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      Smartphone Sensors for Health Monitoring and Diagnosis

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          Abstract

          Over the past few decades, we have witnessed a dramatic rise in life expectancy owing to significant advances in medical science and technology, medicine as well as increased awareness about nutrition, education, and environmental and personal hygiene. Consequently, the elderly population in many countries are expected to rise rapidly in the coming years. A rapidly rising elderly demographics is expected to adversely affect the socioeconomic systems of many nations in terms of costs associated with their healthcare and wellbeing. In addition, diseases related to the cardiovascular system, eye, respiratory system, skin and mental health are widespread globally. However, most of these diseases can be avoided and/or properly managed through continuous monitoring. In order to enable continuous health monitoring as well as to serve growing healthcare needs; affordable, non-invasive and easy-to-use healthcare solutions are critical. The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an individual’s health and wellbeing with negligible additional costs. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. A discussion on regulatory policies for medical devices and their implications in smartphone-based healthcare systems is presented. Finally, some future research perspectives and concerns regarding smartphone-based healthcare systems are described.

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          Most cited references216

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          Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group.

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          The modified Airlie House classification of diabetic retinopathy has been extended for use in the Early Treatment Diabetic Retinopathy Study (ETDRS). The revised classification provides additional steps in the grading scale for some characteristics, separates other characteristics previously combined, expands the section on macular edema, and adds several characteristics not previously graded. The classification is described and illustrated and its reproducibility between graders is assessed by calculating percentages of agreement and kappa statistics for duplicate gradings of baseline color nonsimultaneous stereoscopic fundus photographs. For retinal hemorrhages and/or microaneurysms, hard exudates, new vessels, fibrous proliferations, and macular edema, agreement was substantial (weighted kappa, 0.61 to 0.80). For soft exudates, intraretinal microvascular abnormalities, and venous beading, agreement was moderate (weighted kappa, 0.41 to 0.60). A double grading system, with adjudication of disagreements of two or more steps between duplicate gradings, led to some improvement in reproducibility for most characteristics.
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            Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

            Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.
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              Wearable Sensors for Remote Health Monitoring

              Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                09 May 2019
                May 2019
                : 19
                : 9
                : 2164
                Affiliations
                [1 ]Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada; majums3@ 123456mcmaster.ca
                [2 ]School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
                Author notes
                [* ]Correspondence: jamal@ 123456mail.ece.mcmaster.ca ; Tel.: +1-905-525-9140 (ext. 27137)
                Author information
                https://orcid.org/0000-0003-0517-6008
                https://orcid.org/0000-0002-6390-0933
                Article
                sensors-19-02164
                10.3390/s19092164
                6539461
                31075985
                bef7c6d3-565b-47cc-9978-d0e557cb3065
                © 2019 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 ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 March 2019
                : 30 April 2019
                Categories
                Review

                Biomedical engineering
                smartphone,remote healthcare,mhealth,telehealth,medical device,regulation,smartphone sensor

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