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.

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.