Kinematic Motor Synergy Analysis to Understand Lock Dance Choreographies.

Lock dance, or locking, is one of the popular old-school street dance styles featuring sharp, sudden, and isolated body movements through intricate control and coordination of joints and muscles. This work aims to understand the complex lock dance motions based on kinematic motor synergy analysis. Lock dance motions performed by three experienced dancers were measured with a markerless human motion capture technique. The motor synergies were identified and summarized using principle component analysis (PCA). The motion complexity, joint contributions, and motor coordination of ten basic lock dance choreographies were analyzed based on the synergy patterns and their activations. The results enhance our understanding of complex dance motions and serve as a step toward future applications to, e.g. dance skill or injury risk assessments.

Knock-down of IGFBP2 ameliorates lung fibrosis and inflammation in rats with severe pneumonia through STAT3 pathway.

OBJECTIVE: To observe the mechanism of IGFBP2 knock-down in improving lung fibrosis and inflammation through STAT3 pathway in rats with severe pneumonia. MATERIALS AND METHODS: First, SP rat model was established. Then rats were divided into the Control group, the SP group, the SP + Lv-vector shRNA group, the SP + Lv-IGFBP2 shRNA group, the SP + Lv-vector group, and the SP + Lv-IGFBP2 group. The mRNA and protein levels of IGFBP2, NOS, CD206 and Arg 1 were detected by RT-qPCR and Western blot. IHC was used to check the positive expression of IGFBP2 and MCP1. A fully automated blood gas analyzer was used to detected PaCO2, CO2 content, PaO2 and SaO2. HE and Masson staining were performed to observe the lung tissue injury and collagen deposition of rats in each group. ELISA assays were used to calculate the levels of inflammatory factors IL-1ß, IL-6, TNF-α, IL-4, and IL-10. Flow cytometry was conducted to acquire the ratio of M1-type AMs and M2-type AMs. RESULTS: Compared with the Control group, IGFBP2, iNOS, CD206, and Arg1 mRNA and protein expression levels, IGFBP2 and MCP1 positive expressions, PaCO2, p-STAT3/STAT3, p-JAK2/JAK2, IL-1ß, IL-6, and TNF-α levels, the number of AMs and neutrophils, the proportion of M1 type AMs and the expressions of α-SMA, Collagen-I, Collagen III, and Fibronectin were significantly increased in SP rats (p < 0.05), while PaCO2, CO2, and SaO2, IL-4 and IL-10 levels, and the proportion of M2 type AMs decreased (p < 0.05). However, the knockdown of IGFBP2 reversed the above index trends. CONCLUSION: Knock-down of IGFBP2 ameliorated lung injury in SP rats, inhibited inflammation and pulmonary fibrosis, and promoted M2-type transformation of AMs by activating the STAT3 pathway.

Self-organizing neural network for reproducing human postural mode alternation through deep reinforcement learning.

A self-organized phenomenon in postural coordination is essential for understanding the auto-switching mechanism of in-phase and anti-phase postural coordination modes during standing and related supra-postural activities. Previously, a model-based approach was proposed to reproduce such self-organized phenomenon. However, if we set this problem including the process of how we establish the internal predictive model in our central nervous system, the learning process is critical to be considered for establishing a neural network for managing adaptive postural control. Particularly when body characteristics may change due to growth or aging or are initially unknown for infants, a learning capability can improve the hyper-adaptivity of human motor control for maintaining postural stability and saving energy in daily living. This study attempted to generate a self-organizing neural network that can adaptively coordinate the postural mode without assuming a prior body model regarding body dynamics and kinematics. Postural coordination modes are reproduced in head-target tracking tasks through a deep reinforcement learning algorithm. The transitions between the postural coordination types, i.e. in-phase and anti-phase coordination modes, could be reproduced by changing the task condition of the head tracking target, by changing the frequencies of the moving target. These modes are considered emergent phenomena existing in human head tracking tasks. Various evaluation indices, such as correlation, and relative phase of hip and ankle joint, are analyzed to verify the self-organizing neural network performance to produce the postural coordination transition between the in-phase and anti-phase modes. In addition, after learning, the neural network can also adapt to continuous task condition changes and even to unlearned body mass conditions keeping consistent in-phase and anti-phase mode alternation.

Classification of Human Balance Recovery Strategies Through Kinematic Motor Synergy Analysis.

A key problem in human balance recovery lies in understanding the mechanism of balance behavior with redundant bio-mechanical motors. Motor synergy has been known as an efficient tool to analyze characteristics of motion behavior and reconstruct control command. In this paper, motor synergy analysis for different control strategies is proposed to analyze different balance motion coordination for various levels of pushing force, and understand the coordination of human multiple joints regarding balance recovery. The spatial synergy of specific joint angles for different pushing force levels exerted on the subject's back is computed with the principal component analysis (PCA) to evaluate the adaptive balance motion response patterns and illustrate the improvement of balance robustness through the switch of joint coordination. Therefore, the switch of postural coordination over multiple joints in balance recovery movements was analyzed to better understand the mechanism of balance strategy generation in this study.

Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control.

The study of human balance recovery strategies is important for human balance rehabilitation and humanoid robot balance control. To date, many efforts have been made to improve balance during quiet standing and walking motions. Arm usage (arm strategy) has been proposed to control the balance during walking motion in the literature. However, limited research exists on the contributions of the arm strategy for balance recovery during quiet standing along with ankle and hip strategy. Therefore, in this study, we built a simplified model with arms and proposed a controller based on nonlinear model predictive control to achieve human-like balance control. Three arm states of the model, namely, active arms, passive arms, and fixed arms, were considered to discuss the contributions of arm usage to human balance recovery during quiet standing. Furthermore, various indexes such as root mean square deviation of joint angles and recovery energy consumption were verified to reveal the mechanism behind arm strategy employment. In this study, we demonstrate to computationally reproduce human-like balance recovery with and without arm rotation during quiet standing while applying different magnitudes of perturbing forces on the upper body. In addition, the conducted human balance experiments are presented as supplementary information in this paper to demonstrate the concept on a typical example of arm strategy.