Drexel University Events Calendar

American Heart Association (AHA) Classes (2/2/2024 - 12/31/2025)

Fri, 2 Feb 2024 05:00:00 GMT

Start Date:	2/2/2024	All Day
End Date:	12/31/2025

American Heart Association (AHA) Classes - BLS, ACLS, PALS, Plus More!
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CNHP Continuing Education (2/2/2024 - 12/31/2025)

Fri, 2 Feb 2024 13:00:00 GMT

Start Date:	2/2/2024	Start Time:	8:00 AM
End Date:	12/31/2025	End Time:	5:00 PM

CNHP Continuing Education Programs

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A Full Time Job (6/17/2024)

Mon, 17 Jun 2024 14:00:00 GMT

Start Date:	6/17/2024	Start Time:	10:00 AM
End Date:	6/17/2024	End Time:	5:00 PM

Exhibition from June 12 through December 13, 2024. Even from its beginning in 1891, Drexel was a pioneer in promoting education for women. The original Drexel Institute of Art, Science and Industry consisted of eleven programs . One of these departments was the Department of Domestic Economy, which was one of the first of its kind outside of schools that were dedicated solely to the education of women. This exhibition shows the history of Westphal College from the early years of the Home Economics program to its push into art and design in the latter half of the 20th century to the college today. Featuring photographs and letters from Drexel University Archive s and pieces from the Drexel Founding Collection, the exhibition highlights the people, programs and practices that have been pivotal to Westphal’s growth from one of the earliest academic programs for women.

This exhibition was curated by Priya Vaswani, Photography & Art History '24 and designed by Will Schumacher, Graphic Design '24.

An opening reception will be held on June 12th from 5 - 7 PM. RSVP required.

The exhibition is free and open to the public.

Identifying Neurophysiological Biomarkers Derived From fNIRS To Quantify Cognitive Workload (6/17/2024)

Mon, 17 Jun 2024 15:00:00 GMT

Start Date:	6/17/2024	Start Time:	11:00 AM
End Date:	6/17/2024	End Time:	1:00 PM

BIOMED Master's Thesis Defense

Title:
Identifying Neurophysiological Biomarkers Derived From fNIRS To Quantify Cognitive Workload During a UAS Mission

Speaker:
Cooper Molloy, Master's Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Kurtulus Izzetoglu, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Safety critical tasks include missions where errors in execution can lead to negative consequences such as loss of life, financial loss, and environmental damage. Unmanned Aerial Systems (UAS) operators often perform such critical missions, and operator error has been shown to be a primary contributor to UAS mishaps. Cognitive workload, the mental effort required to complete a task, has been implicated as a factor that impairs UAS operator performance and increases mishaps. Thus, quantifying cognitive workload in UAS operators is crucial for performance enhancement and safety by enabling detection and management of cognitive overload and inadequate training. Traditional methods of measuring cognitive workload, such as the NASA-TLX and behavioral performance metrics, feature limitations that reduce the validity of their assessment of true cognitive workload. Neurophysiological measures, particularly functional Near Infrared Spectroscopy (fNIRS), have shown promise as direct, objective, non-intrusive tools to measure cognitive workload. Hence, the goal of this study is to evaluate the use of neurophysiological biomarkers derived from fNIRS measures in quantifying cognitive workload experienced during complex real-world UAS operations.

To assess the utility of fNIRS in quantifying cognitive workload, novice UAS operators (n=35) performed complex search and target find scenarios using a high-fidelity, commercially available UAS training simulator. Low- and high-workload conditions were induced by changing weather conditions. Neurophysiological, performance, and questionnaire data were collected for each scenario. Linear discriminant analysis, support vector machine, logistic regression machine learning classifiers were used to determine the classifier and feature set with the best predictive power. Leave one group out cross validation (LOGOCV) was implemented to generate test accuracies and F1-scores for each classifier. Forward and backward sequential feature selection were performed for both SVM and LR classifiers: all feature selected subsets contained fNIRS biomarkers, and all classifiers performed best with fNIRS biomarkers in the feature set. The LR classifier trained on backward selected features was shown to have the highest predictive power (test accuracy = 0.82 ± 0.00, F1-score = 0.87 ± 0.01). This study demonstrates the utility of neurophysiological measures in predicting cognitive workload.

West Philadelphia Community Justice Dialogue Series (6/17/2024)

Mon, 17 Jun 2024 21:00:00 GMT

Start Date:	6/17/2024	Start Time:	5:00 PM
End Date:	6/17/2024	End Time:	7:00 PM

Building Community Consciousness through courageous conversations about home ownership, food security, affordable childcare, intergenerational wealth, and health and wellness.

Breast Cancer Survivorship Series (6/17/2024)

Mon, 17 Jun 2024 23:00:00 GMT

Start Date:	6/17/2024	Start Time:	7:00 PM
End Date:	6/17/2024

Join us to speak with Fox Chase Center Breast Oncology Fellows.