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Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.

Additive Manufacturing (AM) is projected to have a profound impact on mass customization. In order to benet from this new technology, we need to incorporate AM into design processes. This paper addresses that need by introducing an AM process model for product family design. The proposed model reects the ability of AM to produce customized and complex parts without tooling efforts. By utilizing AM, we eliminate all constraints which arise in conventional product family designs from nding a compromise between commonality and performance. The proposed model starts by identifying design requirements and constraints. In the second step, we use topology optimization to determine an optimal design for each product. Subsequently, Finite Element Analysis (FEA) and cost analysis are performed. The analysis results are interpolated such that they form both a performance and a cost graph. We combine these two graphs in one three dimensional plot, which displays the merits of the individual component realizations. Thus, a fair and competitive evaluation of the components is possible and the most suitable product family design can be selected. The nal designs are fabricated using Fused Deposition Modeling (FDM). A case study is conducted to illustrate how the proposed model facilitates the benets of AM. The results show that the model has the potential to provide aordable customization.

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The article describes the practical use of Unity technology in neurogaming. For this purpose, the article describes Unity technology and brain–computer interface (BCI) technology based on the Emotiv EPOC + NeuroHeadset device. The process of creating the game world and the test results for the use of a device based on the BCI as a control interface for the created game are also presented. The game was created in the Unity graphics engine and the Visual Studio environment in C#. The game presented in the article is called “NeuroBall” due to the player’s object, which is a big red ball. The game will require full focus to make the ball move. The game will aim to improve the concentration and training of the user’s brain in a user-friendly environment. Through neurogaming, it will be possible to exercise and train a healthy brain, as well as diagnose and treat various symptoms of brain disorders. The project was entirely created in the Unity graphics engine in Unity version 2020.1.