Due to the limitations of traditional laser processing technology, which is confined to two-dimensional planes, it is difficult to meet the demands of complex curved surface laser processing. This study proposes a laser processing robot digital twin system for complex curved surfaces. Two surface trajectory generation strategies are introduced: the surface projection algorithm based on a scanning model supports the generation of unknown surface trajectories, and the STEP model analysis method based on OCC(Opencascade) enables trajectory generation for user-defined surfaces. Furthermore, a multi-task digital twin system is developed, integrating dynamic simulation of surface trajectories and real-time mapping of laser processing. Experimental verification shows that the system successfully realizes the full-process monitoring of graphene wire preparation by laser induction and arc additive manufacturing on complex curved surfaces.

Currently, robots face significant challenges in independently completing tasks within dynamic and unstructured environments. Teleoperation systems that utilize exoskeletons as input devices present an effective solution to this issue. This paper introduces an ergonomic 7-degree-of-freedom (7-DOF) exoskeleton device and develops an immersive teleoperation system integrated with a virtual reality (VR) head-mounted display (HMD). In this system, the operator, serving as the master side, dons the exoskeleton to issue control commands to the slave-side robot while leveraging feedback from both the exoskeleton and the VR HMD for cognitive decision-making. This closed-loop teleoperation system provides a multi-sensory feedback experience that integrates visual and haptic sensations, significantly enhancing operational stability and accuracy. Furthermore, for force feedback control, we propose a strategy based on environmental parameter estimation in conjunction with Weber’s law, allowing for self-adaptive adjustments of force feedback mapping in response to varying environmental conditions. Experimental results indicate that operators experience a high level of immersion with this system and successfully complete tasks such as remote ultrasound detection. The system demonstrates superior performance in terms of stability, accuracy, and user adaptability, highlighting its potential for complex remote operations in dynamic and unstructured environments.

Tele-homecare has become a promising approach to meet the growing demand for elderly and disability care. In such a context, ensuring human-robot interaction safety during teleoperation poses a critical challenge. Existing teleoperation control approaches focus solely on the robot’s end-effector trajectory, failing to handle inevitable or even desirable contacts on other robot links. This paper proposes a teleoperated shared-control strategy to deal with this challenge. A lightweight exoskeleton is developed to teleoperate the robot and give force feedback to the operator. Additionally, an exoskeleton-based shared-control strategy is proposed to integrate operator commands with real-time proximity sensing information, allowing the robot to avoid collisions while executing tasks. To react to inevitable contact, the force feedback function is incorporated into the proposed strategy to enable the operator to experience intuitive contact. Comparative experiments and a demonstration are designed to evaluate the feasibility and reliability of the proposed strategy in a tele-homecare scenario. Compared to the traditional teleoperation strategy, the proposed method can greatly reduce the contact forces on the robot’s links, indicating the potential of the proposed strategy in advancing safety in tele-homecare systems.

Performing unstructured grasping tasks in cluttered or obstacle-rich food processing environments is a key challenge in robotic systems. This work presents a task-adaptive grasping approach for a dual-arm anthropomorphic robot, named Herdsman, developed for the food industry. With an articulated torso, Herdsman is able to perform human-like reaching motions for more flexible grasping operations. To recognize the target object and extract the features for grasping, a vision pipeline, including a lightweight network GDC-YOLO for real-time object detection and a U-ReSENet network for grasping detection enhancement, is designed based on convolutional neural networks. After the detection comes the grasp execution, where a task-adaptive grasping strategy that works with the articulated torso is put forward to carry out grasping tasks in unstructured environments. Comparative experiments are designed to evaluate the detection performance between the proposed network and other popular networks for object detection and grasping detection. In addition, the task-adaptive grasp strategy for the Herdsman robot is experimentally validated by grasping the objects at different heights. The results have shown that the task-adaptive grasping solution exhibits robustness against variations in the target object position, which could be a promising approach for its application in unstructured environments requiring autonomous grasping.

The development of wireless communication has indeed promoted cloud-fog automation in the industry. It also introduces new issues of reliability and latency for control systems. This study sought to investigate the impacts of the commonly used industrial wireless network on the control performance parameters using a ball-and-beam (BB) time-critical balancing control system. An internal model control-inspired latency-aware wireless control framework (IMC-LA) is presented and employed in the BB system to handle the time delays and instability-creating elements introduced by wireless communication. A preliminary control assessment of the BB system under two new-generation wireless technologies, Wi-Fi 6 and 5G, was delivered. The correlation between network performance and control performance was analyzed statistically compared to the wired Ethernet condition. Test results show a dramatic decrease in position error after utilizing the proposed latency-aware wireless control framework. This study provides practical insights into the potential impacts of industrial wireless networks on control systems with a workable latency-aware wireless control approach. The methodology presented in this work has the potential to expedite the adoption of wireless communication in time-critical control. Note to Practitioners—Many factory automation processes experience undesirable latencies when transitioning from classic architecture to cloud automation architecture, particularly with the implementation of wireless communication. This paper aims to address the potential instability problem introduced by practical wireless networks and proposes a latency-aware control framework using the concept of internal model control. Meanwhile, this work also provides a way for practical operators to explore the relationship between critical parameters of communication networks and control performance parameters. The purpose of this paper is not to judge which wireless technology is better, instead we only want to demonstrate the effectiveness of the proposed latency-aware control in improving control performance under various wireless scenarios. The proposed framework is verified using the BB system for the commonly used cascaded PID control under two advanced wireless networks, 5G and Wi-Fi 6. It is also applicable to other industrial wireless networks with millisecond-class latency in the other regulatory control cases. The proposed IMC-LA wireless control framework reduces the significant effort and cost associated with constructing and tuning the controller in a real control system.

