scholarly journals Design and Development of the CTAR All-Star

10.29007/h37n ◽  
2019 ◽  
Author(s):  
Terri Heglar ◽  
Andrew Penrose ◽  
Austin Yount ◽  
Kristine Galek ◽  
Yantao Shen ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Pressure Sensor ◽  
Mobile Application ◽  
Future Research ◽  
Objective Data ◽  
Chin Tuck ◽  
Time Graph ◽  
Cross Platform ◽  
Over Time

The CTAR All-Star is a system consisting of a rubber ball, a pressure sensor, and a bluetooth transmitter paired with a cross-platform mobile application. The device is used as a rehabilitation tool for people with dysphagia in a similar fashion to the traditional chin tuck against resistance (CTAR) exercise by squeezing a ball between the chin and upper chest. The mobile device monitors and displays the pressure inside the ball on a real-time graph allowing the patient to follow exercise routines set by Speech-Language Pathologists. Additionally, the application stores exercise data that can be used to both monitor the patient's progress over time and provide objective data for future research purposes.

scholarly journals The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Daegyu Choe ◽  
Eunjeong Choi ◽  
Dong Keun Kim
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Mobile Device ◽  
Mobile Application ◽  
Feature Recognition ◽  
Species Recognition ◽  
Color Similarity ◽  
Parrot Species ◽  
The Many ◽  
Google Search

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

scholarly journals Differences In The Effects On Human Focus Of Music Playlists And Personalized Soundscapes, As Measured By Brain Signals

2021 ◽  
Author(s):  
Aia Haruvi ◽  
Ronen Kopito ◽  
Noa Brande-Eilat ◽  
Shai Kalev ◽  
Eitan Kay ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Young Adult ◽  
Physical Properties ◽  
Real Time ◽  
Future Research ◽  
Specific Time ◽  
Time Intervals ◽  
Brain Signals ◽  
Individual Participant ◽  
Over Time

ABSTRACTThe goal of this study was to learn what properties of sound affect human focus the most. Participants (N=62, 18-65y) performed various tasks while listening to either no background sound (silence), popular music playlists for increasing focus (pre-recorded songs), or personalized soundscapes (audio composed in real-time to increase a specific individual’s focus). While performing tasks on a tablet, participants wore headphones and brain signals were recorded using a portable electroencephalography headband. Participants completed four one-hour long sessions, each with different audio content, at home. We successfully generated brain-based models to predict individual participant focus levels over time and used these models to analyze the effects of various audio content during different tasks. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p=0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36y), silence was significantly less effective at producing focus than audio content of any type tested (p=0.001-0.009). Personalized soundscapes enhanced focus the most relative to silence, but professionally crafted playlists of pre-recorded songs also increased focus during specific time intervals, especially for the youngest audience demographic. We also found that focus levels can be predicted from physical properties of sound, enabling human and artificial intelligence composers to test and refine audio to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research includes real-time adjustment of sound for other functional objectives, such as affecting listener enjoyment, calm, or memory.

scholarly journals MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil

2021 ◽  
Vol 9 ◽  
Author(s):  
Aisha Aldosery ◽  
Anwar Musah ◽  
Georgiana Birjovanu ◽  
Giselle Moreno ◽  
Andrei Boscor ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Data Collection ◽  
Real Time ◽  
Mobile Application ◽  
Health Workers ◽  
Critical Role ◽  
Analytical Models ◽  
Environmental Surveillance ◽  
Mosquito Surveillance ◽  
Cross Platform

