Deep Learning in IoT-Based Healthcare Applications

2021 ◽  
pp. 183-200
Author(s):  
S.M. Zobaed ◽  
Mehedi Hassan ◽  
Muhammad Usama Islam ◽  
Md Enamul Haque
Keyword(s):  
Deep Learning ◽  
Healthcare Applications

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications

2020 ◽  
Vol 79 (41-42) ◽  
pp. 31663-31690
Author(s):  
Debadyuti Mukherjee ◽  
Riktim Mondal ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Healthcare Applications ◽  
Smartphone Sensors

scholarly journals Towards Automatic and Interpretable Assignments of Patients Presenting with Pain to the Emergency Department

2021 ◽  
Author(s):  
J.A. Hughes ◽  
N.J. Brown ◽  
Thanh Vu ◽  
Anthony Nguyen
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Quality Of Care ◽  
Machine Learning Techniques ◽  
Initial Assessment ◽  
Common Symptom ◽  
Healthcare Applications ◽  
Learning Techniques ◽  
Real Time Identification

Introduction: Pain is the most common symptom that patients present with to the emergency department. It is hard to identify patients who have presented in pain to the emergency department when compliance with structured pain assessment is low. An ability to identify patients presenting in pain allows further investigation of the quality of care provided. Background: Machine and deep learning techniques are commonly used for text analysis in healthcare. Applications such as the classification of diagnosis and unplanned readmissions from textual medical records have previously been described. In other work, conventional and deep-learning techniques have demonstrated high performance in identifying patients presenting to the emergency department in pain. However, these models have lacked interpretability. Methods: This paper proposes the use of machine learning techniques to identify patients who present in pain based upon their initial assessment using interpretable deep learning models. Results: The interpretable deep learning model of pain identification was shown to have more accuracy and precision than other machine and deep learning techniques. This technique has significant application to large datasets for the identification of the quality of care and real-time identification of patients presenting in pain to improve their care.

scholarly journals Deep learning for healthcare applications based on physiological signals: A review

2018 ◽  
Vol 161 ◽  
pp. 1-13 ◽  
Author(s):  
Oliver Faust ◽  
Yuki Hagiwara ◽  
Tan Jen Hong ◽  
Oh Shu Lih ◽  
U Rajendra Acharya
Keyword(s):  
Deep Learning ◽  
Physiological Signals ◽  
Healthcare Applications

Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

2020 ◽  
Vol 12 (3) ◽  
pp. 14-27 ◽  
Author(s):  
John Sarivougioukas ◽  
Aristides Vagelatos
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Ubiquitous Computing ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Learning Networks ◽  
Healthcare Applications ◽  
Medical Diagnoses ◽  
The Given

Ubiquitous computing environments that are involved in healthcare applications are typically characterized by dynamically changing contexts. The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystem must be capable of extracting medically valuable characteristics, making precise decisions, and taking medically appropriate actions. In this framework, deep learning networks can be used for data fusion of large and complex sets of information in order to make the appropriate medical diagnoses. The quality of decisions depends on the selection of appropriate network weights, which define a transformation of the given input into a diagnosis. Denotational mathematics provide a promising framework for modeling deep learning networks and adjusting their behavior by adapting their weights for the given input. Furthermore, the fidelity of the network's output can be controlled by applying a regulator to the weights values. The authors show that Denotational Mathematics can serve as a rigorous framework for modeling and controlling deep learning networks, thereby enhancing the quality of medical decision making.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 156-177
Author(s):  
Janani Viswanathan ◽  
N. Saranya ◽  
Abinaya Inbamani
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Diagnostics ◽  
Healthcare Sector ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Medical Sector

Deep learning (DL) and artificial intelligence (AI) are emerging tools in the healthcare sector for medical diagnostics. This chapter elaborates on general reasons for the popularity of computational techniques such as deep learning and machine learning (ML) applications in the medical image processing domain. The initial part of this chapter focuses on reviewing the fundamental concepts of DL algorithms, competence with machine learning, need in healthcare, applications, and challenges in medical image processing. Doing so allows understanding the reasons for the construction of all of them and offers a different view on various domains in the medical sector. The tools and technology required for DL, selection, implementation, optimization, and testing are discussed with respect to an application of cancer detection. Thus, this chapter gives an overall vision of deep learning concepts related to biomedical research.

Deep learning based fall detection using smartwatches for healthcare applications

2022 ◽  
Vol 71 ◽  
pp. 103242
Author(s):  
Gökhan Şengül ◽  
Murat Karakaya ◽  
Sanjay Misra ◽  
Olusola O. Abayomi-Alli ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Fall Detection ◽  
Healthcare Applications

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2018 ◽  
Vol 10 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Optimization Techniques ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

2019 ◽  
Author(s):  
Alireza Abedin ◽  
S. Hamid Rezatofighi ◽  
Qinfeng Shi ◽  
Damith C. Ranasinghe
Keyword(s):  
Deep Learning ◽  
Older People ◽  
Activity Recognition ◽  
Human Activity ◽  
Data Streams ◽  
Sparse Data ◽  
Human Activity Recognition ◽  
Learning Activity ◽  
Healthcare Applications ◽  
Sensing Applications

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.

Sign in / Sign up

Export Citation Format

Share Document