Physiological Indicators of Workload in a Remotely Piloted Aircraft Simulation

Toward preventing performance decrements associated with mental overload in remotely piloted aircraft (RPA) operations, the current research investigated the feasibility of using physiological measures to assess cognitive workload. Two RPA operators were interviewed to identify factors that impact workload in target tracking missions. Performance, subjective workload, cortical, cardiac and eye data were collected. One cardiac and several eye measures were sensitive to changes in workload as evidenced by performance and subjective workload data. Potential future applications of this research include closed loop systems that employ advanced augmentation strategies, such as adaptive automation. Thus, by identifying physiological measures well suited for monitoring workload a realistic simulation, this research advances the literature toward real-time workload mitigation in RPA field operations

The Electrooculogram and a New Blink Detection Algorithm

Accurate and efficient real-time cognitive workload assessment has many important applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal, the electrooculogram (EOG), can provide blink rate and blink duration measures. In a recent study, we developed and validated a robust blink detection algorithm based on the vertical EOG (VEOG). This algorithm does not require baseline data and is adaptive in the sense that it works for a wide variety of individuals without any experimenter adjustments. The performance of the algorithm is quantified using truth data based on video recordings. The algorithm produced blink rate and blink duration data for participants in a simulated remotely piloted aircraft experiment. Although this paper focuses on the blink detection algorithm, some results from the study will be included. Specifically, it was found that participants blinked fewer times and with a shorter duration in the more difficult experimental conditions

Saccade Detection Using Polar Coordinates – a New Algorithm

Over the past few decades substantial research has been conducted regarding saccades (rapid eye movements). There are two components of this research. First there is the detection of the saccades, and second how to interpret the saccades features (amplitude, length, and velocity) to inform specific areas of research. This involves both experimental research and clinical applications. The detection of saccades is typically accomplished using two approaches, including cameras and the electrooculogram (EOG). Both of these approaches require algorithms to process the raw data, detect saccades, and calculate the saccade features. The current effort focuses on detecting saccades in the EOG using a new algorithm based on polar coordinates. The details of this algorithm will be presented, as will a calibration procedure and validation of the algorithm’s accuracy. This algorithm was used in a recent study in which operator workload was manipulated. The saccade features produced by the algorithm were analyzed with respect to the workload manipulations. These results will be discussed