A Real-time Solider State and Performance Monitoring
and Predication via
Multi-modal Physiological and behavioral Signals
Developing on-line solider state and performance measurement
techniques is a critical task for operational safety. The successful
measurement can ensure the safety by identifying changes in human behavior
before human performance deterioration happens. Physiological measures have
been validated for operator state and performance predication. For this research, we propose a wireless
transmission based real-time, portable, and low-cost GLUE-ON system and a
multi-modality bio-signal analysis framework for solider state monitoring and
performance predication. In the proposed GLUE-ON system, a set of low-cost
physiological, behavioral, and environmental sensors (totally seven
different-type sensors) are pasted onto two wearable devices (one is an
arm-wearable belt and the other is a chest strap) to collect solider’s bio-signals and inspect the temperature change of
the operational environment.
With the acquisition of the multi-modality bio-signal analysis, we then
propose a multi-modality bio-signal analysis framework for solider state
inspection and performance predication. The novel framework is developed to
solve the issues on the three aspects: 1) achieve 1:1 mapping between
bio-signal and operator state via distinctive feature extraction and selection;
2) avoid post-hoc analysis and extensive integration resources by using Dynamic
Bayesian Network; 3) Intelligently and automatically select the most efficient
sensors from the available sensors for cost-efficient solider state recognition
and performance predication through active sensor selection.