Search Results (15)

With the development of electrification and intelligent technologies in vehicles, ride comfort issues represented by motion sickness have become a key constraint on the performance of autonomous driving. The occurrence of motion sickness is influenced by the comprehensive movement of the vehicle in the longitudinal, lateral, and vertical directions, involving ACC, LKA, active suspension, etc. Existing motion sickness control method focuses on optimizing the longitudinal, lateral, and vertical directions separately, or coordinating the optimization control of the longitudinal and lateral directions, while there is relatively little research on the coupling effect and coupled optimization of the longitudinal and vertical directions. This study proposes a coupled framework of ACC and active suspension control system based on MPC. By adding pitch angle changes caused by longitudinal acceleration to the suspension model, a coupled state equation of half-car vertical dynamics and ACC longitudinal dynamics is constructed to achieve integrated optimization of ACC and suspension for motion suppression. The suspension active forces and vehicle acceleration are regulated coordinately to optimize vehicle vertical, longitudinal, and pitch dynamics simultaneously. Simulation experiments show that compared to decoupled control of ACC and suspension, the integrated control framework can be more effective. The research results confirm that the dynamic coordination between the suspension and ACC system can effectively suppress the motion sickness, providing a new idea for solving the comfort conflict in the human vehicle environment coupling system. Full article

Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants’ attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised–Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers’ cumulative Motion Sickness Dose Value (MSDV) across several test scenarios. Full article

Cybersickness remains a significant barrier to the widespread adoption of virtual reality (VR) technology. Traditional methods for predicting cybersickness rely on self-reported questionnaires or physiological signals from specialized sensors, which have their limitations. This study explores the potential of using real-time, easily acquired head-tracking data (HTD) from standard VR headsets as a scalable alternative for estimating cybersickness. Twelve participants engaged in a VR session using an Oculus Quest 2 headset while their HTD were recorded. Kinematic metrics such as linear and angular velocity, acceleration, and jerk were computed from the HTD, including positional and angular parameters. Participants’ cybersickness levels were assessed using the Virtual Reality Sickness Questionnaire. While exploratory data analysis revealed no significant direct correlation between individual kinematic variables and cybersickness scores, machine learning models were employed to identify predictive patterns. Subsequently, four regression models, including Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines, were trained and evaluated using the computed kinematic features to predict the cybersickness score. Among these, the Gradient Boosting model demonstrated superior performance, accurately predicting cybersickness scores with normalized differences less than 3.08% on unseen data. This approach offers a scalable and practical solution for real-time cybersickness prediction in VR applications and compliments other techniques that rely on physiological sensors, hardware, or user profiles. Full article

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety. Full article

Body balancing is a complex task that includes the coordination of muscles, tendons, bones, ears, eyes, and the brain. Imbalance or disequilibrium is the inability to maintain the center of gravity. Perpetuating body balance plays an important role in preventing us from falling or swaying. Biomechanical tests and video analysis can be performed to analyze body imbalance. The musculoskeletal system is one of the fundamental systems by which our balance or equilibrium is sustained and our upright posture is maintained. Electromyogram (EMG) and ground reaction force (GRF) monitoring can be utilized in cases where a rapid response to body imbalance is a necessity. Body balance also depends on visual stimuli that can be either real or virtual. Researchers have used virtual reality (VR) to predict motion sickness and analyze heart rate variability, as well as in rehabilitation. VR can also be used to induce body imbalance in a controlled way. In this research, body imbalance was induced in a controlled way by playing an Oculus game and, simultaneously, EMG and GRF were recorded. Features were extracted from the EMG and were then fed to a machine learning algorithm. Several machine learning algorithms were tested and upon 10-fold cross-validation; a minimum accuracy of 71% and maximum accuracy of 98% were achieved by Gaussian Naïve Bayes and Gradient Boosting classifiers, respectively, in the classification of imbalance and its intensities. This research can be incorporated into various rehabilitative and therapeutic systems. Full article

Social cognition is fundamental in everyday life to understand “others’ behavior”, which is a key feature of social abilities. Previous studies demonstrated the efficacy of a rehabilitative intervention in semi-immersive virtual reality (VR) controlled by whole-body motion to improve the ability of patients with cerebellar disorders to predict others’ intentions (VR-SPIRIT). Patients with severe ataxia that have difficulties at multiple levels of social processing could benefit from this intervention in terms of improving their social prediction skills, but they may have difficulties in controlling VR with whole-body movements. Therefore, we implemented VR-SPIRIT on a wearable, affordable, and easy-to-use technology, such as the Oculus Quest, a head-mounted display. The aim of this work was to evaluate the usability and tolerability of this VR application. We recruited 10 patients (37.7 ± 14.8 years old, seven males) with different types of hereditary ataxia who performed a single VR-SPIRIT session using the Oculus Quest viewer. After the session, patients answered a series of questionnaires to investigate the overall usability of the system and its potential effects in terms of cyber sickness. The preliminary results demonstrated system usability and tolerability. Indeed, only three patients did not complete the session due to different problems (dizziness, nausea, and boredom). In future studies, more patients will be enrolled to assess the effectiveness of the application, paving the way for the implementation of social training that can also be delivered at home. Full article

