This paper provides an overview and appraisal of the International Design Engineering Annual (IDEA) challenge - a virtually hosted design hackathon run with the aim of generating a design research dataset that can provide insights into design activities at virtually hosted hackathons. The resulting dataset consists of 200+ prototypes with over 1300 connections providing insights into the products, processes and people involved in the design process. The paper also provides recommendations for future deployments of virtual hackathons for design research.

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A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

This paper presents an exploratory case study where video-based pose estimation is used to analyse human motion to support data-driven design. It provides two example use cases related to design. Results are compared to ground truth measurements showing high correlation for the estimated pose, with an RMSE of 65.5 mm. The paper exemplifies how design projects can benefit from a simple, flexible, and cost-effective approach to capture human-object interactions. This also entails the possibility of implementing interaction and body capturing in the earliest stages of design, at minimal effort.

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