A Review on Deep Neural Networks for ICD Coding.pdf
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Citation Kristoffersson, A.; Lindén,
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Citation: Kristoffersson, A.; Lindén,
M. A Systematic Review of Wearable
Sensors for Monitoring Physical
Activity. Sensors 2022, 22, 573.
https://doi.org/10.3390/s22020573
Academic Editor: Emiliano Schena
Received: 29 November 2021
Accepted: 5 January 2022
Published: 12 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Review
A Systematic Review of Wearable Sensors for Monitoring
Physical Activity
Annica Kristoffersson * and Maria Lindén
School of Innovation, Design and Engineering, Mälardalen University, Box 883, 721 23 Västerås, Sweden;
[email protected]
* Correspondence: [email protected]
Abstract: This article reviews the use of wearable sensors for the monitoring of physical activity (PA)
for different purposes, including assessment of gait and balance, prevention and/or detection of falls,
recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring
of neurological disease progression. The article provides in-depth information on the retrieved
articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy
participants vs patients, and study conditions. It is well known that motion patterns change with age
and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older
persons are rare. Gender distribution was not even provided in several studies, and others included
only, or a majority of, men. Another shortcoming is that none of the studies were conducted in
real-life conditions. Hence, there is still important work to be done in order to increase the usefulness
of wearable sensors in these areas. The article highlights flaws in how studies based on previously
collected datasets report on study samples and the data collected, which makes the validity and
generalizability of those studies low. Exceptions exist, such as the promising recently reported open
dataset FallAllD, wherein a longitudinal study with older adults is ongoing.
Keywords: wearable sensors; sensor systems; gait and balance; fall detection; physical activity
recognition; rehabilitation; neurological diseases; data sets; research shortcomings; user demography
1. Introduction
In this article, we review the use of wearable sensors for monitoring physical activity
(PA). The monitoring of PA has a number of different purposes. These include: the assess-
ment of gait and balance, prevention and/or detection of falls, the recognition of various
PAs. PA monitoring is also useful for the conduction and assessment of rehabilitation
exercises and for the monitoring of neurologi ...
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The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
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global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
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The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinea...
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* Complete Documentation
* Complete Presentation Slides
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* Video Tutorials
* Supporting Softwares
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=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
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Citation Kristoffersson, A.; Lindén,
����������
�������
Citation: Kristoffersson, A.; Lindén,
M. A Systematic Review of Wearable
Sensors for Monitoring Physical
Activity. Sensors 2022, 22, 573.
https://doi.org/10.3390/s22020573
Academic Editor: Emiliano Schena
Received: 29 November 2021
Accepted: 5 January 2022
Published: 12 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Review
A Systematic Review of Wearable Sensors for Monitoring
Physical Activity
Annica Kristoffersson * and Maria Lindén
School of Innovation, Design and Engineering, Mälardalen University, Box 883, 721 23 Västerås, Sweden;
[email protected]
* Correspondence: [email protected]
Abstract: This article reviews the use of wearable sensors for the monitoring of physical activity (PA)
for different purposes, including assessment of gait and balance, prevention and/or detection of falls,
recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring
of neurological disease progression. The article provides in-depth information on the retrieved
articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy
participants vs patients, and study conditions. It is well known that motion patterns change with age
and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older
persons are rare. Gender distribution was not even provided in several studies, and others included
only, or a majority of, men. Another shortcoming is that none of the studies were conducted in
real-life conditions. Hence, there is still important work to be done in order to increase the usefulness
of wearable sensors in these areas. The article highlights flaws in how studies based on previously
collected datasets report on study samples and the data collected, which makes the validity and
generalizability of those studies low. Exceptions exist, such as the promising recently reported open
dataset FallAllD, wherein a longitudinal study with older adults is ongoing.
