This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection.

Navigation in a 3D immersive virtual environment is known to be prone to visually induced motion sickness (VIMS). Several psychophysiological and behavioral methods have been used to measure the level of sickness of a user, among which is postural instability. This study investigates all the features that can be extracted from the body postural sway: area of the projection of the center of gravity (mainly considered in past studies) and its shape and the frequency components of the signal’s spectrum, in order to estimate and predict the occurrence of sickness in a typical virtual reality (VR) application. After modeling and simulation of the body postural sway, an experiment on 17 subjects identified a relation between the level of sickness and the variation both in the time and frequency domains of the body sway signal. The results support and go further into detail of findings of past studies using postural instability as an efficient indicator of sickness, giving insight to better monitor VIMS in a VR application.

This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research.

This paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of. 89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback.

A motion control method of lifting a heavy object up to a higher position with humanoid robots is developed. The key issue of lifting motion is how to reduce the load on humanoid arms in which low-power actuators are implemented. The use of singular postures of arms is well-known to avoid actuator saturation of the arms. By combining two different kinds of humanoid motions such as accelerating an object upward and sliding the body into under the object, we propose a method that enables to transit one singular posture of arms to another while lifting the object. Simulation results show the effectiveness of the proposed method for reducing the load on the arms. We realize a motion of lifting a heavy object dynamically with the humanoid robot HRP-2 through experiment.