As automated vehicles evolve, seating designs must accommodate a wider range of postures, particularly for non-driving-related activities such as relaxing and sleeping. This study aims to model human back shapes in seated and reclined positions to improve ergonomic seat designs. Human back contour data were collected from 36 participants using a custom measurement device in two setups: a 25° backrest angle and a seat pan angle of 15°, simulating a driving posture, and a 50° backrest angle with the same seat pan angle, representing a reclined posture. Statistical Shape Models (SSMs) were developed to analyze the variability of back contours. The 25° setup exhibited a flatter spinal curve and higher compactness, capturing 79.7 % of the variance with the first principal component (PC1), compared to 74.6 % in the 50° setup. The combined setup balanced these differences, providing a comprehensive model for diverse postures. Overall, PC1, PC2, and PC3 together captured more than 96 % of total contour variance, indicating that variations in back height, neck bending, and lumbar prominence constitute the dominant sources of geometric diversity. These findings offer actionable dimensions for designing ergonomic backrests that support diverse users and postures. Future research should investigate whether implementing these guidelines enhances comfort and should include more diverse populations and a broader range of postures.

Comfort is a pivotal construct in human-centered design, connecting technical functionality with well-being across products, services, and systems. The 2023 International Comfort Congress (ICC2023) brought together researchers and practitioners to explore how new contexts of use, predictive approaches, and user diversity are reshaping this field. Contributions in this special issue address comfort in automated driving and turboprop aviation, where physical, psychological, and environmental factors converge. Advances in predictive comfort science demonstrate how objective measures, physiological sensing, and modeling complement traditional self-reports. At the same time, inclusive design approaches—ranging from XR-based co-creation to analyses of posture and movement variability—highlight the importance of accounting for heterogeneous user needs and comfort trade-offs. Together, these studies illustrate a shift toward more adaptive, accessible, and sustainable systems, underscoring comfort as a multidimensional construct that evolves with technological innovation and societal change.

Professional truck drivers spend prolonged periods seated, often leading to discomfort and fatigue. Conventional seats are typically designed for average body dimensions rather than individual morphology, which limits their ability to provide optimal support. This study investigates whether 3D-printed personalized seat inserts, developed through an integrated digital workflow, can improve pressure distribution and perceived comfort compared with a standard truck seat. Sixteen participants completed the full workflow from body-data acquisition to comfort evaluation in a static truck buck. Unlike existing personalization approaches, the workflow explicitly incorporates occupational context and task-related posture constraints as design inputs, and validates a complete, reproducible end-to-end process combining vacuum cushion molding, 3D scanning, computational modelling, and large-format additive manufacturing. Pressure mapping and subjective comfort ratings were collected for both baseline and personalized conditions. The personalized inserts reduced mean pressure by 39% and peak pressure by 18%, while increasing contact area by 15%. Subjective comfort scores improved significantly across all regions, particularly in the buttock area, with participants describing firmer yet more stable support. Beyond these ergonomic outcomes, the study contributes a context-driven personalization method and demonstrates that geometric adaptation informed by real use conditions yields quantifiable comfort benefits in an occupational transport setting.

Turboprop aircraft should be improved as they are more environmentally friendly aircraft compared to turbojet aircraft but noise and vibration are often too high for passengers. A simple and uncomplicated way to carry out experiments is using a demonstrator. To determine whether the demonstrator represents the reality, it must be validated. In this project, real flights were first conducted in a turboprop aircraft. During two 70-minute flights, 94 subjects answered questions about symptoms, mood or comfort levels related to noise and vibration, among other things. In the next step, investigations will be carried out in the demonstrator under the same conditions as the real flights. Both results will be compared with each other. If the data from the demonstrator corresponds to that of the real flights, the demonstrator is considered to have been successfully validated. The requirement for this is that the demonstrator data lies within the confidence intervals of the results from the real flights. The aim is to validate a full-scale on-ground demonstrator of a regional turboprop aircraft cabin that will be used for multiple tests like subject tests and comfort evaluation, composite materials and structures, systems and energy consumption.

Structural electronics has garnered significant attention in the past decade. However, there remains a lack of a systematic approach in designing and manufacturing sensors that leverage both mechanical and electronic properties of materials for different applications. In this paper, we introduce a method for designing piezoresistive force sensors utilizing structural electronics and 3D printing techniques. Based on the principles of piezoresistive force sensing, we defined the geometric profile of the sensor by simultaneously maximizing strain and ensuring as uniform as possible stress distribution across the geometry. CAD models of the sensors were then formulated based on the optimized profile and fabricated using conductive filaments and the material extrusion 3D printing technique. Subsequently, we evaluated the accuracy, the sensitivity, and part-to-part variations of the sensors during loading and unloading. The influence of environmental temperature and humidity on the sensor's response were also investigated and compensated. Experiment results demonstrated the feasibility of the proposed method and revealed potential application domains, as well as limitations of the sensors.

4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D-shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. Here, we introduce a generic framework able to 1) find and align dynamic features in the 3D-scans captured by each camera using the nonrigid-iterative-closest-farthestpoints algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTMbased-network capable of aligning 3D-scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a highquality 3D-mesh model using a non-rigid registration method. With a newly developed 4D-foot-scanner, we validate the framework and create the first open-access data-set, namely the 4D-feet. It includes 4Dshapes (15 fps) of the right and left feet of 58 participants (116 feet including 5147 3D-frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D-scans.

Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.