UROP Project
Activity Identification Using Deep Learning
AI, machine learning, sensor technologies, embedded systems, and biomedical applications
Research Mentor: Oscar Chuy,
Department, College, Affiliation: Electrical and Computer Engineering, FAMU-FSU College of Engineering
Contact Email: chuy@eng.famu.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Department, College, Affiliation: Electrical and Computer Engineering, FAMU-FSU College of Engineering
Contact Email: chuy@eng.famu.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Electrical and Computer
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: Yes Remote or In-person: In-person
Approximate Weekly Hours: 5-10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable
Number of Research Assistants: 2
Relevant Majors: Electrical and Computer
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: Yes Remote or In-person: In-person
Approximate Weekly Hours: 5-10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable
Project Description
As the elderly population continues to grow, understanding their daily activities has become increasingly important for designing supportive technologies that promote independence. This research project focuses on recognizing human activities—such as walking, sitting, or reaching—by analyzing data collected from various sensors.Students will work with sensors such as accelerometers and cameras to gather data and apply deep learning techniques to detect and classify different activities.
Through this research experience, undergraduate students will gain hands-on skills in:
• Basic electronics and sensor setup
• Data collection and preprocessing
• Deep learning for activity recognition
This is a great opportunity for students who are interested in AI, machine learning, sensor technologies, embedded systems, and biomedical applications.
Research Tasks: Literature review, data collection, programming (python), data analysis, and deep learning implementation.
Skills that research assistant(s) may need: Basic programming, Interest in AI TensorFlow or PyTorch, Interesest in Sensors and Data Analysis.
Mentoring Philosophy
I believe undergraduate research is a chance for students to grow into independent problem-solvers. My role as a mentor is to guide, not micromanage. I provide the tools, feedback, and support needed, but I expect students to take ownership of their learning and drive their own progress.I encouraged student to
• Take initiative and explore solutions on their own
• Learn from challenges and setbacks
• Ask thoughtful questions and seek out resources
• Communicate clearly and manage their time responsibly