Received: 23 September 2019 Accepted: 2 December 2019 Internet of Things (IoT) based smart health monitoring system is a patient monitoring system in which a patient can be monitored 24 hours. In the present world, IoT is changing the infrastructure of technologies. By facilitating effortless interaction among various modules, IoT has enabled us to implement various complex systems such as smart home appliances, smart traffic control systems, smart office systems, smart environment, smart vehicles and smart temperature control systems and so on in very little space. Health monitoring systems are one of the most notable applications of IoT. Many types of designs and patterns have already been implemented to monitor a patient’s health condition through IoT. In this paper, a review of IoT based smart health monitoring systems is presented. The latest innovative technologies developed for IoT based smart health monitoring system with their merits and demerits have been discussed. This review aims to highlight the common design and implementation patterns of intelligent IoT based smart health monitoring devices for patients.

/pdf/developing-iot-based-smart-health-monitoring-systems-a-237daay9am.pdf

Developing IoT Based Smart Health Monitoring Systems: A Review

This paper proposes an automatic classification technique that uses leaf images some medicinal plants. It is primarily the core reason that drives the research presented here, including the introduction of new innovative segmentation and classification techniques that are deployed to facilitate automatic detection. The major aim of the work is to introduce a new leaf disease prediction technique. The study conducted here a unique but effective image segmentation, feature extraction, as well as plant leaf disease classification. The proposed approach initially preprocesses leaf images of plants thereafter which the diseased sections of the plant are segmented by deploying Particle Swarm Optimization (PSO)–based fuzzy c means segmentation (PSO‐FCM), Gaussian Mixture Model (GMM)–based background subtraction. Vein and shape features, edge‐based feature extraction, and texture characteristics or texture features (TF) are computed. This methodology classifies the leaves of medicinal plants by deploying the Multiple Kernel Parallel Support Vector Machine (MK‐PSVM) classifier. The classifier is implemented via the use of MATLAB classifier. The results are measured using the accuracy, sensitivity, specificity, precision, and F‐measure metrics. Experimental results depict that the classifiers that have been proposed here achieve a higher classification accuracy enabling leaf detection.

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

This paper focuses on elderly and disabled people who live alone and run certain risks, such as frequently falling, entering dangerous places within the house (the second floor, the attic, the basement) or being unable to handle certain problems. Each person has particular needs which must be considered. It is even necessary to foresee that they may not face difficulties only in their houses, but also when they go shopping or for a walk. In such occasions, they may lose their sense of direction. Mobile technologies can provide solutions to these problems, generating a non-invasive control system which may be monitored by family members and friends. Using an app with such characteristics helps the person feel free and safe at the same time. This paper presents a mobile app prototype for planned care and follow-up of elderly or disabled people. The app uses the sensors in devices such as GPSs, accelerometers, barometers, etc, and enables a non- invasive control and follow-up system. The Android-developed app can be easily installed on a wide variety of devices, and facilitate its portability towards android based wearable platforms such us Wear OS and Android Wear. This makes the use of this app more transparent, safer, and less invasive.

Mobile follow-up system for elderly and disabled people

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. An Optimum Inexact Design for an Energy Efficient Hearing Aid Sai Praveen Kadiyala, Aritra Sen, Shubham Mahajan, Quingyun Wang, Avinash Lingamaneni, James Sneed German, Hong Xu, Krishna Palem, Arindam Basu

/pdf/an-optimum-inexact-design-for-an-energy-efficient-hearing-2dswlquhqv.pdf

An optimum inexact design for an energy efficient hearing aid

Hearing aids are a field of medical devices, powered by digital signal processing techniques. Development of hearing aid modules is realized usually in the first stages using prototyping boards equipped with specialized Embedded DSP. The paper presents a generic model of a hearing aid developed on a DSP development system. The method used to develop and prototype the model (Rapid prototyping) is the core contribution of the paper. Starting from a standard algorithm flow, the system allows to demonstrate and to experiment the basic principles and processing algorithms in hearing aids. The purpose is mainly educational, but with the potential for a commercial application. The main advantage is the possibility to visualize, analyze and enhance every step of the processing flow. To develop, test and demonstrate the functionality we used simple Virtual Instrumentation concepts, making possible to keep all the components of the test bench in a single PC.

A Rapid Prototyping model concept for a DSP based hearing aid

The system combines the wireless communication technology and medical sensor detection technology, to realize the remote medical monitoring of multiple physiological parameters. As the basis of the Android platform, the mobile health platform construct a health report with physical examination, health guidance for every members in family. After using the corresponding physiological data acquisition sensor module to collect physiological data, the mobile intelligent terminal will send the collected results to the background center server with Bluetooth. The central server is responsible for storing the patient's medical information and providing the data sharing service for other subsystems in the system. Android mobile data collection terminal and server connect together each other with Wifi network, and data services through implementation based on HTTP web server interface. The external terminal get medical information through accessing to the web server. Data storage in the client using SQLite database, server using MySQL database. It mainly record the daily health conditions of the family members, which can effectively manage various chronic diseases and the prevention of disease. At the same time, for some of the family members who have been suffering from chronic diseases, it can help patients manage their daily diet and treatment.

Design on mobile health service system based on Android platform

With the advent of large numbers of data and a large number of samples, the traditional support vector machine algorithm is not applicable because of it's too much memory overhead and time overhead. Traditional parallel SVM based on MapReduce is to separate the train data into multiple sub-training sets on MapReduce-based model, these sub-datasets are trained by SVM, and then, get the support vectors and obtain a classification mode. It can reduce the training time,but,its classification accuracy needs to be improved. In addition,designing suitable kernel functions for a given problem is the core issue of support vector machine. Because a single kernel function has a fixed format and its changing space is relatively small, the generalization capability and robustness of single kernel function SVMs are limited. Therefore, in this paper, we propose a hybrid kernel function and a hybrid parallel support vector machine to improve the classification accuracy and reduce the training time. The experimental results show that this learning strategy improves the classification accuracy, reduces the training time, and speeds up the classification speed.

Optimization of multi kernel parallel support vector machine based on Hadoop

In this paper, we describe the basic principle of the wide dynamic range compression algorithm, the hardware structure of digital hearing aids, and the structure of core and peripheral devices of DSP5535. On this basis, through the introduction of the realization of the wide dynamic range compression algorithm, this paper describes how to optimize the algorithm in hearing aid platform based on the DSP5535. In the optimization method, some optimization methods make the required cycles of algorithm related operations to reduce 2–3 times, which fully demonstrates the effectiveness of the optimization method. These optimization methods play an important role for the implement of WDRC algorithm in the real-time processing of speech signals.

Optimized realization of wide dynamic range compression based on DSP5535 hearing aid platform

The results of minimum variance (MVDR) algorithm map to the signal subspace to get the weighted coefficients based on feature subspace theory, In the low SNR environment, there will be the main lobe offset, waveform distortion, serious decline in the performance of beamforming. This paper puts forward an improved method for the emergence of this kind of situation. Using the principle of correlation matrix, the covariance matrix of the original algorithm is restructured in order to isolate signal subspace and noise subspace better. Using Unitary transformation, the complex matrix computation transforms to the real matrix computation. While reducing the amount of computation, the algorithm has robust performance in the case of fewer samples. Finally, computer simulation is used to verify the effectiveness of the improved method.

An improved beamforming algorithm based on feature space

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Binwen Fan

Author Tools

Chat about Author

Papers

Design on mobile health service system based on Android platform

Optimization of multi kernel parallel support vector machine based on Hadoop

Optimized realization of wide dynamic range compression based on DSP5535 hearing aid platform

An improved beamforming algorithm based on feature space