Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites

Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch's low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method's integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses.

The state of the art of Natural Language Processing - a systematic automated review of NLP literature using NLP techniques

Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current "state of the field" and thus, to enter it. This brought the idea of applying state-of-the-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

Semantically Enriched Data Access Policies in eHealth

Internet of Things (IoT) requires novel solutions to facilitate autonomous, though controlled, resource access. Access policies have to facilitate interactions between heterogeneous entities (devices and humans). Here, we focus our attention on access control in eHealth. We propose an approach based on enriching policies, based on well-known and widely-used eXtensible Access Control Markup Language, with semantics. In the paper we describe an implementation of a Policy Information Point integrated with the HL7 Security and Privacy Ontology.