Graduation Year
2018
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Sriram Chellappan, Ph.D.
Committee Member
Tansel Yucelen, Ph.D.
Committee Member
Mehran Mozaffari Kermani, Ph.D.
Keywords
Android, CrazyFlie, Crazyradio, Gyroscope, Moto 360, Port-to-port
Abstract
Gesture Recognition is a new and upcoming trend that is being widely used in wearable devices and mobile handheld devices. The sensors like Accelerometer, Gyroscope, Heart rate monitor, Barometer and Ambient Light are mostly being included within the device to detect the static or continuous motion, rotational velocity, heartbeat rate of the user, pressure and light conditions for the device respectively. Implementing algorithms to capture the readings of these sensors and implementing them in a necessary way allows a user to use the wearable devices for a wide variety of applications. One such application is controlling Drone that takes user input to determine their motion. A Drone can accept signals from a combination of computer and a radio dongle and would fly according to the accepted commands. The wearable device can detect the motion of the wearer's hand when moved left, right, up, down etc using the Gyroscope sensor. This information can be used to process and send the signals to the Drone to enable wireless and gesture-based movement control.
Scholar Commons Citation
Dadi, Venkata Sireesha, "Drone Movement Control using Gesture Recognition from Wearable Devices" (2018). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/7492