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.

As many robot applications become more reliant on wireless communications, wireless network latency and reliability have a growing impact on robot control. This article proposes a network hardware-in-the-loop (N-HiL) simulation framework to evaluate the impacts of wireless on robot control more efficiently and accurately, and then improve the design by employing correlation analysis between communication and control performances. The N-HiL method provides communication and robot developers with more trustworthy network conditions, while the huge efforts and costs of building and testing the entire physical robot system in real life are eliminated. These benefits are showcased in two representative latency-sensitive applications: 1) safe multirobot coordination for mobile robots, and 2) human-motion-based teleoperation for manipulators. Moreover, we deliver a preliminary assessment of two new-generation wireless technologies, the Wi-Fi6 and 5G, for those applications, which has demonstrated the effectiveness of the N-HiL method as well as the attractiveness of the wireless technologies.

More and more robot automation applications have changed to wireless communication, and network performance has a growing impact on robotic systems. This study proposes a hardware-in-the-loop (HiL) simulation methodology for connecting the simulated robot platform to real network devices. This project seeks to provide robotic engineers and researchers with the capability to experiment without heavily modifying the original controller and get more realistic test results that correlate with actual network conditions. We deployed this HiL simulation system in two common cases for wireless-network-controlled robotic applications: (1) safe multi-robot coordination for mobile robots, and (2) human-motion-based teleoperation for manipulators. The HiL simulation system is deployed and tested under various network conditions in all circumstances. The experiment results are analyzed and compared with the previous simulation methods, demonstrating that the proposed HiL simulation methodology can identify a more reliable communication impact on robot systems.

With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.

In real-time feedback control systems, control performance, e.g., control cost and tracking error, is significantly affected by information freshness, which in turn relies heavily on the design of the feedback update policy. In this paper, we study the update policy for real-time feedback control systems. We first discuss the relationship between the age of information (AOI) and control performance, where AOI represents the level of "dissatisfaction" for information staleness. We find that minimizing AOI is not always equivalent to optimizing the control performance. Then, a new metric, called the value of information (VOI), is proposed to evaluate the timeliness of system update by linking AOI to the decay rate of the control system. By maximizing VOI, we design a new update policy, called the α - wait, which has superiorities in improving both control performance and communication cost. Finally, simulation results verify our method.

With the development of smart sensing and the aging trend of the society, telerobot is playing an increasingly important role in the field of homecare service. In this article, we present the design, implementation, and evaluation of a wearable sensor glove. The sensor glove is based on inertial measurement units (IMUs) with 9 axes. We put 6 nodes on the glove and calculate the quaternions of all the orientations of the knuckles with fusion algorithm and interpolation algorithm. The main goal of this research is to develop a human-robot interface in order to help to control the telerobot remotely. This paper mainly deals with three issues: 1) designing a simple and extensible motion capture system architecture, 2) choosing a simple but effective fusion algorithm for motion capture, 3) performance evaluation of the sensor glove based on IMUs. The experimental results suggest that the sensor glove we designed can meet the requirements of speed and accuracy. Furthermore, the architecture we designed can not only be used on the sensor glove, it can also be extended to up to 128 nodes to capture the motion of the hole body.

With the development of intelligent sensing and human-robot interaction technology, homecare robots play an increasingly important role in the field of homecare services. At the same time, the interaction between the operator and the homecare robot is particularly valued. This paper proposed a homecare robot interaction system based on a wearable inertial motion capture device. In this system, the wearable motion capture device is used to capture the operator's motion signals. After processing the motion signals and recognizing the corresponding motion poses, the system controls the homecare robot to imitate the intention of the operator. In this paper, we focused on controlling the movement of the homecare robot through the operator's lower limb motion data and designed a novel gait recognition algorithm. The proposed system was evaluated experimentally, proving that the system has strong performance and practicality.

