scholarly journals Actigraphy Signal Analysis System for Sleep and Wake Studies

2021 ◽  
Author(s):  
Yashodhan Athavale
Keyword(s):  
Data Compression ◽  
Activity Recognition ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Research Work ◽  
Movement Direction ◽  
Smart Devices ◽  
Physiological Data ◽  
The Internet ◽  
Embedded Sensing

The Internet of Things (IoT) is a trending model in the wake of recent advancements in ubiquitous sensors and smart devices, and is rapidly being deployed in communications, infrastructure, transportation and healthcare services. The Internet of Medical Things (IoMT) is a subset of the IoT and provides a layered architecture for connecting individuals with mobile devices and wearables, such that their vital physiological data can be captured and analyzed non-invasively using smart sensors embedded within these devices. Currently available wearables have embedded sensing modules for measuring movement, direction, light and pressure. Actigraphs are one such type of wearables which exclusively employ the use of accelerometers for capturing human movement-based vibration data. The main intention of this research work is the analysis of unstructured, non-stationary actigraphy signals. This study aims to address three key objectives: (i) enabling compression and denoising of actigraphy data during acquisition; (ii) extracting regions of interest from the actigraphy data, and; (iii) deriving actigraphy specific features for improving the activity classification accuracy. These have been achieved through three key contributions, namely: (A) a signal encoding framework for data compression and denoising, (B) two novel adaptive segmentation schemes which help in extracting specific movement information from actigraphy data, and (C) two key actigraphy specific features, which quantify limb movements, and hence provide a better classification accuracy using machine learning algorithms. The outcome of this research work is a device-independent actigraphy analysis application for estimating the severity of neuromuscular diseases, identifying types of daily activity, and classify joint degeneration severity, which has been tested on five different actigraphy datasets from wake and sleep states. Compared to traditional signal processing methods, the proposed algorithms ensured a 20-90% increase in SNR (signal-to-noise ratio), 50-80% reduction in bit rate, 50-90% data compression, and an increase in activity recognition accuracy by over 5-20%. Results from this systematic investigation indicate that data analysis conducted at the acquisition source, optimizes signal denoising, memory and power usage, and activity recognition, thereby promoting an edge computing approach to physiological signal analysis using wearables in a low resource environment.

scholarly journals Actigraphy Signal Analysis System for Sleep and Wake Studies

2021 ◽  
Author(s):  
Yashodhan Athavale
Keyword(s):  
Data Compression ◽  
Activity Recognition ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Research Work ◽  
Movement Direction ◽  
Smart Devices ◽  
Physiological Data ◽  
The Internet ◽  
Embedded Sensing

The Internet of Things (IoT) is a trending model in the wake of recent advancements in ubiquitous sensors and smart devices, and is rapidly being deployed in communications, infrastructure, transportation and healthcare services. The Internet of Medical Things (IoMT) is a subset of the IoT and provides a layered architecture for connecting individuals with mobile devices and wearables, such that their vital physiological data can be captured and analyzed non-invasively using smart sensors embedded within these devices. Currently available wearables have embedded sensing modules for measuring movement, direction, light and pressure. Actigraphs are one such type of wearables which exclusively employ the use of accelerometers for capturing human movement-based vibration data. The main intention of this research work is the analysis of unstructured, non-stationary actigraphy signals. This study aims to address three key objectives: (i) enabling compression and denoising of actigraphy data during acquisition; (ii) extracting regions of interest from the actigraphy data, and; (iii) deriving actigraphy specific features for improving the activity classification accuracy. These have been achieved through three key contributions, namely: (A) a signal encoding framework for data compression and denoising, (B) two novel adaptive segmentation schemes which help in extracting specific movement information from actigraphy data, and (C) two key actigraphy specific features, which quantify limb movements, and hence provide a better classification accuracy using machine learning algorithms. The outcome of this research work is a device-independent actigraphy analysis application for estimating the severity of neuromuscular diseases, identifying types of daily activity, and classify joint degeneration severity, which has been tested on five different actigraphy datasets from wake and sleep states. Compared to traditional signal processing methods, the proposed algorithms ensured a 20-90% increase in SNR (signal-to-noise ratio), 50-80% reduction in bit rate, 50-90% data compression, and an increase in activity recognition accuracy by over 5-20%. Results from this systematic investigation indicate that data analysis conducted at the acquisition source, optimizes signal denoising, memory and power usage, and activity recognition, thereby promoting an edge computing approach to physiological signal analysis using wearables in a low resource environment.

