Search Results (47)

This study investigates the perception of postural discomfort experienced by passengers seated in the middle rear seat of a vehicle—an area often overlooked in ergonomic research. A total of 20 participants (12 males and 8 females) were involved in a pilot test using two car models: a City Car (Fiat Panda) and a C-SUV (Renault Arkana). Each participant completed a short on-road ride (~24 min, 11.7 km) in both vehicles. Discomfort was assessed using a 5-point Likert scale, considering both overall and localized body discomfort, as well as other elements/factors, such as those involved in the human–car interaction (e.g., the central tunnel, the headrest, AC vents, and other passengers). The results showed that overall discomfort was significantly higher in the City Car (mean: 3.75 ± 0.72) compared to the SUV (mean: 3.00 ± 1.16). The most affected body regions in the City Car were the arms (mean: 3.95), knees (3.90), and legs/feet (3.55). In the SUV, discomfort was lower across all regions, with the arms (3.15) and knees (3.05) still being notably impacted. Strong correlations were observed between discomfort and several vehicle features: backrest width, headrests, interference with adjacent passengers, and rear air conditioning vents. This study highlights specific ergonomic issues in the middle rear seat and suggests design improvements, including wider backrests and headrests, repositioned air vents, and the inclusion of lateral supports. These findings offer actionable insights for automotive manufacturers to enhance passenger comfort in multi-passenger configurations. Full article

This study presents an advanced simulation-based methodology for evaluating the dynamic vibrational behavior of the operator–vehicle assembly in autonomous electric agricultural tractors. Using the TE-0 electric tractor as the experimental platform, the research is structured into three integrated stages. In the first stage, a seated anthropometric virtual model of the human operator is developed based on experimental data and biomechanical validation. The second stage involves a detailed modal analysis of the TE-0 electric tractor using Altair Sim Solid, with the objective of determining the natural frequencies and vibration modes in the [0–80] Hz range, in compliance with ISO 2631-1. This analysis captures both the structural-induced frequencies—associated with the chassis, wheelbase, and metallic frame—and the operational-induced frequencies, influenced by the velocity and terrain profile. Subsequently, the modal analysis of the “Grammer Cabin Seat” is conducted to assess its dynamic response and identify critical vibration modes, highlighting how the seat behaves under vibrational stimuli from the tractor and terrain. The third stage extends the analysis to the virtual operator model seated on the tractor seat, investigating the biomechanical response of the human body and the operator–seat–vehicle interaction during simulated motion. Simulations were carried out using SolidWorks 2023 and Altair Sim Solid over a frequency range of [0–80] Hz, corresponding to operation on unprocessed soil covered with grass, at a constant forward speed of 7 km/h. The results reveal critical resonance modes and vibration transmission paths that may impact operator health, comfort, and system performance. The research contributes to the development of safer, more ergonomic, and sustainable autonomous agricultural transport systems. By simulating real-world operation scenarios and integrating a rigorously validated experimental protocol—including vibration data acquisition, biomechanical modeling, and multi-stage modal analysis—this study demonstrates the importance of advanced modeling in optimizing system-level performance, minimizing harmful vibrations, and supporting the transition toward resilient and eco-efficient electric tractor platforms in smart agricultural mobility. Full article

Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the ‘information silo’ problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human–machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments. Full article

This study investigates how three technical factors—steering wheel tilt, torque, and cabin vibration frequency—affect driver posture. Heavy-truck drivers often suffer from musculoskeletal disorders (MSDs), mainly due to poor cabin ergonomics and prolonged postures during work. In countries like Ecuador, making major structural changes to cabin design is not feasible. These factors were identified through video analysis and surveys from drivers at two Ecuadorian trucking companies. An experimental system was developed using a simplified cabin to control these variables, while posture and muscle activity were recorded in 16 participants using motion capture, inertial sensors, and electromyography (EMG) on the upper trapezius, middle trapezius, triceps brachii, quadriceps muscle, and gastrocnemius muscle. The test protocol simulated key truck-driving tasks. Data were analyzed using ANOVA (${\displaystyle p<0.05}$), with technical factors and mass index as independent variables, and posture metrics as dependent variables. Results showed that head mass index significantly affected head abduction–adduction (${\displaystyle -}$8.12 to ${\displaystyle -}$2.18°), and spine mass index influenced spine flexion–extension (${\displaystyle -}$0.38 to 6.99°). Among technical factors, steering wheel tilt impacted trunk flexion–extension (${\displaystyle -}$13.56 to 16.99°) and arm rotation (${\displaystyle -}$31.1 to 19.7°). Steering wheel torque affected arm rotation (${\displaystyle -}$30.49 to ${\displaystyle -}$6.77°), while vibration frequency influenced forearm flexion–extension (3.76 to 16.51°). EMG signals showed little variation between muscles, likely due to the protocol’s short duration. These findings offer quantitative support for improving cabin ergonomics in low-resource settings through targeted, cost-effective design changes. Full article

