Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine.

The development of an active retrodirective antenna involving frequency conversion stages is presented. This retrodirective array of printed dipoles and regular commercial components is designed to operate at the GSM1900 band. Experimental measurements of monoestatic and biestatic responses are given, showing very good performance for a small retrodirective array.

Wireless communication has led to an explosive growth of emerging consumer and military applications of radio frequency (RF), microwave and millimeter wave circuits and systems. Future personal (hand-held) and ground communications systems as well as communications satellites necessitate the use of highly integrated RF frontends, featuring small size, low weight, high performance and low cost. Continuing chip scaling has contributed to the extent that off-chip, bulky passive RF components,

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HighlightsA novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.Significant decreases in quality level were observed for different noise levels.