Self-Defined Gesture Recognition on Keyless Handheld Devices using MEMS 3D Accelerometer

2008 ◽  
Author(s):  
Shiqi Zhang ◽  
Chun Yuan ◽  
Yan Zhang
Keyword(s):  
Gesture Recognition ◽  
Handheld Devices

USER INDEPENDENT GESTURE INTERACTION FOR SMALL HANDHELD DEVICES

2006 ◽  
Vol 20 (04) ◽  
pp. 505-524 ◽  
Author(s):  
SANNA KALLIO ◽  
JUHA KELA ◽  
PANU KORPIPÄÄ ◽  
JANI MÄNTYJÄRVI
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Evaluation Criteria ◽  
Handheld Devices ◽  
Practical Application ◽  
Gesture Interaction ◽  
Low Resource ◽  
Computational Performance ◽  
Gesture Control ◽  
Home Appliance

Accelerometer-based gesture recognition facilitates a complementary interaction modality for controlling mobile devices and home appliances. Using gestures for the task of home appliance control requires use of the same device and gestures by different persons, i.e. user independent gesture recognition. The practical application in small embedded low-resource devices also requires high computational performance. The user independent gesture recognition accuracy was evaluated with a set of eight gestures and seven users, with a total of 1120 gestures in the dataset. Twenty-state continuous HMM yielded an average of 96.9% user independent recognition accuracy, which was cross-validated by leaving one user in turn out of the training set. Continuous and discrete five-state HMM computational performances were compared with a reference test in a PC environment, indicating that discrete HMM is 20% faster. Computational performance of discrete five-state HMM was evaluated in an embedded hardware environment with a 104 MHz ARM-9 processor and Symbian OS. The average recognition time per gesture calculated from 1120 gesture repetitions was 8.3 ms. With this result, the computational performance difference between the compared methods is considered insignificant in terms of practical application. Continuous HMM is hence recommended as a preferred method due to its better suitability for a continuous-valued signal, and better recognition accuracy. The results suggest that, according to both evaluation criteria, HMM is feasible for practical user independent gesture control applications in mobile low-resource embedded environments.

The Evolution of Augmentative Communication and the Importance of Alternate Access

2011 ◽  
Vol 20 (1) ◽  
pp. 34-37 ◽  
Author(s):  
David Chapple
Keyword(s):  
Augmentative And Alternative Communication ◽  
Cell Phone ◽  
Handheld Devices ◽  
Alternative Communication ◽  
Access Methods ◽  
The Past ◽  
Communication Device ◽  
Internet Browser ◽  
A Cell ◽  
Development Of Language

Abstract Over the past 20 years, there have been many advances in the computer industry as well as in augmentative and alternative communication (AAC) devices. Computers are becoming more compact and have multiple purposes, such as the iPhone, which is a cell phone, mp3 player, and an Internet browser. AAC devices also have evolved to become multi-purpose devices; the most sophisticated devices have functionality similar to the iPhone and iPod. Recently, the idea of having the iPhone and iPad as a communication device was initiated with the development of language applications specifically for this format. It might be true that this idea could become the future of AAC devices; however, there are major access issues to overcome before the idea is a reality. This article will chronicle advancements in AAC devices, specifically on access methods, throughout the years, towards the transition to handheld devices. The newest technologies hold much promise with both features and affordability factors being highly attractive. Yet, these technologies must be made to incorporate alternate access if they are to meet their fullest potential as AAC tools.

Customized Gesture Recognition for Educational Games

2016 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Seong Jeong ◽  
HongJun Ju ◽  
Hyo-Rim Choi ◽  
TaeYong Kim
Keyword(s):  
Gesture Recognition ◽  
Educational Games

HUMAN GESTURE RECOGNITION USING BISPECTRUM-BASED WIRELESS SIGNAL PROCESSING

2020 ◽  
Vol 79 (1) ◽  
pp. 47-57
Author(s):  
O. G. Viunytskyi ◽  
A. V. Totsky ◽  
Karen O. Egiazarian
Keyword(s):  
Signal Processing ◽  
Gesture Recognition ◽  
Wireless Signal
Sign in / Sign up

Export Citation Format

Share Document