Especially in real-world circumstances with uneven road surfaces and impulsive shocks, nonlinear dynamic effects in vehicle systems can greatly skew biometric data utilized to track passenger and driver physiological states. By creating a thorough multibody human–seat–chassis model, this work tackles the effect of vehicle-induced vibrations on the accuracy and dependability of biometric measures. The model includes external excitation from road-induced inputs, nonlinear damping between structural linkages, and vertical and angular degrees of freedom in the head–neck system. Motion equations are derived using a second-order Lagrangian method; simulations are run using representative values of a typical car and human body segments. Results show that higher vehicle speed generates more vibrational energy input, which especially in the head and torso enhances vertical and angular accelerations. Modal studies, on the other hand, show that while resonant frequencies stay constant, speed causes a considerable rise in amplitude and frequency dispersion. At speeds ≥ 50 km/h, RMS and VDV values exceed ISO 2631 comfort standards in the body and head. The results highlight the need to include vibration-optimized suspension systems and ergonomic design approaches to safeguard sensitive body areas and preserve biometric data integrity. This study helps to increase comfort and safety in both traditional and autonomous car uses. Full article

Advanced technology systems and neuroelectronics for crash risk assessment and anticipation may be a promising field for advancing responsible automated driving on urban roads. In principle, there are prospects of an artificially intelligent (AI)-powered automated vehicle (AV) system that tracks the degree of perceived crash risk (as either low, mid, or high) and perceived safety. As a result, communicating (verbally or nonverbally) this information to the user based on human factor aspects should be reflected. As humans and vehicle automation systems are prone to error, we need to design advanced information and communication technologies that monitor risks and act as a mediator when necessary. One possible approach is towards designing a crash risk classification and management system. This would be through responsible AI that monitors the user’s mental states associated with risk-taking behaviour and communicates this information to the user, in conjunction with the driving environment and AV states. This concept is based on a literature review and industry experts’ perspectives on designing advanced technology systems that support users in preventing crash risk encounters due to long-term effects. Equally, learning strategies for responsible automated driving on urban roads were designed. In a sense, this paper offers the reader a meticulous discussion on conceptualising a safety-inspired ‘ergonomically responsible AI’ concept in the form of an intelligent risk assessment system (iRisk) and an AI-powered Risk information Human–Machine Interface (AI rHMI) as a useful concept for responsible automated driving and safe human–automation interaction. Full article

Vibration can have a significant impact on long-term health, driver comfort, and vehicle performance. With a focus on steering wheel vibrations, this study examines both general and local vibrations that affect the driver. Under real-world conditions, a series of controlled test drives were conducted, with high-precision accelerometers mounted on the driver’s seat and steering wheel recording vibration data. The measurements were conducted in accordance with ISO 5349 and ISO 2631-1, which guaranteed a consistent assessment of vibration exposure. The results suggest that the daily vibration exposure for general vibrations at the driver’s seat is significantly lower than the legal limit, as evidenced by the presence of significant frequencies in the vertical (Z) axis. Nevertheless, steering wheel vibrations may cause pain due to their proximity to the resonance frequencies of the human hand–arm system, which have frequency maxima at approximately 35 Hz and harmonic 70 Hz. Additionally, the vibration intensity was elevated at vehicle velocities between 70 and 80 km/h, suggesting the potential presence of a resonance effect within the suspension or powertrain. The results emphasize the significance of advanced vibration reduction strategies in enhancing driver comfort and safety, including the implementation of a well-designed steering system and enhanced seat absorption. This research offers valuable insights for automotive engineers and ergonomics specialists who are interested in minimizing long-term health risks and vibration-induced fatigue. The aim of this study is to indicate the areas of the drive system fault that have a direct impact on the vibrations of the body structure. The article presents an analysis of the recorded vibration results based on which of the areas of change in the comfort of using the vehicle were selected. Full article

