Sensor Artifact and Noise Reduction Algorithms for Cognitive and Physiological Status Monitoring

Measuring and tracking the physiological and cognitive status of Command, Control, and Intelligence (C2I) system operators will provide useful diagnostic Test and Evaluation (T&E) data and reduce the risk of test failures or accidents. For example, a system that can diagnose and detect fatigue or overload can provide off-line data for gauging system effectiveness, or on-line advisories that increase test safety. However, a critical barrier to the operational deployment of such monitoring systems is the excessive loss of data arising from artifacts recorded by the various physiological sensors. These artifacts can be attributed to external interfering sources, additional physiologic signals (e.g. muscle action) or are related to significant subject motion. Typically 30 to 70% of sensor data collected during performance of active T&E tasks are still severely compromised after processing. The overall objective of this project is to develop and test the feasibility of an integrated, intelligent processing system to reduce the effect of artifacts when monitoring the physiological and cognitive states of C2I operators during T&E operations. The system will use advanced sensors and intelligent software to maximize the quality of sensor data, and provide these data in a real-time format suitable for assessing the effectiveness of T&E operations.