scholarly journals A Smart Health Care Applications and Benefits using IoT

2019 ◽  
Vol 8 (3) ◽  
pp. 7120-7123
Keyword(s):  
Smart Devices ◽  
Physiological Data ◽  
The Internet ◽  
Healthcare Applications ◽  
Data Interoperability ◽  
Vast Number ◽  
Smart Object ◽  
Fast Development ◽  
Health Care Applications ◽  
Iot Devices

Internet of Things (IoT) is the developing paradigm, where a vast number of smart object and smart devices associated with the internet for communication. The fast development of the IoT technology makes it feasible for connecting different smart items collectively through the Internet and giving higher and more data interoperability strategies for significant utilize and other application reason. IoT devices are utilized in numerous fields which make the client's daily life all the more simple and agreeable. Patient Physiological data observing is significant in any hospital, this system proposed healthcare applications and benefits dependent on IoT, software, and hardware, this system can indicate temperature heart rate with precision and notice and passion state of a patient..

scholarly journals Impact of PCA Feature Extraction Method used in Malware Detection for Security Enhancement

2020 ◽  
Vol 9 (4) ◽  
pp. 1802-1807
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Denial Of Service ◽  
Research Work ◽  
The Internet ◽  
Security Threats ◽  
Binary Classifier ◽  
Feature Extraction Method ◽  
Security Enhancement

Malware is one of the all told the foremost security threats on the net now a days. Some of the Internet problems like denial of service attacks and spam e-mails have malware threat cause. Computers involved with malware are however networked together for making botnets, and major of threats or attacks are basically launched with the help of these types of malicious and attacker-controlled networks. Downloading files like Executable files like .exe, .bat, .msi etc from sources of untrusted internet probably having an opportunity of getting maliciousness. Further it is seen that these executables are smartly obfuscated with the help of some of the anomalous user for bypassing antivirus stuffs. In this research work , we have proposed an enhanced approach for detecting some of the malicious executables files with the help of analysing the traced Portable Executable (PE) files which are extracted from executable files and use of PCA feature extraction method. The method used in this paper consists of training a supervised binary classifier with the help of these extracted features from the portable executables files from the normal and malicious executables. Considering this approach experimentation has been done on an outsized publicly available dataset and it is seen that over 95% of classification accuracy can be obtained.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

Analysing and Transcribing Electroacoustic Music: the experience of the Portraits polychromes of GRM

2006 ◽  
Vol 11 (2) ◽  
pp. 125-129 ◽  
Author(s):  
ÉVELYNE GAYOU
Keyword(s):  
Research Work ◽  
Electroacoustic Music ◽  
Theoretical Research ◽  
The Internet ◽  
Primary Concern ◽  
Internet Site ◽  
Multimedia Documents ◽  
Multimedia Tools ◽  
Musical Works ◽  
The 1950S

Portraits polychromes are a series of books associated with multimedia documents presented on the Internet site of the GRM since 2001. In releasing this collection, our primary concern was to increase awareness of the electroacoustic repertoire and the reserves in the GRM archives. The GRM, being a pioneering centre of electroacoustics, is fortunate to possess a consistent and significant reserve dating back to the beginning of the 1950s. At present, the catalogue contains around 2,000 works, accompanied with supplementary documents: composer's biographies, reviews, photographs, documentary movies, radio broadcasts, recorded public lectures, theoretical research work, transcriptions and analyses. In addition to the heritage value of the GRM's collection, the enterprise of the Portraits polychromes, with the aid of multimedia tools, aims to advance the progress of research on analysis and the transcription of musical works.

