scholarly journals EyePhone Technology: A Smart Wearable Device

2021 ◽  
Author(s):  
K.G. Kharade ◽  
S.K. Kharade ◽  
S. R. Ghatage ◽  
V.S. Kumbhar ◽  
T. Nandana Ravishankar ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Eye Tracking ◽  
Computer System ◽  
Mobile Devices ◽  
Wearable Sensors ◽  
Human Computer Interface ◽  
Previous Decade ◽  
New Type ◽  
Smart Wearable ◽  
Expanded Form

In the past few decades, wearable sensors and devices have evolved into central technologies that have significantly impacted next-generation healthcare solutions in the previous decade. It is an age of incredibly cut-throat competition, even the youth. This technology is Hand-free, eyephone-operated telephone technology. It measures where the user’s eyes are located on the phone’s display employing a camera attached to the device. The typical work the eye bones assigned to eye tracking, eye blinking, detecting, etc., by way of eye-tracking, users can take care of their email, calendar, phone, etc. The leading technologies in use are a Human-Computer Interface (HCI) and a Human Phone Interface (HPI). It helps those with disabilities greatly. The IT Index is an expanded form of HCI. The use of mobile devices like smartphones and tablets is on the rise, and, to some extent, it could displace the use of desktops and laptops. Human-computer interaction is interested in the interaction between people and the computer system and between software and applications. Our focus is on this revolutionary new type of technology called the eye-phone.

An Approach to Develop Mobile Proxemic Applications

2021 ◽  
Vol 2 (2) ◽  
pp. 166-169
Author(s):  
Paulo Perez ◽  
Philippe Roose ◽  
Yudith Cardinale ◽  
Mark Dalmau ◽  
Dominique Masson ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Wearable Sensors ◽  
Smart Environments ◽  
Design And Implementation ◽  
Huge Variety ◽  
Computer Interaction

Traditional Human-Computer Interaction (HCI) is being overpowered by the widespread diffusion of smart and mobile devices. Currently, smart environments involve daily day activities covered by a huge variety of applications, which demand new HCI approaches. In this context, proxemic interaction, derived from the proxemic theory, becomes an influential approach to implement new kind of Mobile Human-Computer Interaction (MobileHCI) in smart environments. It is based on five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO). However, there is a lack of general and flexible tools and utilities focused on supporting the development of mobile proxemic applications. To respond to this need, we have previously proposed a framework for the design and implementation of proxemic applications for smart environments, whose devices interactions are defined in terms of DILMO dimensions. In this work, we extend this framework by integrating a Domain Specif Language (DSL) to support the designing phase. The framework also provides an API, that allows developers to simplify the process of proxemic information sensing (i.e., detection of DILMO dimensions) with mobile phones and wearable sensors. We perform an exhaustive revision of relevant and recent studies and describe in detail all components of our framework.

scholarly journals Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 352
Author(s):  
Ruonan Li ◽  
Xuelian Wei ◽  
Jiahui Xu ◽  
Junhuan Chen ◽  
Bin Li ◽  
...  
Keyword(s):  
Medical Care ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
The Elderly ◽  
The Body ◽  
Assessment System ◽  
Triboelectric Nanogenerator ◽  
Personal Healthcare ◽  
Healthcare Monitoring ◽  
Smart Wearable

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

scholarly journals Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment

2021 ◽  
Vol 11 (13) ◽  
pp. 6197
Author(s):  
Alexandros A. Lavdas ◽  
Nikos A. Salingaros ◽  
Ann Sussman
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Wearable Sensors ◽  
Computer Gaming ◽  
Geometric Patterns ◽  
Relative Design ◽  
Fixation Points ◽  
High Degree ◽  
Artificial Intelligence Application ◽  
Intelligence Application

Eye-tracking technology is a biometric tool that has found many commercial and research applications. The recent advent of affordable wearable sensors has considerably expanded the range of these possibilities to fields such as computer gaming, education, entertainment, health, neuromarketing, psychology, etc. The Visual Attention Software by 3M (3M-VAS) is an artificial intelligence application that was formulated using experimental data from eye-tracking. It can be used to predict viewer reactions to images, generating fixation point probability maps and fixation point sequence estimations, thus revealing pre-attentive processing of visual stimuli with a very high degree of accuracy. We have used 3M-VAS software in an innovative implementation to analyze images of different buildings, either in their original state or photographically manipulated, as well as various geometric patterns. The software not only reveals non-obvious fixation points, but also overall relative design coherence, a key element of Christopher Alexander’s theory of geometrical order. A more evenly distributed field of attention seen in some structures contrasts with other buildings being ignored, those showing instead unconnected points of splintered attention. Our findings are non-intuitive and surprising. We link these results to both Alexander’s theory and Neuroscience, identify potential pitfalls in the software’s use, and also suggest ways to avoid them.