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse. Full article

This research provides an integrated perspective on the application of ergonomics and human factors engineering (HFE) to enhance comfort and efficiency in military transport systems (MTS) and civilian transport systems (CTS), identified as imperative research needs. Comfort has become the definition of vehicle quality in CTS, while in MTS, comfort directly influences crew performance in operational environments, and protecting their health is essential. The study emphasizes the importance of adopting an integrative and scientifically grounded approach that eliminates systematic errors to meet user needs and ensure optimal vehicle performance in diverse operational settings. It further highlights how applying ergonomic principles optimizes human–machine interactions, improving user comfort and safety while providing a basis for future innovations in artificial intelligence and adaptive ergonomics. This study introduces a novel hybrid methodology combining bibliometric analysis and case studies to provide fresh insights into the application of ergonomics and human factors engineering, bridging gaps between civilian and military transport systems. This approach aims to promote efficiency, comfort, and performance in operational environments. The results confirm the research hypotheses, demonstrating that ergonomic principles and human factors engineering (HFE) can significantly enhance the comfort, safety, and performance of vehicle users in both civilian and military contexts. Full article

The Sustainable Development Goals (SDGs) set forth by the United Nations aim to eradicate poverty, protect the environment, and promote global prosperity by 2030. Within this framework, Goal 11 targets explicitly sustainable cities and communities, emphasizing the need for accessible, safe, and sustainable transportation systems for all individuals, including those with disabilities. However, despite these aspirations, individuals with disabilities often face unique challenges and barriers in accessing transportation services. This study delves into the complexities of transportation accessibility for people with disabilities, aiming to understand their perceptions and expectations of service quality regarding reliability, tangibles, cleanliness, safety, comfort, personnel, and stops. In a comprehensive survey involving 302 individuals with disabilities, data were collected considering strata such as visual impairment, hearing impairment, chronic illness, and physical disability. In the study where cluster analysis was applied to examine the common and unique assessments of individuals with disabilities, both demographic characteristics and transportation habits were evaluated to determine the most effective inputs. The optimal results were obtained using disability level, car ownership, access to stops, and frequency of service use, while the inclusion of other sociodemographic variables (such as age and income) negatively affected the quality of the clustering process. By analyzing service quality independently for each cluster, the study unveils potential variations in how people with disabilities perceive and evaluate transportation services. The findings shed light on the distinct evaluation approaches employed by people with disabilities based on their characteristics, highlighting the need for tailored transportation planning and policy-making solutions. For example, in the overall assessment of individuals with disabilities, vehicle ergonomics was not highlighted as an area for improvement, but it emerged as the aspect with the least satisfaction among individuals with higher levels of disability. By addressing these nuances, policymakers and stakeholders can better understand and meet the diverse needs of people with disabilities, contributing to the creation of more inclusive and accessible transportation systems in line with the SDGs. Full article

Human–Machine Interfaces (HMIs) in autonomous driving technology have recently gained significant research interest in public transportation. However, most of the studies are biased towards qualitative methods, while combining quantitative and qualitative approaches has yet to receive commensurate attention in measuring user acceptance of design outcome evaluation. To the best of our knowledge, no standardized test procedure that combines quantitative and qualitative methods has been formed to evaluate and compare the interrelationships between different designs of HMIs and their psychological effects on users. This paper proposes a practical and comprehensive protocol to guide assessments of user acceptance of HMI design solutions. We first defined user acceptance and analyzed the existing evaluation methods. Then, specific ergonomic factors and requirements that the designed output HMI should meet were identified. Based on this, we developed a protocol to evaluate a particular HMI solution from in- and out-of-vehicle perspectives. Our theoretical protocol combines objective and subjective measures to compare users’ behavior when interacting with Autonomous Vehicles (AVs) in a virtual experimental environment, especially in public transportation. Standardized testing procedures provide researchers and interaction designers with a practical framework and offer theoretical support for subsequent studies. Full article

