based on complete skeleton data, few people consider action recognition in occlusion. For the sake of improving the recognition accuracy on occluded skeleton data, we put forward an occlusion-aware multi-stream fusion graph convolutional network (dubbed MSFGCN). Multiple streams are comprised in MSFGCN, and different occlusion cases can be disposed by different streams. Besides, in order to construct more discriminative features, multi-modal features were extracted simultaneously, such as joint coordinates, relative coordinates, small-scale temporal differences and large-scale temporal differences. In particular, it is the first time to take advantage of motion features at large and small scales at the same time, which helps to distinguish actions with different motion amplitudes. What's more, considering the different importance of different parts of the human body for different action recognition, the content adaptation operation is used to further optimize the recognition performance. Effective training strategies are also presented to further improve the performance of the model. Experimental results show that the proposed MSFGCN has a great advantage over other methods on occluded skeleton datasets. To showcase the effectiveness of different modules, extensive ablation experiments are performed on various skeleton action recognition datasets.

The Association of Age and Sex With Joint Angles and Coordination During Unanticipated Cutting in Soccer Players.

Deficits in movement patterns during cutting while running might place soccer players at risk of injury. The objective was to compare joint angles and intersegment coordination between sexes and ages during an unanticipated side-step cutting task in soccer players. This cross-sectional study recruited 11 male (four adolescents and seven adults) and 10 female (six adolescents and four adults) soccer players. Three-dimensional motion capture was used to measure lower-extremity joint and segment angles as participants performed an unanticipated cutting task. Hierarchical linear models examined relationships between joint angle characteristics with age and sex. Continuous relative phase was used to quantify intersegment coordination amplitude and variability. These values were compared between age and sex groups using analysis of covariance. Adult males had greater hip flexion angle excursions than adolescent males, while adult females had smaller excursions than adolescent females (p = .011). Females had smaller changes in hip flexion angles (p = .045), greater hip adduction angles (p = .043), and greater ankle eversion angles (p = .009) than males. Adolescents had greater hip internal rotation (p = .044) and knee flexion (p = .033) angles than adults, but smaller changes in knee flexion angles at precontact compared with stance/foot off (p < .001). For intersegment coordination, females were more out-of-phase than males in the foot/shank segment in the sagittal plane. There were no differences in intersegment coordination variability between groups. Differences in joint motion during an unanticipated cutting task were present between age groups and sexes. Injury prevention programs or training programs may be able target specific deficits to lower injury risk and improve performance.

Slackline training for Paralympic alpine sit skiers: Development of human-device multi-joint coordination.

Para-alpine sit skiers face unique challenges in balance control due to their disabilities and the use of sit skis. This study assessed their multi-joint coordination before and after slackline training. Nine alpine sit skiers (6 M/3 F; 27 ± 8 years; height: 168.3 ± 6.0 cm; body mass: 55.4 ± 6.9 kg) with different disabilities (LW10-LW12) volunteered for the experiment. All subjects performed slackline training for 5 weeks (20 sessions). Joint kinematics were captured by vision-based markerless motion analysis. Root mean square (RMS) amplitude, mean velocity and mean power frequency (MPF) were evaluated. After training, performance improved significantly with an increase in balance time (1041%, p = 0.002), and a decrease in joint angular velocities and RMS amplitude of the sit ski foot (p < 0.05). Joint synergies were developed through in- or anti-phase movements between joint pairs, particularly involving the hip joints (continuous relative phase angles ~0° or 180°, p < 0.001). Multi-joint coordination shifted from large-RMS amplitude of elbows to low-MPF large-RMS amplitude of the hip and shoulders (p < 0.05), with a significant increase of hip weighting (77.61%, p = 0.031) in the principal component analysis. The coordination was maintained with the change of slackline tension (p < 0.05). Athletes with severe trunk disabilities (LW10) had shorter balance time and poorer coordination than athletes with full trunk functions (LW12). Our findings showed the development of joint coordination involving better control of the hip and sit skis during the challenging slackline training task.

