The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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Long-term care facilities are currently installing dynamic lighting systems with the aim to improve the well-being and behaviour of residents with dementia. The aim of this study was to investigate the implementation of dynamic lighting systems from the perspective of stakeholders and the performance of the technology. Therefore, a questionnaire survey was conducted with the management and care professionals of six care facilities. Moreover, light measurements were conducted in order to describe the exposure of residents to lighting. The results showed that the main reason for purchasing dynamic lighting systems lied in the assumption that the well-being and day/night rhythmicity of residents could be improved. The majority of care professionals were not aware of the reasons why dynamic lighting systems were installed. Despite positive subjective ratings of the dynamic lighting systems, no data were collected by the organizations to evaluate the effectiveness of the lighting. Although the care professionals stated that they did not see any large positive effects of the dynamic lighting systems on the residents and their own work situation, the majority appreciated the dynamic lighting systems more than the old situation. The light values measured in the care facilities did not exceed the minimum threshold values reported in the literature. Therefore, it seems illogical that the dynamic lighting systems installed in the researched care facilities will have any positive health effects.
Purpose – In the domain of healthcare, both process efficiency and the quality of care can be improved through the use of dedicated pervasive technologies. Among these applications are so-called real-time location systems (RTLS). Such systems are designed to determine and monitor the location of assets and people in real time through the use of wireless sensor networks. Numerous commercially available RTLS are used in hospital settings. The nursing home is a relatively unexplored context for the application of RTLS and offers opportunities and challenges for future applications. The paper aims to discuss these issues. Design/methodology/approach – This paper sets out to provide an overview of general applications and technologies of RTLS. Thereafter, it describes the specific healthcare applications of RTLS, including asset tracking, patient tracking and personnel tracking. These overviews are followed by a forecast of the implementation of RTLS in nursing homes in terms of opportunities and challenges. Findings – By comparing the nursing home to the hospital, the RTLS applications for the nursing home context that are most promising are asset tracking of expensive goods owned by the nursing home in orderto facilitate workflow and maximise financial resources, and asset tracking of personal belongings that may get lost due to dementia. Originality/value – This paper is the first to provide an overview of potential application of RTLS technologies for nursing homes. The paper described a number of potential problem areas that can be addressed by RTLS. Published by Emerald Publishing Limited Original article: https://doi.org/10.1108/JET-11-2017-0046 For this paper Joost van Hoof received the Highly Recommended Award from Emerald Publishing Ltd. in October 2019: https://www.emeraldgrouppublishing.com/authors/literati/awards.htm?year=2019
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