The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements.

Rigorous blood glucose management is vital for individuals with diabetes to prevent states of too low blood glucose (hypoglycemia). While there are continuous glucose monitors available, they are expensive and not available for many patients. Related work suggests a correlation between the blood glucose level and physiological measures, such as heart rate variability. We therefore propose a machine learning model to detect hypoglycemia on basis of data from smartwatch sensors gathered in a proof-of-concept study. In further work, we want to integrate our model in wearables and warn individuals with diabetes of possible hypoglycemia. However, presenting just the detection output alone might be confusing to a patient especially if it is a false positive result. We thus use SHAP (SHapley Additive exPlanations) values for feature attribution and a method for subsequently explaining the model decision in a comprehensible way on smartwatches.

Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project AR-ROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end.In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.

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