The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently. As a key part of the shop-floor, humans' high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior. Therefore, the modeling system for cross-scale human behavior in digital twin shop-floors was developed, powered by the data fusion of macro-behavior and micro-behavior virtual models. Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position. Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation. In this study, we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors. Based on this theoretical system, we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling. Lastly, we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors. Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

Kineto-Static Analysis and Design Optimization of a 3-DOF Wrist Rehabilitation Parallel Robot with Consideration of the Effect of the Human Limb

When designing rehabilitation robots, there remains the challenge of ensuring the comfort and safety of users, especially for wearable rehabilitation robots that interact with human limbs. In this paper, we present a kineto-static analysis of the 3-RPS parallel wrist rehabilitation robot, taking into account the soft characteristics of the human limb and its kinematic mobility. First, the human upper-limb model was made to estimate the interaction force and moment through inverse kinematic analysis. Second, a static analysis was conducted to obtain the force and moment acting on the human limb, which is directly related to the user’s comfort and safety. Then, the design parameters of the 3-RPS robot were obtained by generic optimization through kineto-static analysis. Finally, the influence of the parasitic motion of the 3-RPS robot and the initial offset between the wrist center and the robot moving platform were discussed. Through the analysis results, we provide effective solutions to ensure the safety and comfort of the user.

Tissues and Cell Types of Appendage Regeneration: A Detailed Look at the Wound Epidermis and Its Specialized Forms

Therapeutic implementation of human limb regeneration is a daring aim. Studying species that can regrow their lost appendages provides clues on how such a feat can be achieved in mammals. One of the unique features of regeneration-competent species lies in their ability to seal the amputation plane with a scar-free wound epithelium. Subsequently, this wound epithelium advances and becomes a specialized wound epidermis (WE) which is hypothesized to be the essential component of regenerative success. Recently, the WE and specialized WE terminologies have been used interchangeably. However, these tissues were historically separated, and contemporary limb regeneration studies have provided critical new information which allows us to distinguish them. Here, I will summarize tissue-level observations and recently identified cell types of WE and their specialized forms in different regeneration models.

ARMA-Based Segmentation of Human Limb Motion Sequences

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.

Management of congenital limb deficiency

A broad range of abnormalities can affect the developing human limb. This chapter includes an overview of limb bud development, the mechanisms involved in normal growth, and congenital skeletal deficiencies of the lower limb. The use of ultrasonography in antenatal screening and the clinical and radiological features in childhood are also discussed in addition to management including surgical reconstruction and prosthetic use.