Mosquito surveillance is a crucial process for understanding the population dynamics of mosquitoes, as well as implementing interventional programs for controlling and preventing the spread of mosquito-borne diseases. Environmental surveillance agents who performing routine entomological surveys at properties in areas where mosquito-borne diseases are endemic play a critical role in vector surveillance by searching and destroying mosquito hotspots as well as collate information on locations with increased infestation. Currently, the process of recording information on paper-based forms is time-consuming and painstaking due to manual effort. The introduction of mobile surveillance applications will therefore improve the process of data collection, timely reporting, and field worker performance. Digital-based surveillance is critical in reporting real-time data; indeed, the real-time capture of data with phones could be used for predictive analytical models to predict mosquito population dynamics, enabling early warning detection of hotspots and thus alerting fieldworker agents into immediate action. This paper describes the development of a cross-platform digital system for improving mosquito surveillance in Brazil. It comprises of two components: a dashboard for managers and a mobile application for health agents. The former enables managers to assign properties to health workers who then survey them for mosquitoes and to monitor the progress of inspection visits in real-time. The latter, which is primarily designed as a data collection tool, enables the environmental surveillance agents to act on their assigned tasks of recording the details of the properties at inspections by filling out digital forms built into the mobile application, as well as details relating to mosquito infestation. The system presented in this paper was co-developed with significant input with environmental agents in two Brazilian cities where it is currently being piloted.

scholarly journals Evaluating the Acceptability and Usability of EASEL: A Mobile Application that Supports Guided Reflection for Experiential Learning Activities

10.28945/3791 ◽  
2017 ◽  
Vol 16 ◽  
pp. 195-214 ◽  
Author(s):  
Jerry C Schnepp ◽  
Christian B Rogers
Keyword(s):  
Experiential Learning ◽  
Real Time ◽  
Interaction Design ◽  
Mobile Application ◽  
Network Connectivity ◽  
Evaluation Study ◽  
Location Based Services ◽  
Needs Analysis ◽  
Learning Activity ◽  
Future Research

Aim/Purpose: To examine the early perceptions (acceptability) and usability of EASEL (Education through Application-Supported Experiential Learning), a mobile platform that delivers reflection prompts and content before, during, and after an experiential learning activity. Background: Experiential learning is an active learning approach in which students learn by doing and by reflecting on the experience. This approach to teaching is often used in disciplines such as humanities, business, and medicine. Reflection before, during, and after an experience allows the student to analyze what they learn and why it is important, which is vital in helping them to understand the relevance of the experience. A just-in-time tool (EASEL) was needed to facilitate this. Methodology: To inform the development of a mobile application that facilitates real-time guided reflection and to determine the relevant feature set, we conducted a needs analysis with both students and faculty members. Data collected during this stage of the evaluation helped guide the creation of a prototype. The user experience of the prototype and interface interactions were evaluated during the usability phase of the evaluation study. Contribution: Both the needs analysis and usability assessment provided justification for continued development of EASEL as well as insight that guides current development. Findings: The interaction design of EASEL is understandable and usable. Both students and teachers value an application that facilitates real-time guided reflection. Recommendations for Practitioners: The use of a system such as EASEL can leverage time and location-based services to support students in field experiences. This technology aligns with evidence that guided reflection provides opportunities for metacognition. Recommendation for Researchers: Iterative prototyping, testing, and refinement can lead to a deliberate and effective app development process. Impact on Society: The EASEL platform leverages inherent functionality of mobile devices, such as GPS and persistent network connectivity, to adapt reflection tasks based on lo-cation or time. Students using EASEL will engage in guided reflection, which leads to metacognition and can help instructors scaffold learning Future Research: We will continue to advance the application through iterative testing and development. When ready, the application will be vetted in larger studies across varied disciplines and contexts.

scholarly journals COVID-19 India Tracker Application using Flutter

2021 ◽  
Vol 7 (01) ◽  
pp. 10-15
Author(s):  
Sanju Kumar Sahu M.L. Sharma and Krishna Chandra Tripathi
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Mobile Application ◽  
Human Life ◽  
Public Awareness ◽  
The Novel ◽  
Human Race ◽  
Cross Platform ◽  
Novel Coronavirus ◽  
Digital Protection

Nowadays, COVID-19 is the biggest impediment for the survival of the human race. Again, as mobile technology is now an important component of human life, hence it is possible to use the power of mobile technology against the treat of COVID-19. Every nation is now trying to deploy an interactive platform for creating public awareness and share the important information related to COVID-19. Keeping all of these in mind, authors have deployed an interactive cross-platform (web/mobile) application INDIA COVID-19 TRACKER for the ease of the users, especially in India. This dashboard is featured with all the real-time attributes about the novel coronavirus disease and its measures and controls. The system purposely aims to maintain the digital protection of the society, create public awareness, and not create any agitation situation among the individuals of the society.

