Following the rule of human-centricity, Human Motion Digital Twin (HMDT) attempts to apply human motion data to ensure the development and well-being of human beings. Particularly, perception and estimation of human motion play fundamental roles in realizing HMDT. This work proposes an inertial motion capture system for human motion digital twin (InMoDT). The designed motion capture device is made up of a hub node and inertial measurement units attached to the human body. The proposed algorithm framework supported by sensor fusion and pose calibration algorithms, enables to acquire orientations of sensors and body segments. With the deployment of algorithms, InMoDT achieves an average root mean square error of 4.7$^{\circ}$in estimating orientations when compared with an optical motion capture system. Experimental results show a great correlation ($92.5\%$) and agreement ($97.8\%$) between InMoDT and the optical system. The abilities of InMoDT are spotted in terms of human-centric applications based on the integration of human, cyber system, and physical system, such as motion monitoring and estimation, and human-robot teleoperation.Note to Practitioners—This paper is motivated by the problem of inadequate attention on humans in Cyber-Physical System (CPS) while the roles of operators have a significant effect on industry. With the popular applications of digital twins in CPS, HMDT is expected to monitor, analyze, and assess motion data for facilitating the Human-Cyber-Physical System (HCPS) In this research work, the authors present a system for whole-body motion capture. The proposed system based on a wearable inertial sensor-based device provides a solution to construct HMDT. Moreover, a novel algorithm framework is employed, which consists of the sensor fusion algorithm and the kinematic constraints-based pose calibration algorithm. Experimental results demonstrate the system’s effectiveness in motion sensing accuracy, correlation, and agreement in comparison with the gold standard. The validated applications of the system lie in motion monitoring and estimation, and human-robot teleoperation, showing the potential for enhancing HMDT.

Human-centric cyber physical system has become one of the most promising approaches in Industry 4.0. Humans are not obtained adequate attention in the traditional industrial process although humans serve as the orchestrator and beneficiary in the system. Human-centric physical system pertains to a safe and beneficial working environment, to the respect of human rights. This paper implements the inertial motion capture system into the industrial process, which supports to monitor human’s motion and recognize working activities regarding the assessment of the ergonomic performances. The inertial motion capture system integrates wearable inertial measurement units and the Unity3D application together for reconstructing human motion. Quaternion-based calibration algorithm is employed to achieve sensor-to-body segment alignment. Convolutional neural network based model classifies different activities of an assembly task when motion data are input of the deep learning network. The feasibility of the proposed system is validated by experiments.