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A modular sensor application for measuring athlete performance in skiing sports was developed. Using inertial measurement units (IMUs) and load cells in a modular system, a force orientation measurement system, FOMS, was developed. A functioning prototype capable of measuring ski sports dynamics was created. Data processing using the system, a validation of the prototype in terms of angle measurement IMU accuracy, example data from in-field athlete testing, and visualization by animations are described. The system developed contains four subsystems: a controller, two pole measuring modules, and a terrain-measuring module. The system structure also allows for additional modules, making the system applicable to different sports. The IMUs use orientation-sensing components to measure pole orientations, which are used to calculate decomposed forces relative to the terrain. Data from different modules are synchronized using wireless communication and saved on SD cards with time stamps. A validation experiment was conducted in which the angles from the modules were compared with the Oqus motion capture system from Qualisys. Examples for athlete testing in both cross country and alpine skiing were calculated from the matrix provided by the different modules and are presented in graphs to evaluate the athlete. In addition, the relative pole/terrain coordinates are visualized in 2D and 3D animations for analyzing the movement pattern in connection with the applied forces, opening up a whole new level of sports analysis. [ABSTRACT FROM AUTHOR]

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research. [ABSTRACT FROM AUTHOR]

This paper sheds light on the new possibilities for user research activities facilitated by access to makerspaces. We present four case studies of user research conducted in two university-based makerspaces as examples of makerspace-driven user research. Further, by comparing the cases to three classical user research activities, namely observation, prototyping, and user journey mapping, we highlight the main aspects of this new context of user research. We find that accessibility to makerspaces enables user researchers to build low-fidelity yet high-functionality prototypes for exploring users' preferences and motivations in controlled and repeatable ways. These prototypes fall into the category of experience prototypes, but they have greater functionality than the prototypes previously used in this field. Thus, a user researcher can explore a topic more systematically and in a more hypothesis-driven manner. In summary, this study encourages stakeholders in the early stages of product development to consider a makerspace as a resource for user-related requirement elicitation rather than for only specific product iteration. [ABSTRACT FROM AUTHOR]

For supporting the selection of the setup of a new makerspace in Molde, Norway, a pre-study was conducted on the state-of-the-art of makerspaces in Norway and beyond. Data includes: observations and interviews at 13 makerspaces visits in Norway, Denmark and the US, interviews with 11 future users and 1 questionnaire (N=25) answered by members of 8 international makerspace communities. Besides identifying the state-of-the-art of makerspaces concerning Tools , Workspace design , Target group , Business models , Roles and Activities , User profiles and Stories we determined key parameters to consider when designing and evaluating a new makerspace. These covers: Activity and Usage, Creating a Community Feeling, and finally to what extend the makerspace manage to educate novel users in the literacies of a makerspace. In general, our paper contributes with applicable knowledge on implementation of prototype-driven behavior. [ABSTRACT FROM AUTHOR]

Over the past thirty years, a powerful methodology for innovation has emerged from engineering and design thinkers in Silicon Valley. It integrates human, business and technical factors in problem forming, solving and design: "Design Thinking." This human-centric methodology integrates expertise from design, social sciences, business and engineering. It is best implemented by high performance project teams applying diverse points-of-view simultaneously. It creates a vibrant interaction environment that promotes iterative learning cycles driven by rapid conceptual prototyping. The methodology has proven successful in the creation of innovative products, systems, and services. Through courting ambiguity, we can let invention happen even if we cannot make it happen. We can nurture a corpus of behaviors that increase the probability of finding a path to innovation in the face of uncertainty. Emphasis is placed on balance of the questions we ask, and the decisions made. A suite of application examples and research finding will be used to illustrate the concepts in principal and in action.'' [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an adapted Delphi methodology that is, contrary to the classical Delphi design is not aiming to minimize expert estimation variance, but to maximize the range of expert opinions inputted sequentially into an online system. After discussing the traditional Delphi approach and its dissensus based derivatives, the author opens the case for a dissensus Delphi based explorative research tool with special consideration of the Delphi aim, the expert sample and the Delphi design. The proposed online Delphi process is then presented conceptually. Next, the proposed tool is demonstrated based on a prototype, exploring the barrier factors to the adoption of mobile data services. A discussion on the theoretical design and practical R&D experience of the dissensus based online Delphi approach concludes the paper. [Copyright &y& Elsevier]

This paper maps and describes how prototypes are used to elicit requirements of unknown unknowns in industry. Eight engineering design companies serve as a dataset for a multi-case investigation. By semi-quantitatively analysing 19 prototypes in terms of functionality, timing, stakeholder involvement and requirement elicitation, we present a wide spectrum of prototype utilizations. However, this broad span leads to misunderstandings of what the term ‘prototype’ encompasses, hindering exploitation of its full potential. Hence, we introduce the term ‘prototrial’ that covers functional prototypes utilized in the early stages of the design process, prototypes that effectively elicit unknown unknowns. With this contribution, we encourage introducing mind-sets and behaviours that aim at exploration and learning rather than lean implementation – a prototrial-driven culture. [ABSTRACT FROM AUTHOR]