ON RANDOM EXPECTED VALUES VARIATIONS OF TRIBOLOGY PARAMETERS IN HUMAN HIP JOINT SURFACES

This paper presents recent progress in the knowledge concerning the stochastic theory of bio- hydrodynamic lubrication with a phospholipids bilayer. On the basis of experimental measurements and analytical solutions, the research concerns the determination of the random expectancy values of load carrying capacity, the friction coefficient, and synovial fluid dynamic variations. After numerous measurements, it directly follows that the random density function of the gap height in the human joint usually indicates a disorderly increases and decreases in the height. Such irregular gap height variations have an important influence on the random synovial bio-fluid dynamic viscosity. This finally leads to the friction coefficient and cartilage wear changes of cooperating bio- surfaces. The main topic of this paper relates to the expectancy values of the tribology parameters localized inside the variable stochastic standard deviation intervals of the human joint gap height. The results obtained finally indicate the influence of the random roughness and growth of living biological cartilage surfaces on the expectancy values of the synovial fluid dynamic viscosity, load carrying capacity and friction forces in human hip joints.

Coordinating social action: A primer for the cross-species investigation of communicative repair

Human joint action is inherently cooperative, manifested in the collaborative efforts of participants to minimize communicative trouble through interactive repair. Although interactive repair requires sophisticated cognitive abilities, it can be dissected into basic building blocks shared with nonhuman animal species. A review of the primate literature shows that interactionally contingent signal sequences are at least common among species of nonhuman great apes, suggesting a gradual evolution of repair. To pioneer a cross-species assessment of repair this paper aims at (i) identifying necessary precursors of human interactive repair; (ii) proposing a coding framework for its comparative study in humans and nonhuman species; and (iii) using this framework to analyse examples of interactions of humans (adults/children) and nonhuman great apes. We hope this paper will serve as a primer for cross-species comparisons on dealing with communicative breakdowns.

Role of Human Body Posture Recognition Method Based on Wireless Network Kinect in Line Dance Aerobics and Gymnastics Training

With the rapid development of the information society, human body gesture recognition has become an important technology for human-computer interaction. This paper combines Kinect’s human bone monitoring technology with auxiliary gymnastics training. The gymnastics and dance training can correct students’ wrong movements in time through feedback and improve the training efficiency, so as to meet the needs of nature and harmony of human-machine interaction. In this paper, based on the wireless network Kinect, the human body posture recognition method and tracking technology are studied, and the joint point angle representation method based on the fixed axis is proposed, and the posture recognition method based on the joint point angle is improved, which can accurately recognize the human body posture. Aiming at the situation that the human joint points are occluded, the human joint point repair algorithm is improved. The algorithm is based on the proportion of human bone nodes and the characteristics of human motion, and based on geometric principles, it repairs the occluded points. The feasibility of the original joint point data, angle feature, and distance feature in expressing human behavior is analyzed through experiments, a standard gymnastics movement database is established, and new gymnastics movements can be entered at any time. A gymnastics auxiliary training system is designed, which can analyze and evaluate the exercises of the trainer from the joint point coordinates and the angle formed by the joints and provide the trainer with intuitive error correction prompts. The human body posture recognition method studied in this paper can accurately give the difference between the trainer’s movement and the standard movement, and the trainer can adjust the movement posture according to the prompts, improve the level of gymnastics, and achieve the purpose of auxiliary training. Experiments show that the algorithm model has an accuracy rate of 95.7% for human body posture recognition, and it plays a huge role in line dance aerobics and gymnastics training.

At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance

Pedestrian detection has always been a research hotspot in the Advanced Driving Assistance System (ADAS) with great progress in recent years. However, for the ADAS, we not only need to detect the behavior of pedestrians in front of the vehicle but also predict future action and the motion trajectory. Therefore, in this paper, we propose a human key point combined optical flow network (KPOF-Net) in the vehicle ADAS for the occlusion situation in the actual scene. When the vehicle encounters a blocked pedestrian at a traffic intersection, we used self-flow to estimate the global optical flow in the image sequence and then proposed a White Edge Cutting (WEC) algorithm to remove obstructions and simply modified the generative adversarial network to initialize pedestrians behind the obstructions. Next, we extracted pedestrian optical flow information and human joint point information in parallel, among which we trained four human key point models suitable for traffic intersections. At last, KPOF-GPDM fusion was proposed to predict the future status and walking trajectories of pedestrians, which combined optical flow information with human key point information. In the experiment, we did not merely compare our method with other four representative approaches in the same scene sequences. We also verified the accuracy of the pedestrian motion state and motion trajectory prediction of the system after fusion of human joint points and optical flow information. Taking into account the real-time performance of the system, in the low-speed and barrier-free environment, the comparative analysis only uses optical flow information, human joint point information, and KPOF-Net three prediction models. The results show that (1) in the same traffic environment, our proposed KPOF-Net can predict the change of pedestrian motion state about 5 frames (about 0.26 s) ahead of other excellent systems; (2) at the same time, our system predicts the trajectory of the pedestrian more accurately than the other four systems, which can achieve more stable minimum error ±0.04 m; (3) in a low-speed, barrier-free experimental environment, our proposed trajectory prediction model that integrates human joint points and optical flow information has higher prediction accuracy and smaller fluctuations than a single-information prediction model, and it can be well applied to automobiles’ ADAS.