In the dynamic landscape of healthcare, Digital Twin (DT) technology has emerged as a transformative force, holding the promise of revolutionizing patient care and industry practices. This article surveys the literature over the period 2020 to 2023 on a comprehensive exploration of DT in healthcare, elucidating its roles, benefits, and implications for smart personalized healthcare. The study addresses fundamental questions concerning the transformative potential of DT, investigating its varied roles and benefits in healthcare, its revolutionary impact on the industry, and the essential requirements for crafting a DT system tailored to the demands of smart personalized healthcare. The research further unveils the key layers necessary for implementing a DT smart healthcare system, examining potential applications that extend from diagnostics to treatment strategies. Methodologically, the paper navigates through different model discussions, providing a structured approach to understanding the implementation of DT in healthcare. Despite the transformative potential, the research delves into the limitations and challenges faced by DT technology, offering a balanced perspective on its current state. In conclusion, the paper synthesizes key findings, outlines methodologies, discusses challenges, and sets the stage for future research, presenting a holistic overview of the potential, pitfalls, and pathways for integrating DT in the healthcare industry.

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.

Over the years, gaze input modality has been an easy and demanding human–computer interaction (HCI) method for various applications. The research of gaze-based interactive applications has advanced considerably, as HCIs are no longer constrained to traditional input devices. In this paper, we propose a novel immersive eye-gaze-guided camera (called GazeGuide) that can seamlessly control the movements of a camera mounted on an unmanned aerial vehicle (UAV) from the eye-gaze of a remote user. The video stream captured by the camera is fed into a head-mounted display (HMD) with a binocular eye tracker. The user’s eye-gaze is the sole input modality to maneuver the camera. A user study was conducted considering the static and moving targets of interest in a three-dimensional (3D) space to evaluate the proposed framework. GazeGuide was compared with a state-of-the-art input modality remote controller. The qualitative and quantitative results showed that the proposed GazeGuide performed significantly better than the remote controller.

Human action recognition (HAR) technology is receiving considerable attention in the field of human-computer interaction. We present a HAR system that works stably in real-world applications. In real-world applications, the HAR system needs to identify detailed actions for specific purposes, and the action data includes many variations. Accordingly, we conducted three experiments. First, we tested our recognition system’s performance on the UTD-MHAD dataset. We compared our system’s accuracy with results from previous research and confirmed that our system achieves an average performance among recognition systems. Furthermore, we hypothesized the use of a HAR system to detect burglary. In the second experiment, we compared the existing benchmark data with our crime detection dataset. We recognized the test scenarios’ data by using the recognition system trained by each dataset. The recognition system trained by our dataset achieved higher accuracy than the past benchmark dataset. The results show that the training data should contain detailed actions for a real application. In the third experiment, we tried to find the motion data type that stably recognizes action regardless of data variation. In a real application, the action data are changed by people. Thus, we introduced variations in the action data using the cross-subject protocol and moving area setting. We trained the recognition system using each position and angle data. In addition, we compared the accuracy of each system. We found that the angle format results in better accuracy because the angle data are beneficial for converting the action variation into a consistent pattern.