MobileSens

2013 ◽  
Vol 3 (2) ◽  
pp. 47-55 ◽  
Author(s):  
Ang Li ◽  
He Li ◽  
Rui Guo ◽  
Tingshao Zhu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Mobile Device ◽  
User Behavior ◽  
Real Life ◽  
Psychological Research ◽  
Tablet Computer ◽  
Objective Data ◽  
General Packet Radio Service ◽  
Web Surfing

In psychological research, it is difficult to acquire unintrusive, real-time and objective data under real-life non-experimental scenario. This article proposes a system (MobileSens) for automatically recording user behavior on Android mobile device (e.g., turning on device, sending messages, and web surfing), and uploading data to web server through General Packet Radio Service (GPRS) for subsequent analysis. During testing, MobileSens runs smoothly and efficiently on both the smartphone and tablet computer. It indicates that, in the future, this method of data acquisition can improve the performance of conducting psychological research.

scholarly journals Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2561 ◽  
Author(s):  
Ahmed Almassri ◽  
Wan Wan Hasan ◽  
Siti Ahmad ◽  
Suhaidi Shafie ◽  
Chikamune Wada ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Pressure Sensor ◽  
Pressure Sensors ◽  
Ann Model ◽  
Self Calibration ◽  
Proposed Model ◽  
Calibration Algorithm ◽  
Over Time

This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

scholarly journals Augmented Reality in the Presentation of City Monuments: A Case Study of “Bielsko-Biała AR Guide” Mobile Application

2021 ◽  
Vol 20 ◽  
pp. 31-40
Author(s):  
Stanisław Szombara ◽  
Małgorzata Zontek
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Mobile Device ◽  
Mobile Application ◽  
Geospatial Data ◽  
Camera Image ◽  
Historical Objects ◽  
The City ◽  
Modern Technologies

Augmented Reality (AR) is one of the modern technologies used for sharing 3D geospatial data. This article presents possible ways of enriching a mobile application containing information about 50 objects located in the city of Bielsko-Biała with an AR functionality. The application was created in two programs: Android Studio and Unity. The application allows to get to know historical objects of the city, encourages to visit them by adding virtual elements observed in the background of a real-time camera image from a mobile device. The article presents the statistics of the application usage and the results of a survey conducted among a group of testers. Feedback from application testers confirms the validity of using AR technology in the application. ROZSZERZONA RZECZYWISTOŚĆ W PREZENTACJI ZABYTKÓW MIASTA: APLIKACJA „BIELSKO-BIAŁA PRZEWODNIK AR”, STUDIUM PRZYPADKU Rzeczywistość Rozszerzona (Augmented Reality – AR) jest jedną z nowoczesnych technologii wykorzystywanych do udostępniania danych przestrzennych 3D. W artykule przedstawiono możliwe sposoby wzbogacenia aplikacji mobilnej o funkcjonalność AR. Aplikacja zawiera informacje o 50 obiektach zlokalizowanych na terenie miasta Bielska-Białej i została stworzona w dwóch programach: Android Studio oraz Unity. Aplikacja pozwala na poznanie zabytkowych obiektów miasta oraz zachęca do ich zwiedzania poprzez dodanie wirtualnych elementów obserwowanych w czasie rzeczywistym na tle obrazu z kamery urządzenia mobilnego. W artykule przedstawiono statystyki użytkowania aplikacji oraz wyniki ankiety przeprowadzonej wśród grupy testerów. Informacje zwrotne od testerów aplikacji potwierdzają zasadność zastosowania technologii AR w aplikacji.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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