scholarly journals Implementation of a Flexible Bayesian Classifier for the Assessment of Patient’s Activities within a Real-time Personalized Mobile Application

2017 ◽  
Vol 7 (1) ◽  
pp. 1405-1412 ◽  
Author(s):  
V. Miskovic ◽  
D. Babic
Keyword(s):  
Neural Networks ◽  
Decision Trees ◽  
Real Time ◽  
Mobile Devices ◽  
Data Model ◽  
Mobile Application ◽  
Bayesian Classifier ◽  
Accelerometer Sensor

This paper presents an implementation of a mobile application that provides a real-time personalized assessment of patient’s activities by using a Flexible Bayesian Classifier. The personalized assessment is derived from data collected from the 3-axial accelerometer sensor and the counting steps sensor, both widespread among nowadays mobile devices. Despite the fact that online mobile solutions with Bayesian Classifier have been rare and insufficiently precise, we have proven that the accuracy of the proposed system within a defined data model is comparable to the accuracy of decision trees and neural networks.

scholarly journals DEEP LEARNING TECHNOLOGY FOR VIDEOFRAME PROCESSING IN FACE SEGMENTATION ON MOBILE DEVICES

2021 ◽  
Vol 4 (2) ◽  
pp. 185-194
Author(s):  
Victoria M. Ruvinskaya ◽  
Yurii Yu. Timkov
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Mobile Devices ◽  
Processing Time ◽  
High Accuracy ◽  
Visual Effects ◽  
Segmentation Methods ◽  
Face Segmentation ◽  
Frame Processing

The aim of the research is to reduce the frame processing time for face segmentation on videos on mobile devices using deep learning technologies. The paper analyzes the advantages and disadvantages of existing segmentation methods, as well as their applicability to various tasks. The existing real-time realizations of face segmentation in the most popular mobile applications, which provide the functionality for adding visual effects to videos, were compared. As a result, it was determined that the classical segmentation methods do not have a suitable combination of accuracy and speed, and require manual tuning for a particular task, while the neural network-based segmentation methods determine the deep features automatically and have high accuracy with an acceptable speed. The method based on convolutional neural networks is chosen for use because, in addition to the advantages of other methods based on neural networks, it does not require such a significant amount of computing resources during its execution. A review of existing convolutional neural networks for segmentation was held, based on which the DeepLabV3+ network was chosen as having sufficiently high accuracy and being optimized for work on mobile devices. Modifications were made to the structure of the selected network to match the task of two classes segmentation and to speed up the work on devices with low performance. 8-bit quantization was applied to the values processed by the network for further acceleration. The network was adapted to the task of face segmentation by transfer learning performed on a set of face images from the COCO dataset. Based on the modified and additionally trained segmentation model, a mobile app was created to record video with real-time visual effects, which applies segmentation to separately add effects on two zones - the face (color filters, brightness adjustment, animated effects) and the background (blurring, hiding, replacement with another image). The time of frames processing in the application was tested on mobile devices with different technical characteristics. We analyzed the differences in testing results for segmentation using the obtained model and segmentation using the normalized cuts method. The comparison reveals a decrease of frame processing time on the majority of devices with a slight decrease of segmentation accuracy.

Intelligent Chair Sensor

2014 ◽  
Vol 3 (2) ◽  
pp. 65-80 ◽  
Author(s):  
Leonardo Martins ◽  
Rui Lucena ◽  
Rui Almeida ◽  
João Belo ◽  
Cláudia Quaresma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Mobile Application ◽  
Intelligent System ◽  
Conformation Change ◽  
The Neural Network ◽  
First Time ◽  
Best Parameters ◽  
Real Time Classification

In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven standardized postures. Those neural networks were then exported to a mobile application to achieve a real-time classification of the standardized postures. Results showed a real-time classification of 93.4% for eight standardized postures, even for users that experimented for the first-time our intelligent chair. Using the same mobile application they devised and implemented two correction algorithms, acting due to conformation change of the bladders in the chair's seat when a poor seating posture is detected for certain periods of time.

scholarly journals Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno Pombo ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Neural Networks ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Global Positioning System ◽  
Mobile Application ◽  
Daily Living ◽  
Deep Neural Networks ◽  
Gps Receiver ◽  
Processing Data ◽  
Global Positioning

The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.

scholarly journals The Development of Mobile Application to Introduce Historical Monuments in Manado

2018 ◽  
Vol 31 ◽  
pp. 11012 ◽  
Author(s):  
Moshe Markhasi Rupilu ◽  
Suyoto ◽  
Albertus Joko Santoso
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Application ◽  
Location Based Service ◽  
Manual Search ◽  
North Sulawesi ◽  
Historical Monuments ◽  
Personal Insight

Learning the historical value of a monument is important because it preserves cultural and historical values, as well as expanding our personal insight. In Indonesia, particularly in Manado, North Sulawesi, there are many monuments. The monuments are erected for history, religion, culture and past war, however these aren’t written in detail in the monuments. To get information on specific monument, manual search was required, i.e. asking related people or sources. Based on the problem, the development of an application which can utilize LBS (Location Based Service) method and some algorithmic methods specifically designed for mobile devices such as Smartphone, was required so that information on every monument in Manado can be displayed in detail using GPS coordinate. The application was developed by KNN method with K-means algorithm and collaborative filtering to recommend monument information to tourist. Tourists will get recommended options filtered by distance. Then, this method was also used to look for the closest monument from user. KNN algorithm determines the closest location by making comparisons according to calculation of longitude and latitude of several monuments tourist wants to visit. With this application, tourists who want to know and find information on monuments in Manado can do them easily and quickly because monument information is recommended directly to user without having to make selection. Moreover, tourist can see recommended monument information and search several monuments in Manado in real time.

scholarly journals Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

2020 ◽  
Vol 16 (2) ◽  
pp. 158-166
Author(s):  
Rishiikeshwer B. S. ◽  
T. Aswin Shriram ◽  
J. Sanjay Raju ◽  
M. Hari ◽  
B. Santhi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Mobile Application ◽  
Disease Detection ◽  
Data Set ◽  
Convolution Neural Networks ◽  
Leaf Disease

scholarly journals APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM

2020 ◽  
Vol 4 (8) ◽  
pp. 97-112
Author(s):  
Danylo Svatiuk ◽  
Oksana Svatiuk ◽  
Oleksandr Belei
Keyword(s):  
Operating System ◽  
Neural Networks ◽  
Real Time ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Video Stream ◽  
License Plate ◽  
License Plate Recognition ◽  
Android Operating System

The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

Implementation of an Inventory Control System for a Computer Center Using QR Codes on Android Mobile Devices

2017 ◽  
Vol 7 (11) ◽  
pp. 50
Author(s):  
Gustavo Poot Tah ◽  
Erika Llanes Castro ◽  
José Luis López Martínez ◽  
Victor Chi Pech
Keyword(s):  
Control System ◽  
Inventory Control ◽  
Mobile Devices ◽  
Mobile Application ◽  
Free Software ◽  
Qr Codes ◽  
Design And Development ◽  
Computer Center

This paper presents the design and development of a mobile application that uses QR codes for the inventory control of a computer center. This application was developed to support the administration of the computer center of the Multidisciplinary Unit Tizimin, with the aim to reduce costs and time of searching for articles when making an inventory, by leveraging the capabilities of smartphones and tablets. The implementation of the system was carried out using free software.

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