The Auckland Harbour Bridge (AHB) utilises a movable concrete barrier (MCB) to regulate the uneven bidirectional flow of daily traffic. In addition to the risk of human error during regular visual inspections, staff members inspecting the MCB work in diverse weather and light conditions, exerting themselves in ergonomically unhealthy inspection postures with the added weight of protection gear to mitigate risks, e.g., flying debris. To augment visual inspections of an MCB using computer vision technology, this study introduces a hybrid deep learning solution that combines kernel manipulation with custom transfer learning strategies. The video data recordings were captured in diverse light and weather conditions (under the safety supervision of industry experts) involving a high-speed (120 fps) camera system attached to an MCB transfer vehicle. Before identifying a safety hazard, e.g., the unsafe position of a pin connecting two 750 kg concrete segments of the MCB, a multi-stage preprocessing of the spatiotemporal region of interest (ROI) involves a rolling window before identifying the video frames containing diagnostic information. This study utilises the ResNet-50 architecture, enhanced with 3D convolutions, within the STENet framework to capture and analyse spatiotemporal data, facilitating real-time surveillance of the Auckland Harbour Bridge (AHB). Considering the sparse nature of safety anomalies, the initial peer-reviewed binary classification results (82.6%) for safe and unsafe (intervention-required) scenarios were improved to 93.6% by incorporating synthetic data, expert feedback, and retraining the model. This adaptation allowed for the optimised detection of false positives and false negatives. In the future, we aim to extend anomaly detection methods to various infrastructure inspections, enhancing urban resilience, transport efficiency and safety. Full article

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. Full article

The design of the space hatch door mechanisms is crucial in the aerospace field, impacting not only durability and reliability but also the life safety of astronauts during space missions. This review extensively researches vehicle doors and hatches in civil and military systems across various environments, including land, sea, deep sea, aviation, aerospace, and extreme conditions. Specially, it focuses on the structural design of hatches and related mechanisms in civil aviation and military aerospace environments, such as opening and closing mechanisms, release mechanisms, locking mechanisms, sealing mechanisms, and the ergonomic design of door structures. The review highlights the integration of bionic design principles with hatch mechanisms to explore future solutions. By systematically examining these aeras, this review addresses the lack of comprehensive studies in previous reviews, which often overlook the interconnectivity and applicability of hatch mechanisms across different fields. The absence of such holistic reviews has led to fragmented knowledge and missed opportunities for cross-industry innovation. This review aims to fill these gaps by providing a wide range of design solutions and offering insights that can enhance the development of more reliable, efficient, and safe hatch mechanisms in aerospace and other high-stakes environments. Full article

Agricultural tractors are an essential agricultural power source. Therefore, the scientific literature tests have described agricultural tractors’ evolution over time and determined future trends. This paper uses bibliometric tools to assess the agricultural evolution of tractor testing from 1969 to 2022 to ascertain the publication’s scientific perspective on operational, ergonomic, and energy performance. We searched for relevant research in the Scopus and Web of Science (WOS) databases. The data were processed in RStudio software version 4.4.1, and we used elaborated bibliometric maps to research evolution, major journals, studies, countries, and keywords. The first research mainly concerned the development of new wheelsets, more efficient engines, and fuel consumption prediction models. After the 2000s, environmental protocols contributed to increasing publications on biofuels and renewable energies. Recently, an intense process of robotization in autonomous vehicles has improved to allow the replacement of combustion engines. Ergonomics and safety have been less recurrent topics in recent years, indicating a stable level in the actual research. New machine control models involving artificial intelligence are currently applied to obtain test results without using the machine in the field. These virtual models reduce costs and optimize resources. The most common terms were “tractor” and “agricultural machinery”. The terms “Electric tractor”, “agricultural robots”, and “Matlab” indicate solid trends for future research. Full article