Mobile Device Interaction with Force Sensing

2009 ◽  
pp. 133-150 ◽  
Author(s):  
James Scott ◽  
Lorna M. Brown ◽  
Mike Molloy
Keyword(s):  
Mobile Device ◽  
Force Sensing ◽  
Device Interaction

Communicative modalities for mobile device interaction

2013 ◽  
Vol 71 (10) ◽  
pp. 988-1002 ◽  
Author(s):  
Eli R. Hooten ◽  
Sean T. Hayes ◽  
Julie A. Adams
Keyword(s):  
Mobile Device ◽  
Device Interaction

Gaze Perception and Computation Method in the Environment of Mobile Device Interaction

2019 ◽  
Vol 31 (1) ◽  
pp. 1
Author(s):  
Shiwei Cheng ◽  
Qianjing Wei ◽  
Zhangwei Zhang ◽  
Wenjie Qi ◽  
Honggang Cai
Keyword(s):  
Mobile Device ◽  
Computation Method ◽  
Gaze Perception ◽  
Device Interaction

MagiThings

2013 ◽  
Vol 5 (3) ◽  
pp. 23-41 ◽  
Author(s):  
Hamed Ketabdar ◽  
Amin Haji-Abolhassani ◽  
Mehran Roshandel
Keyword(s):  
Magnetic Field ◽  
User Interface ◽  
Mobile Devices ◽  
Mobile Device ◽  
Data Entry ◽  
Field Pattern ◽  
New Approach ◽  
Wide Range ◽  
Device Interaction ◽  
The Magnetic Field

The theory of around device interaction (ADI) has recently gained a lot of attention in the field of human computer interaction (HCI). As an alternative to the classic data entry methods, such as keypads and touch screens interaction, ADI proposes a touchless user interface that extends beyond the peripheral area of a device. In this paper, the authors propose a new approach for around mobile device interaction based on magnetic field. Our new approach, which we call it “MagiThings”, takes the advantage of digital compass (a magnetometer) embedded in new generation of mobile devices such as Apple’s iPhone 3GS/4G, and Google’s Nexus. The user movements of a properly shaped magnet around the device deform the original magnetic field. The magnet is taken or worn around the fingers. The changes made in the magnetic field pattern around the device constitute a new way of interacting with the device. Thus, the magnetic field encompassing the device plays the role of a communication channel and encodes the hand/finger movement patterns into temporal changes sensed by the compass sensor. The mobile device samples momentary status of the field. The field changes, caused by hand (finger) gesture, is used as a basis for sending interaction commands to the device. The pattern of change is matched against pre-recorded templates or trained models to recognize a gesture. The proposed methodology has been successfully tested for a variety of applications such as interaction with user interface of a mobile device, character (digit) entry, user authentication, gaming, and touchless mobile music synthesis. The experimental results show high accuracy in recognizing simple or complex gestures in a wide range of applications. The proposed method provides a practical and simple framework for touchless interaction with mobile devices relying only on an internally embedded sensor and a magnet.

scholarly journals A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1665
Author(s):  
Juechen Yang ◽  
Jun Kong ◽  
Chunying Zhao
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Mobile Device ◽  
Position System ◽  
Gradient Boosting ◽  
Learning Models ◽  
Large Display ◽  
Cursor Position ◽  
Device Interaction ◽  
Machine Learning Models

The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.

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