Integrative Sensing Techniques for Building Non-Invasive Body Sensor Networks

The span and accuracy of sensors being developed for Body Sensor Networks (BSNs) are rapidly improving, aiding well-being services, and paving the way for real-time Affective Communication. While traditionally accurate sensing techniques were either highly invasive or rudimentary in their data, the roadmap of developments ahead shall enable a multitude of dependable analysis of patient monitoring. However, given the spectrum of techniques and tools available, researchers in this domain may find it challenging to balance accuracy, invasiveness, responsiveness and other design parameters of health-centric BSNs. In this paper we analyze and contrast different sensor types and sensing inference techniques, along with the response metrics that they are able to deliver. We further elaborate on their association to responses from the nervous system, and how they correlate to different measures of well-being. To aid future research on AI-assisted patient monitoring, we present current benchmarks for assessing BSN data accuracy, and their cross-validation across physiological signals. The goal of this paper is to assist researchers and practitioners in identifying the best subset of sensors and methods that can be used for a BSN for Affective Communication, and enable future immersive health-interactions over the Tactile Internet.