Spatiotemporal Consistency Learning From Momentum Cues for Human Motion Prediction

Extrapolating future human motion based on the historical human pose sequence is the foundation of various intelligent applications. Numerous deep learning-based algorithms have been designed to address this task, achieving state-of-the-art performance on different human motion benchmark datasets. However, most existing methods employ three-dimensional coordinates of joints to demonstrate dynamic motion contexts implicitly. Unfortunately, it remains challenging in capturing motion information from the pose sequence. In this paper, we advocate explicitly describing dynamic contexts via the momentum of human motion mechanic space, as the momentum of a joint is explicit, temporal consistent, and can provide abundant information to the model. In addition, the single-stream methods play a dominant role in the field of human motion prediction. They usually capture motion information via the strategy of continuous or sparse sampling, which might obviate global or detailed local information. Therefore, we present a simple yet effective dual-stream method that can consider both the detailed and global temporal information through a combination of continuous and sparse sampling. The proposed dual-stream paradigm enables the improvement of computational efficiency and the short-term prediction accuracy concurrently. Furthermore, we present a novel temporal attention-based graph convolutional network (TA-GCN) to derive a spatiotemporally consistent motion representation, which can adequately consider the rationality of human body topology. Extensive experiments on two large motion prediction benchmark datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., Human 3.6M and CMU Mocap) show that our algorithm achieves state-of-the-art performance both qualitatively and quantitatively.

Evaluation of a Method to Estimate Foot Eversion Moments During the Support Phase of Running.

Excessive external eversion moments acting on the ankle derived from the ground reaction force (GRF) during the support phase of running are considered a risk factor for overuse lower-limb injuries. The external eversion moment is considered to be dominated by the moment derived from the vertical GRF. However, no studies have directly evaluated the accuracy with which external eversion moment can be estimated with this information. Thus, the objective of this study was to evaluate the extent to which external eversion moment can be estimated from external eversion moment derived from vertical GRF. From three-dimensional foot coordinates and GRF data of 28 healthy participants, we computed external eversion moment (EMrun), center of the ankle coordinates (ANKrun), center of pressure coordinates (COPrun), and vertical GRF (VGRFrun) during the support phase of running. Moreover, we computed center of the ankle joint coordinates (ANKstand) and vertical GRF (VGRFstand) in the resting standing position. A highly significant correlation was observed between EMrun and external eversion moment derived from vertical GRF ([COPrun - ANKrun] × VGRFrun), with a contribution of 84.7%. Moreover, a highly significant correlation was observed between EMrun and (COPrun - ANKstand) × VGRFstand, with a contribution of 81.5%. These results indicate that external eversion moment can be estimated from the external eversion moment derived from vertical GRF with high accuracy. Moreover, it was found that the accuracy did not decrease even if the data of center of ankle and vertical GRF were replaced with the data during standing.

Bias-Compensated Integral Regression for Human Pose Estimation.

In human and hand pose estimation, heatmaps are a crucial intermediate representation for a body or hand keypoint. Two popular methods to decode the heatmap into a final joint coordinate are via an argmax, as done in heatmap detection, or via softmax and expectation, as done in integral regression. Integral regression is learnable end-to-end, but has lower accuracy than detection. This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation. This bias often forces the network to learn degenerately localized heatmaps, obscuring the keypoint's true underlying distribution and leads to lower accuracies. Training-wise, by investigating the gradients of integral regression, we show that the implicit guidance of integral regression to update the heatmap makes it slower to converge than detection. To counter the above two limitations, we propose Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias. BCIR also incorporates a Gaussian prior loss to speed up training and improve prediction accuracy. Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.

Age related changes in lower-limb joint coordination during gait in children with bilateral cerebral palsy

Age related changes in lower-limb joint coordination during gait in children with bilateral cerebral palsy

AiShifu: AI Karate Pose Trainer Using Human Pose Estimation

One application of the artificial intelligence (AI) technology is self-guided physical activities where a computing device acts as a trainer. One key challenge for these applications is how to measure the performance of such an AI trainer, especially when the AI trainer is run on a generic PC or a mobile device. In the spirit of the Turing test, an AI trainer should mimic the behavior of a human trainer. A good human trainer generally considers the training history and the level of the trainee when providing feedback, which requires more than body position analysis. In this work, we built a Martial Art trainer application called AIShifu that helps users practice martial art poses using Human Pose Estimation (HPE). We chose an open-source neural network called HRNET trained with MS-COCO dataset as the core of the HPE. The joint coordinates and angles were used to identify the pose being practiced by the trainee, whether the active side is left or right, and how close is the key joint angle to that from a “golden” image. We collected data from both a black belt martial artist and a novice trainee and on three Karate poses. Based on the data, it is clear that the blackbelt performed the poses more consistently. A much larger sample size was required to test how well an AI trainer can discern the difference between trainees with different levels of proficiency. This understanding forms the foundation to customize AI trainer softwares for different users.