Bioimpedance sensing to characterization osteoarthritis knee-tissue alterations
Faculty Mentor: Todd Freeborn (Electrical and Computer Engineering)
Osteoarthritis is the most common chronic illness and the leading cause of pain and disability among older adults. Sensors to monitor joints and tissues supports tracking disease progression, identifying events that contribute to pain, and evaluating how people respond to therapy.
Teachers will 1) learn about knee tissue physiology, 2) they will measure knee tissue electrical impedance using lab instruments (Keysight E4990A precision impedance analyzer) and wearable sensors; 3) write code to generate visual figures of tissue impedance data, and 4) will analyze and interpret impedance data to answer a research question (e.g. How does knee tissue impedance change during flexion of the knee joint?).
Exploring Physiological Computing Through Modern Web Technologies
Faculty Mentor: Chris Crawford (Computer Science)
Physiological sensing involves the measurement of bio-electrical signals from the body. These signals can measure activity associated with brain (EEG), heart (ECG/EKG), muscles (EMG),
and sweat glands (GSR). Studies on learning featuring physiological sensors have primarily used physiological data to evaluate students’ attention levels, mental effort, anxiety, cognitive load, programming, and learning experience. Other work has aimed to enhance learners’ experiences through closed-loop systems that adapt the learning environment based on learners’ psychophysiological states.
Teachers will 1) learn about physiological sensors and the different aspects of the body they can measure; 2) learn how to use NeuroBlock, a web-based platform for physiological computing education, to collect, filter, and visualize physiological data, 3) measure EEG signals using open-source electronics, and 4) analyze and interpret EEG data to answer a research question (e.g. How does EEG data change during different learning activities?).
Capacitive sensing for breathing assessments with dielectric composites
Faculty Mentor: Amanda Koh (Chemical and Biological Engineering)
Dysfunctional breathing is a challenge that many face due to illness, surgery, or injury. Designing soft, deformable wearable sensors will enable dysfunctional breathing patients and healthcare providers to quantify muscle patterns that demonstrate improved breath control and volume.
Teachers will 1) learn about capacitive sensor materials, 2) measure the electromechanical response of novel dielectric composites using lab instruments (Keysight E4990A precision impedance analyzer and Instron Universal Mechanical Tester), 3) write MATLAB code to organize and visualize capacitive sensor data, and 4) analyze interpret collected data to answer a research question (e.g. How does the capacitive material impedance change when different amounts of pressure are applied?).
Gait and posture measurements using inertial sensors and model-based filtering
Faculty Mentor: Vishesh Vikas (Mechanical Engineering)
Monitoring of human gait and posture is often performed in controlled environments requiring motion capture systems. These provide the user/patient with limited motion capability in a constrained environment. With the increasing availability of low-cost inertial measurement sensors (IMUs) and significant data processing available with low-power, small size hardware; wearable methods for monitoring gait and posture are increasing in interest but require model-based data filtering for accurate estimation and management of sensor inaccuracies.
Teachers will 1) learn how IMUs can be used to measure motion; 2) collect IMU data using simple microcontrollers during different activities (sitting, standing, walking, climbing stairs), 3) write code to organize and visualize IMU data, 4) analyze and interpret data to answer a research question (e.g. how much does IMU data drift over time during extended periods of sitting?).
Underwater robots for sensing and characterization of ocean environments
Faculty Mentor: Aijun Song (Electrical and Computer Engineering)
The increasing utilization of ocean resources for both commercial and environment activities is increasing the need for under-water sensors, autonomous submerged vehicles, and underwater communications. For underwater communications, robust channel estimation poses a great research challenge in coherent communication, and is critical so that ocean environmental data (e.g. temperature, currents, pollution) can be reliably reported and analyzed.
Teachers will 1) learn about the sensors that underwater robotics use to monitor local water conditions; 2) write code to control SeaMATE ROV underwater robotic hardware (motors for location and depth); 3) measure position, temperature, and depth of robotic hardware in controlled lab conditions, 3) write code to organize and visualize collected underwater data; and 4) analyze and interpret data to answer a research question (e.g. how does the transmission speed of communication impact the reliability and accuracy of transmit data underwater?)