Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

To maximize user experience in VR environments, optimizing the comfortability of head-mounted displays (HMDs) is essential. To date, few studies have investigated the fatigue induced by wearing commercially available HMDs. Here, we focus on the effects of HMD weight and balance on the physical load experienced by the user. We conducted an experiment in which participants completed a shooting game while wearing differently weighted and balanced HMDs. Afterwards, the participants completed questionnaires to assess levels of discomfort and fatigue. The results clarify that the weight of the HMD affects user fatigue, with the degree of fatigue varying depending on the center of mass position. Additionally, they suggest that the torque at the neck joint corresponds to the physical load imparted by the HMD. Therefore, our results provide valuable insights, demonstrating that, to improve HMD comfortability, it is necessary to consider both the balance and reduction of weight during HMD design.

Human motion capture (MoCap) plays a key role in healthcare and human–robot collaboration. Some researchers have combined orientation measurements from inertial measurement units (IMUs) and positional inference from cameras to reconstruct the 3D human motion. Their works utilize multiple cameras or depth sensors to localize the human in three dimensions. Such multiple cameras are not always available in our daily life, but just a single camera attached in a smart IP devices has recently been popular. Therefore, we present a 3D pose estimation approach from IMUs and a single camera. In order to resolve the depth ambiguity of the single camera configuration and localize the global position of the subject, we present a constraint which optimizes the foot-ground contact points. The timing and 3D positions of the ground contact are calculated from the acceleration of IMUs on foot and geometric transformation of foot position detected on image, respectively. Since the results of pose estimation is greatly affected by the failure of the detection, we design the image-based constraints to handle the outliers of positional estimates. We evaluated the performance of our approach on public 3D human pose dataset. The experiments demonstrated that the proposed constraints contributed to improve the accuracy of pose estimation in single and multiple camera setting.

In this paper, as a practical approach to building a human-centered cyber-physical system (CPS), we propose a software platform that integrates hardware and software materials to realize a human digital twin (HDT) including model construction, data acquisition, analysis, and intervention in terms of the physical load and physical capabilities of humans. Furthermore, as a case study of this platform in industrial applications, we introduce an example of a human-centered CPS in which humans and robots work together to realize better human workability and production line productivity within the system.

In this paper we introduce a new model of the mechanics of a finger pad that is efficient to simulate. The model simplifies the finger pad mechanics by assuming it to be a thin shell enclosing fluid-like material. Unlike similar approaches in the literature, we incorporate the bending stiffness of the skin and a subject-specific geometric model into the simulation. Thanks to these two features, our model can predict the surface deflection of the whole finger pad with the error of 0.2 to 0.4 mm with reasonable computational cost.