RECOGNIZING USER INTERFACE CONTROL GESTURES FROM ACCELERATION DATA USING TIME SERIES TEMPLATES

2010 ◽  
Keyword(s):  
Time Series ◽  
User Interface ◽  
Interface Control ◽  
Acceleration Data

scholarly journals Mata: An Android Eye-Tracking Based User Interface Control Application

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Yaya Heryadi ◽  
Michael James
Keyword(s):  
User Interface ◽  
Eye Tracking ◽  
User Experiences ◽  
Physical Handicap ◽  
Interface Control ◽  
Smartphone Technology ◽  
Intelligent Devices ◽  
Control Application ◽  
And Control ◽  
Games Development

The advent of smartphone technology has provided us with intelligent devices for communication as well as playing game. Unfortunately, applications that exploit available sensors in the smartphone are mostly designed for people with no physical handicap. This paper presents Mata, a game user interface using eye-tracking to operate and control games running on Android smartphone. This system is designed to enhance user experiences and help motoric impaired peoples in using smartphone for playing games. Development and testing of the Mata system has proven the concepts of eye-tracking and eyegazing usage as unimodal input for game user interface.

scholarly journals Approach for Improving User Interface Based on Gesture Recognition

2021 ◽  
Vol 297 ◽  
pp. 01030
Author(s):  
Issam Elmagrouni ◽  
Abdelaziz Ettaoufik ◽  
Siham Aouad ◽  
Abderrahim Maizate
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Research Methods ◽  
Gesture Recognition ◽  
Visual Detection ◽  
Interface Control ◽  
Learning Frameworks

Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

Temporal-Sound based User Interface for Smart Home

2021 ◽  
Author(s):  
Kido Tani ◽  
Nobuyuki Umezu
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Smart Home ◽  
Classification Learning ◽  
Activity Classification ◽  
Smart Watch ◽  
Acceleration Data ◽  
Physical Controls ◽  
The Difference

We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.

XML-Based Customized General-Purpose Interface Research and Implementation

2013 ◽  
Vol 846-847 ◽  
pp. 1877-1880
Author(s):  
Shi Cao ◽  
Yi Zhuang
Keyword(s):  
User Interface ◽  
Software Development ◽  
Research Work ◽  
General Purpose ◽  
Interaction Method ◽  
Management Mechanism ◽  
Interface Management ◽  
Development Method ◽  
Interface Control ◽  
Control Research

Against for a series of problems with the currently existing of bad modifiability and reusability for the traditional user interface development method, first propose a general-purpose interface automatically generated model--GIAGM, including interface configuration, interface customization, interface generating, the interface control and interface management mechanism, further put forward a interface interaction method based on message control. Research work to achieve an XML-based general-purpose interface automatically generated system. The application of the system not only allows the software development easier and faster, but also easy to maintain. Finally, the instance is introduced for the interface automatically generated system has good scalability and customization capabilities, can reduce the complexity of the interface development, improve the efficiency of the development.

scholarly journals NORPERM, the Norwegian Permafrost Database – a TSP NORWAY IPY legacy

2010 ◽  
Vol 2 (2) ◽  
pp. 235-246 ◽  
Author(s):  
H. Juliussen ◽  
H. H. Christiansen ◽  
G. S. Strand ◽  
S. Iversen ◽  
K. Midttømme ◽  
...  
Keyword(s):  
Time Series ◽  
User Interface ◽  
Temperature Data ◽  
Temperature Time Series ◽  
Database Management System ◽  
Ground Temperature ◽  
Primary Data ◽  
Research Project ◽  
Standard Format ◽  
Temperature Time

Abstract. NORPERM, the Norwegian Permafrost Database, was developed at the Geological Survey of Norway during the International Polar Year (IPY) 2007-2009 as the main data legacy of the IPY research project Permafrost Observatory Project: A Contribution to the Thermal State of Permafrost in Norway and Svalbard (TSP NORWAY). Its structural and technical design is described in this paper along with the ground temperature data infrastructure in Norway and Svalbard, focussing on the TSP NORWAY permafrost observatory installations in the North Scandinavian Permafrost Observatory and Nordenskiöld Land Permafrost Observatory, being the primary data providers of NORPERM. Further developments of the database, possibly towards a regional database for the Nordic area, are also discussed. The purpose of NORPERM is to store ground temperature data safely and in a standard format for use in future research. The IPY data policy of open, free, full and timely release of IPY data is followed, and the borehole metadata description follows the Global Terrestrial Network for Permafrost (GTN-P) standard. NORPERM is purely a temperature database, and the data is stored in a relation database management system and made publically available online through a map-based graphical user interface. The datasets include temperature time series from various depths in boreholes and from the air, snow cover, ground-surface or upper ground layer recorded by miniature temperature data-loggers, and temperature profiles with depth in boreholes obtained by occasional manual logging. All the temperature data from the TSP NORWAY research project is included in the database, totalling 32 temperature time series from boreholes, 98 time series of micrometeorological temperature conditions, and 6 temperature depth profiles obtained by manual logging in boreholes. The database content will gradually increase as data from previous and future projects are added. Links to near real-time permafrost temperatures, obtained by GSM data transfer, is also provided through the user interface.

Development of State Estimation Filter Simulator Built on an Integrated GUI Framework

2016 ◽  
Vol 20 (5) ◽  
pp. 721-729
Author(s):  
Masaru Morita ◽  
◽  
Takeshi Nishida
Keyword(s):  
Time Series ◽  
User Interface ◽  
State Estimation ◽  
Graphical User Interface ◽  
Kalman Filters ◽  
Point Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
System Designer ◽  
The Relationship

We have developed a graphical user interface (GUI)-based state estimation filter simulator (called StefAny) that makes it easy to understand and compare the behaviors of filters such as Kalman filters (KFs) and particle filters (PFs). The key feature of StefAny is to show, when a system designer applies a PF, a detailed graph representing the relationship among the distribution and weights of all particles on any arbitrary timeline through simulation. Moreover, the timeline can be specified on another graph showing an estimated time series for each filter. These features enable system designers to easily check the compatibility between a filter and a target distribution, which determines the state estimation accuracy. In this paper, we present the functions of StefAny and demonstrate in detail how StefAny facilitates understanding of the properties of filters via a compatibility check comparison experiment for PFs, point estimation methods, and distributions.

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