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

scholarly journals Special Focus: The Use of Emerging Technologies on the Internet of Everything

2017 ◽  
Vol 11 (5) ◽  
pp. 4
Author(s):  
Alberto Cardoso ◽  
Maria Teresa Restivo ◽  
Hélia Guerra ◽  
Luís Brito Palma
Keyword(s):  
Emerging Technologies ◽  
International Workshop ◽  
Research Work ◽  
Mobile Technologies ◽  
The Internet ◽  
Special Focus ◽  
Application Development ◽  
Internet Of Everything ◽  
Technology Applications ◽  
Interdisciplinary Concept

The Internet of Everything is an interdisciplinary concept that involves technology, applications and people in a framework where emerging technologies can give a relevant contribute in education and in different application areas, namely in the scope of the interactive mobile technologies. This Special Issue collects a set of contributions to this topic resulting from the works presented in the Experiment@ International Workshop 2016 “The Emerging Technologies on the Internet of Everything” – ETIoE’16, held at University of the Azores (Ponta Delgada, Azores, Portugal). These articles comprise different perspectives in this field from research work and application development to case studies.

scholarly journals Blockchain-Based Trust Management Using Multi-Criteria Decision-Making Model for VANETs

2020 ◽  
Author(s):  
Cong Pu
Keyword(s):  
Decision Making ◽  
Road Safety ◽  
Trust Management ◽  
Detection Rate ◽  
Smart Devices ◽  
Multi Criteria Decision Making ◽  
Trust Value ◽  
Safety Message ◽  
Embedded Sensing ◽  
Decision Making Model

<p>Recent advancements in embedded sensing system, wireless communication technologies, big data, and artificial intelligence have fueled the development of Internet of Vehicles (IoV), where vehicles, road side unit (RSUs), and smart devices seamlessly interact with each other to enable the gathering and sharing of information on vehicles, roads, and their surrounds. As a fundamental component of IoV, vehicular networks (VANETs) are playing a critical role in processing, computing, and sharing travel-related information, which can help vehicles timely be aware of traffic situation and finally improve road safety and travel experience. However, due to the unique characteristics of vehicles, such as high mobility and sparse deployment making neighbor vehicles unacquainted and unknown to each other, VANETs are facing the challenge of evaluating the credibility of road safety messages. In this paper, we propose a blockchain-based trust management system using multi-criteria decision-making model, also referred to as Trust<sup>Block</sup><sub>MCDM</sub>, in VANETs. In the Trust<sup>Block</sup><sub>MCDM</sub>, each vehicle evaluates the credibility of received road safety message and generates the trust value of message originator. Due to the limited storage capacity, each vehicle periodically uploads the trust value to a nearby RSU. After receiving various trust values from vehicles, the RSU calculates the reputation value of message originator of road safety message using multi-criteria decision-making model, packs the reputation value into a block, and competes to add the block into blockchain. We evaluate the proposed Trust<sup>Block</sup><sub>MCDM</sub> approach through simulation experiments using OMNeT++ and compare its performance with prior blockchain-based decentralized trust management approach. The simulation results indicate that the proposed Trust<sup>Block</sup><sub>MCDM</sub> approach can not only improve fictitious message detection rate and malicious vehicle detection rate, but also can increase the number of dropped fictitious messages.<br></p>

scholarly journals IOT Based Health Monitoring System

2021 ◽  
Vol 9 (8) ◽  
pp. 1176-1178
Author(s):  
Sowmya G
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Social Insurance ◽  
Vital Signs ◽  
Smart Phone ◽  
Smart Devices ◽  
The Internet ◽  
Health State ◽  
Health Monitoring System ◽  
Vital Parameters

Abstract: The increased use of smart phones and smart devices in the health zone has brought on extraordinary effect on the world’s critical care. The Internet of things is progressively permitting to coordinate sensors fit for associating with the Internet and give data on the health condition of patients. These technologies create an amazing change in medicinal services during pandemics. Likewise, many users are beneficiaries of the M-Health (Mobile Health) applications and E-Health (social insurance upheld by ICT) to enhance, help and assist continuously to specialists who help. The main aim of this ‘IOT Health Monitoring System’ is to build up a system fit for observing vital body signs such as body temperature, heart rate, pulse oximetry etc. The System is additionally equipped measuring Room Temperature and Humidity and Atmosphere CO level. To accomplish this, the system involves many sensors to display vital signs that can be interfaced to the doctor’s smart phone as well as caretakers’ smartphone. This prototype will upload the readings from the sensor to a server remotely and the information gathered will be accessible for analysis progressively. It has the capacity of reading and transmitting vital parameters measured to the cloud server and then to any Smartphone configured with Blynk App. These readings can be utilized to recognize the health state of the patient and necessary actions can be taken if the vital parameters are not in prescribed limits for a longer period. Keywords: IOT Health Monitoring System, Vital parameters, Blynk App

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