scholarly journals Diabetes classification application with efficient missing and outliers data handling algorithms

2021 ◽  
Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Wearable Sensors ◽  
Model Performance ◽  
Analysis Model ◽  
Human Errors ◽  
Web Based ◽  
Machine Learning Model ◽  
Complete Dataset ◽  
Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

scholarly journals Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6722
Author(s):  
Bernhard Hollaus ◽  
Sebastian Stabinger ◽  
Andreas Mehrle ◽  
Christian Raschner
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Wearable Sensors ◽  
Professional Sports ◽  
American Football ◽  
Data Logging ◽  
Sensor Platform ◽  
Magnetometer Data ◽  
Audio Data ◽  
New Type

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

scholarly journals Accurate Model-Based Point of Gaze Estimation on Mobile Devices

Vision ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 35 ◽  
Author(s):  
Braiden Brousseau ◽  
Jonathan Rose ◽  
Moshe Eizenman
Keyword(s):  
Eye Tracking ◽  
Mobile Devices ◽  
Head Movements ◽  
Roll Angle ◽  
New Method ◽  
Estimation Methods ◽  
Infrared Light ◽  
Gaze Estimation ◽  
Tracking Systems ◽  
Estimation Models

The most accurate remote Point of Gaze (PoG) estimation methods that allow free head movements use infrared light sources and cameras together with gaze estimation models. Current gaze estimation models were developed for desktop eye-tracking systems and assume that the relative roll between the system and the subjects’ eyes (the ’R-Roll’) is roughly constant during use. This assumption is not true for hand-held mobile-device-based eye-tracking systems. We present an analysis that shows the accuracy of estimating the PoG on screens of hand-held mobile devices depends on the magnitude of the R-Roll angle and the angular offset between the visual and optical axes of the individual viewer. We also describe a new method to determine the PoG which compensates for the effects of R-Roll on the accuracy of the POG. Experimental results on a prototype infrared smartphone show that for an R-Roll angle of 90 ° , the new method achieves accuracy of approximately 1 ° , while a gaze estimation method that assumes that the R-Roll angle remains constant achieves an accuracy of 3.5 ° . The manner in which the experimental PoG estimation errors increase with the increase in the R-Roll angle was consistent with the analysis. The method presented in this paper can improve significantly the performance of eye-tracking systems on hand-held mobile-devices.

Context-Awareness and Mobile Devices

2008 ◽  
pp. 205-217 ◽  
Author(s):  
Anind K. Dey ◽  
Jonna Häkkilä
Keyword(s):  
Human Computer Interaction ◽  
Mobile Computing ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Context Awareness ◽  
Design Guidelines ◽  
Context Aware ◽  
Definition Of ◽  
Mobile Context ◽  
Computer Interaction

Context-awareness is a maturing area within the field of ubiquitous computing. It is particularly relevant to the growing sub-field of mobile computing as a user’s context changes more rapidly when a user is mobile, and interacts with more devices and people in a greater number of locations. In this chapter, we present a definition of context and context-awareness and describe its importance to human-computer interaction and mobile computing. We describe some of the difficulties in building context-aware applications and the solutions that have arisen to address these. Despite these solutions, users have difficulties in using and adopting mobile context-aware applications. We discuss these difficulties and present a set of eight design guidelines that can aid application designers in producing more usable and useful mobile context-aware applications.

Leveraging Pervasive and Ubiquitous Service Computing

2009 ◽  
pp. 262-278
Author(s):  
Zhijun Zhang
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Service Oriented Architecture ◽  
Wireless Networking ◽  
Service Computing ◽  
Service Oriented ◽  
Promising Application ◽  
Networking Technologies ◽  
Computer Interaction

The advancement of technologies to connect people and objects anywhere has provided many opportunities for enterprises. This chapter will review the different wireless networking technologies and mobile devices that have been developed, and discuss how they can help organizations better bridge the gap between their employees or customers and the information they need. The chapter will also discuss the promising application areas and human-computer interaction modes in the pervasive computing world, and propose a service-oriented architecture to better support such applications and interactions.

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