Implementasi Motion dan Gesture Pada Aplikasi Mobile Berbasis iOS Sebagai Wireless Remote

Steven Wijaya(1*), Kristo Radion Purba(2),


(1) Program Studi Teknik Informaika
(2) Program Studi Teknik Informaika
(*) Corresponding Author

Abstract


Motion and gesture in the world of technology is growing so smartphones are used everyday and also has a variety of sensors. These sensors can provide data that can be calculated so that a certain movement is known.

Data from the accelerometer is used to determine the movements performed by the user. Accelerometer is used as input data to be used on the air mouse feature and gesture recognition. It will also implement image recognition to move the mouse cursor to nearest objects.

The results of this study is an application that works for a physical mouse replacement so that the mouse cursor can be driven through the smartphone. The sensor used is the accelerometer on the smartphone. This application will be very helpful for the presenter to control his laptop remotely. The advantages of this application than the wireless mouse is this application does not need a flat base and can run in the air.


Keywords


Accelerometer, motion, gesture recognition, image snapping.

Full Text:

PDF

References


Apple.inc, 2016, Core Bluetooth Reference, https://developer.apple.com/reference/corebluetooth, retrieved 10 February 2017.

Apple.inc, 2016, Core Graphic Reference, https://developer.apple.com/reference/coregraphic, retrieved 22 February 2017.

Apple.inc, 2016, Core Image Reference, https://developer.apple.com/reference/coreimage, retrieved 22 February 2017.

Apple.inc, 2016, Core Motion Reference, https://developer.apple.com/reference/coremotion, retrieved 14 February 2017.

Klingman Marco, 2019, Accelerometer-Based Gesture Recognition with the iPhone, MSc in Cognitive Computing, Goldsmiths University of London.

Larson Brad, 2016, GPUImage2, https://github.com/ BradLarson/GPUImage2/, retrieved 27 April 2017.

Park, Jung-Me, Carl G. Looney dan Hui-Chuan Chen, 2012, Fast Connected Component Labeling Algorithm Using Divide and Conquer Techique. University of Albama, Tuscalona.

Tong Lina, Song Quanjun, Ge Yunjian, 2013, HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer, IEEE Sensors Journal

Xie, Michael, David Pan, 2012, Accelerometer Gesture Recognition, Standfort University.


Refbacks

  • There are currently no refbacks.


Jurnal telah terindeks oleh :