Engineering Commons^™

Hand Pattern Recognition Using Smart Band, Theerth Raj Munusamy

Graduate Theses and Dissertations

The Importance of gesture recognition has widely spread around the world. Many research strategies have been proposed to study and recognize gestures, especially facial and hand gestures. Distinguishing and recognizing hand gestures is vital in hotspot fields such as bionic parts, powered exoskeleton, diagnosing muscle disorders, etc. Recognizing such gesture patterns can also create a stress-free and fancy user interface for mobile phones, gaming consoles and other such devices.

The objective is to design a simple yet efficient wearable hand gesture recognizing system. This thesis also shows that by taking both EMG and accelerometer data into account, can improve the …

Human-Machine Interfacing Via Epidermal Electronic Systems, Michael Flynn, Matthew Nelson, Stefan Sharpley, Drew Simmons

Capstone Design Expo Posters

Surface electromyography (EMG) is rapidly becoming a viable control source for interfacing with machines. By measuring the electric potential generated by the contractions of skeletal muscles, systems can be controlled with a mere flick of the wrist, allowing intuitive and versatile control to the wielder. As sensors and classification algorithms become more sophisticated, EMG control has increasing potential to revolutionize the way we interact with and utilize technology. Prosthetics in particular have benefited the most from these recent advances, with one research team successfully returning ambulation to a leg amputee last year. However, this technology is not yet suitable for …

3d-Printed Hand Controlled By Arm Gestures To Verify The Robustness And Reliability Of A Low Cost Surface Electromyography System, Ma. Erika Manlapaz, Marie Perrot, Gabrielle Villavicencio, Bryan Lao, Rosula Sj Reyes

Electronics, Computer, and Communications Engineering Faculty Publications

The study focuses on the development of a low-cost surface electromyography and 3D-printed hand gesture-recognition system. The complete system captures four (4) channels of EMG data through sEMG amplifier circuits interfaced to an Arduino prototyping board. This data is sent to a workstation wherein the graphical user interface shows the pre-processed signal. The gestures are used as control for the movements of the 3D-printed arm.