Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight.

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

Mechanical deconditioning of the heart due to long-term bed rest as observed on seismocardiogram morphology.

During head-down tilt bed rest (HDT) the cardiovascular system is subject to headward fluid shifts. The fluid shift phenomenon is analogous to weightlessness experienced during spaceflight microgravity. The purpose of this study was to investigate the effect of prolonged 60-day bed rest on the mechanical performance of the heart using the morphology of seismocardiography (SCG). Three-lead electrocardiogram (ECG), SCG and blood pressure recordings were collected simultaneously from 20 males in a 60-day HDT study (MEDES, Toulouse, France). The study was divided into two campaigns of ten participants. The first commenced in January, and the second in September. Signals were recorded in the supine position during the baseline data collection (BDC) before bed rest, during 6° HDT bed rest and during recovery (R), post-bed rest. Using SCG and blood pressure at the finger, the following were determined: Pulse Transit Time (PTT); and left-ventricular ejection time (LVET). SCG morphology was analyzed using functional data analysis (FDA). The coefficients of the model were estimated over 20 cycles of SCG recordings of BDC12 and HDT52. SCG fiducial morphology AO (aortic valve opening) and AC (aortic valve closing) amplitudes showed significant decrease between BDC12 and HDT52 (p < 0.03). PTT and LVET were also found to decrease through HDT bed rest (p < 0.01). Furthermore, PTT and LVET magnitude of response to bed rest was found to be different between campaigns (p < 0.001) possibly due to seasonal effects on of the cardiovascular system. Correlations between FDA and cardiac timing intervals PTT and LVET using SCG suggests decreases in mechanical strength of the heart and increased arterial stiffness due to fluid shifts associated with the prolonged bed rest.

Design and evaluation of a dynamic air cushion for pressure ulcers prevention.

Wheelchair users have a higher risk of developing pressure ulcers due to prolonged seated pressure. Pressure ulcers can be painful, may require surgical intervention, and even become life-threatening if infection occurs. To prevent pressure ulcers from forming the patient must either offload themselves or rely on a caregiver to move them allowing pressure redistribution over the seated area. In this work, we designed a dynamic air cushion to relieve pressure on loaded areas using sequences of inflation and deflation of the air cushion cells. The purpose of these sequences is to offload pressure from high-risk areas. To evaluate the effect of the alternating sequences on seated pressure and blood perfusion, we recorded interface pressure, skin blood flow, superficial tissue oxygen saturation, blood concentrations of oxygenated hemoglobin, and deoxygenated hemoglobin from twenty-one healthy volunteers who were asked to sit on the air cushion for static mode recording (3 min) and during the inflation/deflation sequences (up to 22 min). The alternating sequences consisted of ten combined inflation and deflation steps. Results showed that, after applying the alternating sequences, interface pressure reduced significantly (p=0.02) compared to the static mode. Moreover, the coefficient of variation of the seated pressure was higher (p<0.001) during the alternation sequence compared to the static mode. However, interface pressure under the right and left ischial tuberosities increased (p<0.001) during the alternation sequence compared to the static mode. In addition, during the alternating sequences, males had larger dispersion index values of both right and left ischial tuberosities pressure compared to females. Furthermore, the maximum value of oxygen saturation (p=0.04) and skin blood flow (p=0.001) increased during the pressure alternation sequences compared to the static mode. The study findings highlighted the positive effects of the designed dynamic air-cushion to relieve pressure on compressed areas and enhance blood perfusion similar to manual offloading approaches. The outcomes of this study are encouraging to evaluate the performance of the designed air cushion in studies involving wheelchair users.

Using hydrodynamic focusing to predictably alter the diameter of synthetic silk fibers.

Spiders and silkworms provide a model of superior processing for multifunctional and highly versatile high-performance fibers. Mimicking the spider's complex control system for chemical and mechanical gradients has remained an ongoing obstacle for synthetic silk production. In this study, the use of hydrodynamic fluid focusing within a 3D printed biomimetic spinning system to recapitulate the biological spinneret is explored and shown to produce predictable, small diameter fibers. Mirroring in silico fluid flow simulations using a hydrodynamic microfluidic spinning technique, we have developed a model correlating spinning rates, solution viscosity and fiber diameter outputs that will significantly advance the field of synthetic silk fiber production. The use of hydrodynamic focusing to produce controlled output fiber diameter simulates the natural silk spinning process and continues to build upon a 3D printed biomimetic spinning platform.