Keywords: wearable sensors; sensor systems; gait and balance; fall detection; physical activity
recognition; rehabilitation; neurological diseases; data sets; research shortcomings; user demography
1. Introduction
In this article, we review the use of wearable sensors for monitoring physical activity
(PA). The monitoring of PA has a number of different purposes. These include: the assess-
ment of gait and balance, prevention and/or detection of falls, the recognition of various
PAs. PA monitoring is also useful for the conduction and assessment of rehabilitation
exercises and for the monitoring of neurologi ...
Utility and Added Value of Classifications in Health Information Systems
Health Information Systems; ICD, ICD11, SNOMED-CT, Use Cases showing benefits of use of classification- terminology systems; avoid and e-tower of Babel; electronic health record, Enhance Patient Care, Decision Support, Safety & Quality
An internet of things-based automatic brain tumor detection system
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection system
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
COVID-19 VACCINATION CLASSIFICATION OF OPINION MINING WITH SEMANTIC KNOWLEDGE...
The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning,
which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool.
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output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment
plans for the respective individuals i.e., students based on their given details which include diagnosis,
symptoms, and vaccination history. The information given by the students can be automatically processed
and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the
students are inferred from the given knowledge.
Enhancement of Multi-Modal Biometric Authentication Based on IRIS and Brain N...
The proposed method describes the current forensics and biometrics in a modern approach and implements the concept of IRIS along with brain and resolves the issues and increases the strength of Digital Forensics Community. It has enormous features in biometrics to enhance diverse security levels. A new method to identify individuals using IRIS Patterns with the brain wave signals (EEG) is proposed. Several different algorithms were proposed for detecting, verifying and extracting the deterministic patterns in a person’s IRIS from the Eye. The extracted EEG recordings form the person\'s brain has proved to be unique. Next we combine EEG signals into the IRIS patterns a biometric application which makes use of future multi modal combination architecture. The proposed forensic research directions and argues that to move forward the community needs to adopt standardized, modular approaches for person identification. The result of each authentication test is compared with the user\'s pre-recorded measurements, using pattern recognition methods and signal-processing algorithms.
Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet.
Role of Advanced Machine Learning Techniques and Deep Learning Approach Based...
Accurate Medical diagnosis is not always possible at every medical center, especially in the Developing Countries where poor healthcare services and lack of advanced diagnostic methods and equipments affects procedures of medical diagnosis .Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, diagnostic errors and undesirable results are reasons for a need for Machine Learning Techniques based decision support system, which in turns reduce diagnostic errors, increasing the patient safety and save lives. This research focuses on this aspect of Medical diagnosis by learning pattern through the collected dataset of respiratory diseases such as pneumonia and Covid 19, also consist implementation and test of intelligent medical decision support system to assist physicians and radiologists can deliver great assistance by improving their decision making ability. In this Research paper, the proposed System use Neural network Resnet 50 and transfer learning technique to classify these severe diseases and performs evaluation of precision, accuracy, speci city of Decision support system. Patel Smitkumar Hareshbhai "Role of Advanced Machine Learning Techniques and Deep Learning Approach Based Decision Support System for Accurate Diagnosis of Severe Respiratory Diseases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47655.pdf Paper URL : https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/47655/role-of-advanced-machine-learning-techniques-and-deep-learning-approach-based-decision-support-system-for-accurate-diagnosis-of-severe-respiratory-diseases/patel-smitkumar-hareshbhai
neuropredict: a proposal and a tool towards standardized and easy assessment ...
neuropredict: a proposal and a tool towards standardized and easy assessment ...Pradeep Redddy Raamana
Proper application of machine learning to accurately evaluate the accuracy of biomarkers is challenging and error-prone for those without expertise in machine learning or programming. We offer an easy to use tool which implements the best practices and produces a comprehensive yet clinically-relevant report when comparing several biomarkers or different methods/studies. It is called neuropredict, which is open source and applicable to any domain whose biomarkers can be represented by numbers.The Alzheimer's Disease Research Network and the Uniform Data Set
Alzheimer's disease (AD) is recognized as a public health crisis worldwide (IADRP, 2013). AD is a complex neurodegenerative disease and the leading cause of dementia among the elderly people (Evans et al., 1989). Currently, there are approximately five million AD cases in the United States and about 35 million cases worldwide (Alzheimer's Disease International, 2009.
