Chris Snider

Understanding dexterity is a critical factor when designing physical and interactive products, shaped by the unique proprioceptive and musculoskeletal traits of users. The extent to which these individual differences manifest during physical product interactions and the methods to effectively quantify them remain largely unexplored. Measurement of subtle characteristics in hand–object interactions could assist researchers and practitioners to better understand how users interact with products, leading the way for more refined, accessible, bespoke or adaptive products tailored to individuals’ dexterity and usage. This paper investigates (1) individual differences within object interactions for single-handed highly dexterous tasks and (2) the feasibility of data-driven measurement of dexterous interaction. A study explores the ability of data-driven techniques to identify individual differences and characterise dexterous interaction, for (i) an unconstrained hand–object interaction scenario and (ii) a constrained hand–tool–object manipulation scenario. Despite a reduction in performance variance during the constrained task, the classification of user actions remained heavily dependent on participant-specific features. Models trained on group data failed to generalise to new users, highlighting the significant inter-participant variability in dexterous strategies, even under constrained conditions. Our results demonstrate that user-specific data capture could aid personalised product development and provide recommendations for implementation in future work.

The emergent use of mixed reality (MR) technology in product development and evaluation provides new opportunities for prototyping across the physical and virtual domain. However, it is unknown how MR prototypes affect users’ cognitive processes during final stage product evaluation. This experiment explores how different prototype representations—physical, virtual, and MR—affect users’ DLPFC brain activity as measured by functional near-infrared spectroscopy (fNIRS), subjective workload, and affective state. N _total =88 participants split in three independent groups received either a physical ( N = 30), virtual ( N = 30), or MR ( N = 28) representation of a power drill, tasked to evaluate its usability and propose a design change. The results demonstrate significantly different oxygenated and deoxygenated haemoglobin concentration changes in users’ dorsolateral prefrontal cortex (DLPFC), depending on representation mode (i.e., physically, virtual, or MR), and task (i.e., evaluation or proposing a design change). While there were some between-group differences in physical demand and subjective performance, other subjective workload and affective state measures did not significantly differ. These results are novel, demonstrating that users’ DLPFC brain activity differ depending on prototype representation mode. This could imply differing cognitive processes, but further research is required. Nevertheless, choosing prototype representation mode is not trivial and should be a careful consideration.

Hybrid prototyping (HP) – the combination of mixed prototyping media within a single product design – has shown potential to substantially reduce process cost and fabrication time. However, previous work has not considered how HP processes and fabrication should be aligned with designer intent or activity needs, how these may change the realised savings or good practice guidance for successful implementation. This work proposes three approaches for HP with Lego and additive manufacturing, targeted towards enabling mixed fidelity for prototype flexibility, parallelisation for rapid fabrication and component reuse to minimise material waste. It then establishes good practice guidance and proposes a HP method that accounts for practical and process constraints, then implemented through an automated hybridisation tool. Approaches are compared through a simulation study and a case study to establish relative benefit. Results show potential time and material savings of 56% and 76%, respectively, depending on the approach chosen, demonstrating the substantial and practical scope for savings that HP provides.

Spatial Computing (SC), the use of technology to blur the boundaries between physical and digital into an efficient, intuitive, high performance set of tools, holds huge promise for engineering design. With dramatic and accelerating industry prominence but little research in the design field, there is a need to generalize and frame SC for design. This paper contributes an operational framework for Spatial Engineering (SE) systems highlighting the roles of physical and digital users, objects, environments, and data, and five capabilities required for implementation. It then identifies value propositions for SE evidenced from review of the design field, including design activities in which value is generated. Finally, it presents research opportunities centered on good practice, system interaction and technology, and balancing overhead with the value that these systems provide.

Mixed reality prototypes are used for applications like design, analysis, and training. They combine high-fidelity overlays on low-fidelity tangible prototypes, giving users physical interactions in virtual environments. Suitable virtual environments are crucial in taking full advantage of these prototypes. However, there is a lack of guidance in the literature on choosing environment reconstruction methods for various applications. The rapid advancements in this area necessitate the characterisation of the reconstruction methods. This paper thus presents a novel knowledge framework for mapping the reconstruction methods with the requirements of MR prototype applications. The aim of the proposed framework is to help designers and engineers make informed decisions. The effectiveness of the framework has been illustrated using five reconstruction methods and testing via four case studies.

How we gather individual data to inform product design is changing. In ergonomics, methodologies are rooted in qualitative approaches, providing a holistic approach but can lack objectivity and precision. In this work, we explore novel quantitative techniques, involving machine vision and muscle sensing, to create personalized data dashboards that enrich qualitative practices in a mixed-method design. We conducted a pilot study (n=10), evaluating participants’ motion in a simple ergonomic task, followed by interviews discussing the dashboards. A thematic analysis showed that all participants agreed the dashboards affirmed their experience. Furthermore, the order of data presentation influenced their language, affecting subjectivity and specificity. This study highlights participants’ roles as stakeholders, underscoring the need for their engagement to achieve meaningful design outcomes.

Mixed Reality enables individuals to visualise and interact with artefacts and environments through a combination of physical and virtual assets. It has received increased interest from the design community as a means to accelerate, enrich and enhance prototyping activities. This article concerns MR’s ability to deceive an individual through the combination of virtual and physical assets and their underlying traits (e.g., mass, size), and a user’s cognitive ability to ‘join the dots’. If properly implemented, MR could save time and resources by reducing the required prototype fidelity and the need to fully realise variants. However, there is a gap in understanding how the traits of physical and virtual assets and cognition combine to form reality. This article presents a study investigated the role mass, virtual and physical model size played on users perception of an MR prototype. The relative impact of these factors was determined by varying these parameters and assessing the user’s perceived change. The key finding from this study was that the virtual model size had a far greater influence on prototype perceived by the user. This suggests that the required physical fidelity of an MR prototype can be lower than the virtual. Furthermore, exploring size design variants can be achieved exclusively through changes to the virtual model.

This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX.