Enhancing Human Resilience : monitoring, sensing, and feedback

The development of miniaturized monitoring technology represents the greatest opportunity for advancing Resilience and Mental Health in over a century. All experts of the Resilience- and Mental Health domain are contending with a significant mental health burden, e.g. almost half of all work disability across different branches is now related to psychological, psycho-physiological and psychosocial factors, which increased by 30% between 1998 and now; leveraging technology will be part of a concentrated effort to mitigate this impact. One goal of TNO’s Early Research Program – ‘Human Resilience’ (ERP-HR) is to develop a resilience monitoring approach using state-of-the-art and leveraged technologies to reveal insights into the individual psychological, psychophysiological and social psychological factors that determines resilience. Such a monitoring approach requires insights into the availability and feasibility of wearables in workload contexts, their reliability, sensitivity and sensor accuracy, but also how to manage the data stream, storage and analysability. Furthermore, the program aims to feedback the results of the monitoring approach. As such, the work includes the study how to feedback selected results to the employees, their managers and/or associated researchers. To these aims, the collaborative ERP-HR’ work packages 3 and 4 explored in its initial research year the possibilities and gaps of evolving monitoring and sensing systems. Consequently, the two teams focused on the information available about the state-of-the-art advances in technologies to monitor, sense and stream the current individual status and load, and to explore the possibilities for reflection on the results for employees and organizations using dedicated feedback technologies. This report describes the results of this initial exploration divided into 5 chapters, including a brief introduction of the multi-dimensional prospective model for human resilience (WP1), as this model is used as the basis for our resilience monitoring approach.