scholarly journals Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4726-4737
Author(s):  
Ramasamy Kannan ◽  
Sai Harish Pathuri ◽  
Deepak Kumar
Keyword(s):  
Interaction Graph ◽  
Decision Graph ◽  
Device Interaction

Fluid: Flexible User Interface Distribution for Ubiquitous Multi-Device Interaction

2020 ◽  
Vol 23 (4) ◽  
pp. 25-29
Author(s):  
Sangeun Oh ◽  
Ahyeon Kim ◽  
Sunjae Lee ◽  
Kilho Lee ◽  
Dae R. Jeong ◽  
...  
Keyword(s):  
User Interface ◽  
Device Interaction

scholarly journals Device interaction between Cardiac‐Contractility‐Modulation (CCM) und subcutaneus Defibrillator (S‐ICD)

2021 ◽  
Author(s):  
Luca Trolese ◽  
Thomas Faber ◽  
Alexander Gressler ◽  
Johannes Steinfurt ◽  
Judith Stuplich ◽  
...  
Keyword(s):  
Cardiac Contractility ◽  
Cardiac Contractility Modulation ◽  
Device Interaction

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

Highly Liquid Temporal Interaction Graph Embeddings

2021 ◽  
Author(s):  
Huidi Chen ◽  
Yun Xiong ◽  
Yangyong Zhu ◽  
Philip S. Yu
Keyword(s):  
Graph Embeddings ◽  
Interaction Graph ◽  
Temporal Interaction

A simulation tool for mechanical circulatory support device interaction with diseased states

2020 ◽  
Vol 23 (2) ◽  
pp. 124-132
Author(s):  
David J. Horvath ◽  
Dennis W. Horvath ◽  
Jamshid H. Karimov ◽  
Barry D. Kuban ◽  
Takuma Miyamoto ◽  
...  
Keyword(s):  
Mechanical Circulatory Support ◽  
Circulatory Support ◽  
Simulation Tool ◽  
Support Device ◽  
Mechanical Circulatory Support Device ◽  
Device Interaction ◽  
Circulatory Support Device

Learning cognitive embedding using signed knowledge interaction graph

2021 ◽  
pp. 107327
Author(s):  
Yujia Huo ◽  
Derek F. Wong ◽  
Lionel M. Ni ◽  
Lidia S. Chao ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Interaction Graph
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