Search Results (6)

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Risk management involves thinking about what could happen if someone is exposed to a hazard and the likelihood of this event. The vast majority of standards relating to risk management were written in the context of Occupational Health and Safety, as well as Work Health and Safety with the aim of eliminating all risks. In 2023 Standards, Australia published AS ISO 4980:2023: `Benefit-risk assessment for sport and recreational facilities, activities and equipment’. This standard provides a tool for applying risk management in the context of sport and recreation. AS ISO 4980:2023 is a departure from the traditional view of risk management standards in that it presents a view of risk that is not limited to viewing risk only as negative, but rather balances positive benefits with negative risks within the sport and recreation industry. This standard is also different in that it is based specifically on risk to sport and recreational users and not to employees and employers. The benefit that risk provides in the context of sports and recreational activities is placed on an equal footing with the traditional method of assessing risk. There is an expectation in sports and recreational activities that the user is making an implicit trade-off between the benefits and the inherent risks of the activity, including the potential for harm. This implied expectation is that exposure to risk can be positive. Positive risk and learning from making mistakes are not restricted to childhood development where graduated challenges are used but extend throughout our entire life. The goal of this paper is to draw public attention to the new and different perspective of risk on which AS ISO 4980:2023 is based and to provide an understanding of the concept of benefit–risk assessment. The implications of viewing non-workplace risk in a balanced way are discussed. Full article

The straddle monorail tourist transportation system (MTTS) has developed rapidly in recent years, and its structure is an elevated steel structure with a beam–column system, and the design is executed according to the Safety Code for Large Amusement Rides (GB 8408-2018). However, the impact coefficient value of this code is deemed partially unreasonable. Based on this, relying on the Seven Colors Yunnan Happy World project, the dynamic response test is carried out; using the finite element (FEM) software ANSYS (2021) and multibody dynamics (MBD) software SIMPACK (2021x) combined with the monorail unevenness spectra based on the measured monorail, the straddle monorail vehicle–bridge coupling vibration model is established, and mutual verification is carried out with the measured data. A continuous random variable probability model is adopted for the regularity study of impact coefficient samples, combined with probability statistics and the function fitting method to analyse the calculation results and derive the MTTS displacement impact coefficient calculation formula with beam span and driving speed as variables. The results show that the calculated values of the finite element model are in good agreement with the measured data, and the MTTS impact coefficients conform to the extreme value I-type distribution in the probability distribution law, which is inversely proportional to the span and is directly proportional to the traveling speed. Considering a multi-factor MTTS displacement impact coefficient fitting formula of high fit can better reflect the impact coefficient, monorail girder span, and train speed of the interrelationship for related research and design reference, in order to ensure safety and, at the same time, to improve the economy. Full article

This research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of more appealing and sustainable lake parks. To achieve this, two primary methods were employed in this study: the Automated Machine Learning (Auto ML) model and the DeepLab v3+ model. To gather data for the research, 46,444 images were collected from 20 different lake parks from 2019 to 2022. Social media platforms such as Instagram, Flickr, and specific lake park community groups were tapped to source photographs from both professional photographers and the general public. According to the experimental findings, the perceived frequency of natural landscapes was 69.27%, which was higher than that of humanistic landscapes by 30.73%. The perceived intensity was also maintained between 0.09 and 0.25. The perceived frequency of water body landscapes was much greater on a macro-scale, at 73.02%, and the public had various plant preferences throughout the year. Aquatic plant landscapes with low-to-medium green visibility were preferred by the public, according to the landscape share characterization, while amusement rides with medium-to-high openness were preferred. The sky visibility of amusement rides was between 0 and 0.1 and between 0.3 and 0.5, indicating that the public preferred amusement rides with medium-to-high openness. In lake parks, the populace chose settings with less obvious architectural features. When combined, the two models used in this study are useful for identifying and analyzing the intended traits and preferences of lake parks among the general public. They also have theoretical and practical application value for directing the development of lake parks and urban landscapes. Full article

Rapid changes in forces and the resulting changes in acceleration, jerk and higher order derivatives can have undesired consequences beyond the effect of the forces themselves. Jerk can cause injuries in humans and racing animals and induce fatigue cracks in metals and other materials, which may ultimately lead to structure failures. This is a reason that it is used within standards for limits states. Examples of standards which use jerk include amusement rides and lifts. Despite its use in standards and many science and engineering applications, jerk is rarely discussed in university science and engineering textbooks and it remains a relatively unfamiliar concept even in engineering. This paper presents a literature review of the jerk and higher derivatives of displacement, from terminology and historical background to standards, measurements and current applications. Full article

Strokes as amusement park injuries are rare, but have been reported in the literature. Only about 20 cases of cerebrovascular accidents after amusement park visits have been described. We report a healthy 12-year-old boy who presented with facial droop, slurred speech, and inability to use his right arm after riding roller coasters at a local amusement park. He was evaluated and found to have a left middle cerebral artery (MCA) infarction. The patient was treated with anticoagulants and has recovered with no major residual symptoms. It is likely that his neurological symptoms occurred due to the high head accelerations experienced on the roller coasters, which are more detrimental to children due to immature cervical spine development and muscle strength. Early diagnosis of dissection and stroke results in a favorable prognosis. Providers and parents should be aware of the potential risk of roller coasters and act quickly on neurologic changes in children that have recently been to an amusement park. Full article