The focus of this case study is on the Uniform Data Set (UDS), a longitudinal database on Alzheimer’s patients, and the 29 Alzheimer’s Disease Centers (ADC) that submit their data to the UDS and actively collaborate in the ongoing maintenance, development and research utilization of the database. The ADCs are based in major medical institutions across the United States. They have a multi-decade track record of collaborative research and a networked and virtual approach to the scientific study of AD. The central coordinating mechanism for the ADCs and the UDS is the National Alzheimer’s Coordinating Center (NACC), which is located at the University of Washington. The NACC coordinates data collection and supports collaborative research among the ADCs.
Deep learning for biomedicine
Lecture at Southeast Asia Machine Learning (SEA ML) School, Depok, Greater Jakarta, Indonesia, July 2019.
A Study Based On Methods Used In Detection of Cardiac Arrhythmia
This document summarizes research on methods for detecting cardiac arrhythmia. It discusses how machine learning and deep learning techniques like convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have achieved higher accuracy than traditional feature-based machine learning methods for arrhythmia detection. The document reviews several papers that have used techniques like CNNs, LSTMs, oversampling, and focal loss to improve arrhythmia detection. It identifies limitations in relying too heavily on feature extraction and suggests that deep learning methods that learn features automatically can help address these limitations and improve accuracy.
Neural Network and Applications An Overview
Models of the brain and nervous system for Highly parallel to the Process information much more like the brain than a serial computer like Learning and simple principles with complex behaviour as well Applications are powerful problem solvers and also used in biological models. Dr. Mukesh Kumar Lalji | Dr. Ashish Dongre | Rajeev Gupta | Dr. Deepak Paliwal | O. P. Shrivastava | N. K. Jain | Mukesh Katariya | Manoj Sonkusre | Gaurav Lalji | Divya Lalji | Arvind Jain | AR. Sandhya Ekbote "Neural Network & Applications- An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41171.pdf Paper URL: https://www.ijtsrd.comcomputer-science/computer-network/41171/neural-network-and-applications-an-overview/dr-mukesh-kumar-lalji
KNOWLEDGEBASE SYSTEMS IN NEURO SCIENCE - A STUDY
This document summarizes research on using knowledge-based systems and soft computing techniques in neuroscience. It provides an abstract for an article on this topic and then summarizes several other research papers that have applied expert systems, fuzzy logic, neural networks, and other computational approaches to problems in neurology, including diagnosing stroke type, modeling neuromuscular disorders, analyzing EEG data, and developing diagnostic systems for epilepsy. The document surveys this area and provides high-level summaries of several studies that have developed computational models and expert systems to assist with neurological diagnosis and analysis.
IJSCAI PAPER 29--04---2.pdf
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
A Study of Deep Learning Applications
Deep learning is a collection of machine learning algorithms utilizing multiple layers, with which higher levels of raw data are slowly removed. For example, lower layers can recognize edges in image processing whereas higher layers may define concepts for humans such as numbers or letters or faces. In this paper we have done a literature survey of some other papers to know how useful is Deep Learning and how to define other Artificial Intelligence things using Deep Learning. Anirban Chakraborty "A Study of Deep Learning Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31629.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31629/a-study-of-deep-learning-applications/anirban-chakraborty
Knowledgebase Systems in Neuro Science - A Study
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
Similar to A Review on Deep Neural Networks for ICD Coding.pdf (20)
Alzheimer’s diseases classification using YOLOv2 object detection technique
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RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODS
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Utility and Added Value of Classifications in Health Information Systems
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An internet of things-based automatic brain tumor detection system
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COVID-19 VACCINATION CLASSIFICATION OF OPINION MINING WITH SEMANTIC KNOWLEDGE...
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IEEE PROJECTS 2023-2024 TITLE LIST
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