My name is Jake, and it looks like you've found your way to my website. Chances are, you'll probably find me writing code, making music, or building/fixing something.
This site serves as a big, interactive "about me" page. Take a look around!
Where have I worked? I've had a couple of different jobs over the past few years. Check them out by clicking on the icons below!
I'm currently an undergraduate student at Texas A&M University, and I graduated a few years ago from Martin High School.
B.S. in Computer Science
Minor in Business
Recognizing Seatbelt-Fastening Activity Using Wearable Sensor Technology
Authors: Jake Leland and Ellen Stanfill
Advisor: Dr. Tracy Hammond
Sketch Recognition Laboratory
Department of Computer Science
Texas A&M University
Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algo- rithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using labo- ratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use.
Leland, Jake. Recognizing Seatbelt-Fastening Activity Using Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. 43 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Coauthored with Ellen Stanfill.
Aggie Scheduler is a web application that Texas A&M students can utilize to help plan their schedules for upcoming semesters. Aggie Scheduler pulls data from A&M's online course listings and enables students to preview a graphical layout of their schedule. To aid students in selecting the courses that will be best for them, Aggie Scheduler also displays data such as the grade distribution history of each professor. Thousands of students have used Aggie Scheduler to help pick their classes and prepare for their registration.
This project began when a company called MyEdu (now owned by Blackboard) stopped supporting Texas A&M. MyEdu was a website that provided college students with a graphical tool for course selection and schedule planning. Being an avid MyEdu user myself, the shutdown was a huge bummer. However, something made me want to try and build it myself. I don't think I could've anticipated the amount of work that I would put into the project.Breakdown:
Yes, this is the website you're currently on. I've updated (and overhauled) it a couple of times over the past couple of years, but the idea has stayed the same. This website serves both as an online resume and as a vehicle by which I can stay current with web development practices.Breakdown:
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