Available decision-support tools rarely account for the welfare of technicians in maintenance scheduling for offshore wind farms. This creates uncertainties, especially since current operational limits might make a wind farm accessible but the vibrations from transits might be unacceptable to technicians. We explore technician exposure to vibration in transit based on the levels of discomfort and the likelihood of seasickness occurring on crew transfer vessels (CTVs). Vessel motion monitoring systems deployed on CTVs operating in the North Sea and sea-state data are used in a machine learning (ML) process to model the welfare of technicians based on operational limits applied to modelled proxy variables including composite weighted RMS acceleration (aWRMS) and motion sickness incidence (MSI). The model results revealed poor to moderate performance in predicting the proxies based on selected model evaluation criteria, raising the possibility of more data and relevant variables being needed to improve model performance. Therefore, this research presents a framework for an ML approach towards accounting for the wellbeing of technicians in sailing decisions once the highlighted limitations can be addressed. Full article

(1) Background: After motion sickness occurs in the ride process, this can easily cause passengers to have a poor mental state, cold sweats, nausea, and even vomiting symptoms. This study proposes to establish an association model between motion sickness level (MSL) and cerebral blood oxygen signals during a ride. (2) Methods: A riding simulation platform and the functional near-infrared spectroscopy (fNIRS) technology are utilized to monitor the cerebral blood oxygen signals of subjects in a riding simulation experiment. The subjects’ scores on the Fast Motion sickness Scale (FMS) are determined every minute during the experiment as the dependent variable to manifest the change in MSL. The Bayesian ridge regression (BRR) algorithm is applied to construct an assessment model of MSL during riding. The score of the Graybiel scale is adopted to preliminarily verify the effectiveness of the MSL evaluation model. Finally, a real vehicle test is developed, and two driving modes are selected in random road conditions to carry out a control test. (3) Results: The predicted MSL in the comfortable mode is significantly less than the MSL value in the normal mode, which is in line with expectations. (4) Conclusions: Changes in cerebral blood oxygen signals have a huge correlation with MSL. The MSL evaluation model proposed in this study has a guiding significance for the early warning and prevention of motion sickness. Full article

Motion sickness is known under several names in different domains, such as seasickness, carsickness, cybersickness, and simulator sickness. As we will argue, these can all be considered manifestations of one common underlying mechanism. In recent years, it has received renewed interest, largely due to the advent of automated vehicles and developments in virtual reality, in particular using head-mounted displays. Currently, the most widely accepted standard to predict motion sickness is ISO 2631-1 (1997), which is based on studies on seasickness and has limited applicability to these newer domains. Therefore, this paper argues for extending the ISO standard to cover all forms of motion sickness, to incorporate factors affecting motion sickness, and to consider various degrees of severity of motion sickness rather than just emesis. This requires a dedicated standard, separate from other effects of whole-body vibration as described in the current ISO 2631-1. To that end, we first provide a sketch of the historical origins of the ISO 2631-1 standard regarding motion sickness and discuss the evidence for a common mechanism underlying various forms of motion sickness. After discussing some methodological issues concerning the measurement of motion sickness, we outline the main knowledge gaps that require further research. Full article

The utilization of commercially available virtual reality (VR) environments has increased over the last decade. Motion sickness that is commonly reported while using VR devices is still prevalent and reported at a higher than acceptable rate. The virtual reality induced symptoms and effects (VRISE) are considered the largest barrier to widespread usage. Current measurement methods have uniform use across studies but are subjective and are not designed for VR. VRISE and other motion sickness symptom profiles are similar but not exactly the same. Common objective physiological and biomechanical as well as subjective perception measures correlated with VRISE should be used instead. Many physiological biomechanical and subjective changes evoked by VRISE have been identified. There is a great difficulty in claiming that these changes are directly caused by VRISE due to numerous other factors that are known to alter these variables resting states. Several theories exist regarding the causation of VRISE. Among these is the sensory conflict theory resulting from differences in expected and actual sensory input. Reducing these conflicts has been shown to decrease VRISE. User characteristics contributing to VRISE severity have shown inconsistent results. Guidelines of field of view (FOV), resolution, and frame rate have been developed to prevent VRISE. Motion-to-photons latency movement also contributes to these symptoms and effects. Intensity of content is positively correlated to VRISE, as is the speed of navigation and oscillatory displays. Duration of immersion shows greater VRISE, though adaptation has been shown to occur from multiple immersions. The duration of post immersion VRISE is related to user history of motion sickness and speed of onset. Cognitive changes from VRISE include decreased reaction time and eye hand coordination. Methods to lower VRISE have shown some success. Postural control presents a potential objective variable for predicting and monitoring VRISE intensity. Further research is needed to lower the rate of VRISE symptom occurrence as a limitation of use. Full article