Recent emergence of “microblogging” services has been driving cyber-physical social system (CPSS) as a hot topic in real-world applications. How to efficiently detect and recognise spam and fake accounts becomes an important task where it requires analysis of microblog user behavior and prediction of their activity. This article attempts to investigate this challenge by proposing a new strategy to effectively model microblogging user activity and dynamically predicting their activities for the CPSS applications. We first analysis and define a set of benchmarks for measuring microblogging user activeness in considering serval key dynamic attributes including change rate of microblogging numbers, user attentions, etc. Then, we build up a new dynamic microblogging user activity prediction model (DUAPM) based on three important characteristics: personal information, social relationship, and user interaction. Finally, an improved logical regression algorithm is proposed for training the model and predicting user activity. Under the evaluation of a sample dataset containing Sina Weibo 3621 users over 20 weeks, it shows that our model deliver average up to 3% higher prediction accuracy than other social media user activity prediction models using traditional logical regression and random forest algorithms. We also take out a CPSS case study of evaluating DUAPM models for analysis and prediction of Twitter users' activity over 16 countries. The results show that our model effectively reflects the distribution and trends of Twitter users' activity with different background and cultures.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the fastest growing fields of robotics. The combination of robot capabilities with human intelligence, for example, telerobotics for home care, is gradually showing promising potentials. In this paper, the Home-TeleBot system, a generalized IoT-enabled telerobotic architecture designed to support home-centric healthcare system, is proposed. In particular, the implementation of it is realized by integrating human-motion-capture subsystem with robot-control subsystem. The dual-arm cooperative robot, YuMi, imitates human motion captured by a set of wearable inertial motion capture devices to complete tasks. The proposed approach using workspace mapping and path planning of robot manipulators, facilitates telerobot to execute tasks in a natural and human-like way. Based on the constant of proportionality calculated by comparing the human original workspace with the robot original workspace, the workspace mapping is achieved by making assumptions of the distance between end-effectors (human hands, robot's grippers) and shoulders. Additionally, robot manipulators' path is planned by setting virtual obstacles to constrain robot motion, which aims to improve the performance of robot's human-like motion. As a specific example of application, we apply the proposed architecture to a fetching task based on dual-arm motion capture and mapping for telerobotics in home care.

Evolution of smart sensing technologies provide an increasingly utilization for IoT-enabled healthcare. In the context of the aging population, the demand for elderly-assistant robots is increasing. At the same time, more and more attention has been paid to the more intuitive way of remote human-robot interaction. In this article, we present the design, implementation, and evaluation of a telerobotic-assisted healthcare system with the ability to achieve the remote elderly assistant and healthcare application. In this work, a remote operation interface using wearable inertial motion capture suit is proposed to control the YuMi robot remotely. The motion capture subsystem and the robot control subsystem are all based on robot operation system (ROS) to carry out the distributed design and integration. The robot arm is controlled by the position and orientation data of the operator's hand acquired by the motion capture suit. Furthermore, the robot's gripper is controlled by the finger bending signal acquired by a data glove. The achievement and performance of the introduced system was verified by experiments.

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.

This paper presents a wearable biopatch prototype for body surface potential measurement. It combines three key technologies, including mixed-signal system on chip (SoC) technology, inkjet printing technology, and anisotropic conductive adhesive (ACA) bonding technology. An integral part of the biopatch is a low-power low-noise SoC. The SoC contains a tunable analog front end, a successive approximation register analog-to-digital converter, and a reconfigurable digital controller. The electrodes, interconnections, and interposer are implemented by inkjet-printing the silver ink precisely on a flexible substrate. The reliability of printed traces is evaluated by static bending tests. ACA is used to attach the SoC to the printed structures and form the flexible hybrid system. The biopatch prototype is light and thin with a physical size of 16 cm × 16 cm. Measurement results show that low-noise concurrent electrocardiogram signals from eight chest points have been successfully recorded using the implemented biopatch.

This paper presents a fully integrated application specific integrated circuit (ASIC) sensor for the recording of multiple bio-electric signals. It consists of an analog front-end circuit with tunable bandwidth and programmable gain, a 6-input 8-bit successive approximation register analog to digital converter (SAR ADC), and a reconfigurable digital core. The ASIC is fabricated in a 0.18-µm 1P6M CMOS technology, occupies an area of 1.5 × 3.0 mm2, and totally consumes a current of 16.7 µA from a 1.2 V supply. Incorporated with the ASIC, an Intelligent Electrode can be dynamically configured for on-site measurement of different bio-signals. A 2-wire data transmission protocol is also integrated on chip. It enables the serial connection over a group of Intelligent Electrodes, thus minimizes the number of connecting cables. A wearable healthcare system is built upon a printed Active Cable and a scalable number of Intelligent Electrodes. The system allows synchronous processing of maximum 14-channel bio-signals. The ASIC performance has been successfully verified in in-vivo bio-electric recording experiments.

This paper presents the prototype implementation of a Bio-Patch using fully integrated low-power System-on-Chip (SoC) sensor and paper-based inkjet printing technology. The SoC sensor is featured with programmable gain and bandwidth to accommodate a variety of bio-signals. It is fabricated in a 0.18-ìm standard CMOS technology, with a total power consumption of 20 ìW from a 1.2 V supply. Both the electrodes and interconnections are implemented by printing conductive nano-particle inks on a flexible photo paper substrate using inkjet printing technology. A Bio-Patch prototype is developed by integrating the SoC sensor, a soft battery, printed electrodes and interconnections on a photo paper substrate. The Bio-Patch can work alone or operate along with other patches to establish a wired network for synchronous multiple-channel bio-signals recording. The measurement results show that electrocardiogram and electromyogram are successfully measured in in-vivo tests using the implemented Bio-Patch prototype.