College of Arts & Sciences

Saniya Soni

College of Arts & Sciences Psychology

Faculty Mentor: Dr. Danette Morrison Psychology

Examining Learned Helplessness as a Predictor of Depression among Drexel University Undergraduate Students

Research has shown that depression among college students in the United States is on the rise. Academic failure can be a major stressor during college and can often cause students to feel out of control (Misra & Castillo, 2004). Learned helplessness is a perceived loss of control, which may be exhibited in an academic setting for students who have developed loss of motivation and are now faced with challenging tasks. Studies over the years have found a strong positive correlation between learned helplessness and depression (e.g., Garcia, 2017; Susic, 2015). However, less is known about whether learned helplessness is a significant predictor of depression, especially with college students.

Results show that coping competency and parental emotional support significantly correlated negatively to each other and depression, respectively. Also, our regression analyses showed that coping competency and emotional support from parents/guardians significantly and individually predicted the development of depression. These findings suggest that for undergraduate college students, we should examine the factors related to emotional support as well as their sense of self in responding to difficult situations leading to better interventions.

College of Computing & Informatics

Rachelle St. Fleur

College of Computing & Informatics

Computer Science

Faculty Mentor: Dr. Spiros Mancoridis

Computer Science

Alexander Duff Co-Mentor

Machine Learning with IoT Devices

IoT devices, specifically, personal assistants are very prominent in the world we live in today. Many people are unable to go a day without using their personal assistants to carry out daily activities such as catching up on the news or making doctor appointments. This daily dependence put personal information at risk. For example, if someone were to hack into an Amazon Alexa device, all personal information would be exposed and could potentially be misused. IoT devices such as Amazon Alexa is built off the understanding of machine learning. This research explores the use of machine learning in order to understand IoT devices such as Amazon Alexa. This research involves learning and understanding the various topics that fall under machine learning through creating a virtual machine, implementing ubuntu, and understanding sys call sensors.

College of Computing & Informatics

Stephen Hansen

College of Computing & Informatics Computer Science

Faculty Mentor: Dr. William Mongan Computer Science

Signal Detection for a Biomedical Two-Tag RFID System

Current wearable medical devices require constant tethering to an outlet. The Bellyband, being developed by the Drexel Wireless Systems Laboratory, seeks to address this limitation by using powerless and wireless Radio Frequency Identification (RFID) technology. An RFID antenna is sewn into the band, expanding and contracting while the patient breathes, which alters the signal strength that is received.

The changes in signal can then be interpreted as changes in breathing rate. However, RFID is a weak signal, and as a result interference and movement distorts the data. To solve this issue, a two-tag system is used, where a reference tag records noise data and a main tag records noise and respiratory data. The main data tag is then compared to the changing reference distribution window by using various linear algebra techniques. A Python framework automates this process and returns detection and visualization of the differences between the two distributions, where the magnitude of the difference correlates with the rate of breathing. Machine learning techniques estimate the exact times when breathing rate changes. Future modules which find optimal algorithm parameterization will improve the effectiveness of this software.

College of Computing & Informatics

Christopher Lynch

College of Computing & Informatics

Computer Science

Faculty Mentor: Dr. William Mongan

Computer Science

Respiratory Rate Measurement Using Wireless Sensors

Modern respiratory monitoring of infants has the potential to detect life-threatening events in realtime. However, current monitoring devices require adhesive tethered sensors that can be invasive to the patient due to limited body surface area and inhibit mobility. Using Radio Frequency Identification (RFID), it is possible to collect telemetry from a patient using passive wearable sensors powered only by the signals it receives. Currently, one sensor is used on a programmable mannequin to infer respiratory state from changes in power of the signal received. This has indicated feasibility for ambulatory monitoring; to do this, we need to better characterize the RF and mechanical noise to enable classification in motion. In this research, a second, stationary, RFID tag is placed on the mannequin’s shoulder as a baseline reference. This tag is used to collect data about the noise of the environment, unaffected by breathing. Statistical signal processing, combined with filtering of environmental dynamic noise based on the data collected from the stationary tag, is used to estimate the breathing rate of the baby. We have observed improvements in the accuracy breathing rate measurement using these techniques.