Motion sickness is a common central nervous system response, the primary sign of which is vomiting. Its susceptibility varies between individuals. To find predictive factors, we investigated the association of ADRA2A rs1800544 and HTR3B rs3758987 with motion sickness susceptibility and examined their mRNA changes during actual voyages. A total of 315 healthy college students were enrolled for SNP genotyping by the PCR-RFLP method. Blood samples were collected from another 42 subjects during two separate voyages to detect their mRNA expression changes at three time points. The frequency of the rs1800544 GG genotype in the susceptibility group was significantly higher (52.26%), and allele G increased the risk of motion sickness (OR = 1.585, 95% CI = 1.136–2.208). In the logistic regression model, the rs3758987 CC+TC genotype and rs1800544 GG genotype increased the risk of motion sickness-induced vomiting (OR = 2.105, 95% CI = 1.112–3.984; OR = 1.992, 95% CI = 1.114–3.571). The ADRA2A mRNA baseline was lower in the GG carriers and the HTR3B mRNA baseline was lower in the TC/CC carriers before sailing, then increased significantly within 24 h and then decreased after a long-term voyage. People carrying the rs1800544 GG genotype seem more susceptible to motion sickness. In combination with the incidence of vomiting during the actual-voyage experiments, our results indicate the involvement of rs1800544 and rs3758987 in motion sickness-induced vomiting. Full article

The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time. Full article

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error. Full article

Numerous studies on postoperative nausea and vomiting (PONV) have been carried out since the early days of contemporary surgery. The incidence of PONV has been greatly reduced in recent years and new drugs for PONV keep evolving in the market; however, a substantial number of patients are still under the threat of PONV. Female gender, non-smokers, a history of PONV/motion sickness, and postoperative opioid use are four well-recognized risk factors of PONV. Many potential risk factors reported in previous studies were not consistently presented as predictors for PONV. Two questions then arise; are risk factors clinical setting dependent and are risk factors modifiable? We attempted to answer the questions through a comprehensive review of perioperative records of surgical patients from the Trauma Department of our hospital. As nausea is subjective and no standard is applicable for its measurement, postoperative vomiting (POV) was used as an endpoint in this study. To the best of our knowledge, this is the first study to address the POV issue in surgical trauma patients. A total of 855 patients were enrolled in this study after excluding age below 20 years old, total intravenous anesthesia, desflurane anesthesia, or records with missing data. Our results showed that female gender (OR 4.89) is the strongest predicting factor, followed by a less potent predicting factor—more intraoperative opioid consumption (OR 1.07)—which favor more POV. More intraoperative crystalloid supply (OR 0.71) and a higher body weight (OR 0.9) favor less POV. Other potential risk factors did not reach statistical significance in this study as independent risk factors. Our results also showed that when the intraoperative crystalloid infusion rate is greater than 4 mL/kg/h (OR 0.20), it favors a lower rate of POV; when intraoperative opioid consumption is greater than 12 mg morphine equivalents, MME (OR 1.87), it favors a higher rate of POV. We concluded that dominance of any independent risk factor over other risk factors depends on how individual factors interact with the clinical setting. Some risk factors could be modified, and a cut-off value could be derived to facilitate a better plan for POV prevention. Full article

Many studies have attempted to understand which individual differences may be related to the symptoms of discomfort during the virtual experience (simulator sickness) and the generally considered positive sense of being inside the simulated scene (sense of presence). Nevertheless, a very limited number of studies have employed modern consumer-oriented head-mounted displays (HMDs). These systems aim to produce a high the sense of the presence of the user, remove stimuli from the external environment, and provide high definition, photo-realistic, three-dimensional images. Our results showed that motion sickness susceptibility and simulator sickness are related, and neuroticism may be associated and predict simulator sickness. Furthermore, the results showed that people who are more used to playing videogames are less susceptible to simulator sickness; female participants reported more simulator sickness compared to males (but only for nausea-related symptoms). Female participants also experienced a higher sense of presence compared to males. We suggest that published findings on simulator sickness and the sense of presence in virtual reality environments need to be replicated with the use of modern HMDs. Full article