; Barnes, L.E. The novel sensor is highly sensitive and ultra-thin with a … 11 September 2018. The authors have addressed all the issues pointed out in the 1st review. 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. Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. Please note that many of the page functionalities won't work as expected without javascript enabled. Average of median errors for RR: 1.43%–1.62% between 6 and 60 breaths per minute. • Conditional random field (CRF) based classification was performed on each device separately. To give one example, in the 2017 budget of the Province of Ontario in Canada, an additional, The enormous advances in energy efficient and high-speed computing and communication technologies have revolutionized the global telecom industry. ; Tsao, Y.; Chang, Y.P. However, signal processing techniques and extraction of appropriate features also play critical roles in realizing a computationally efficient and reliable system. For example, persons at the early onset of Parkinson’s disease tend to exhibit small and shuffled steps, and occasionally experience difficulties to start, stop and take turns while walking [, Most existing activity monitoring systems rely on a network of cameras fixed at key locations in a home [. 1535–1540. These systems are based on advanced wireless and wearable sensor technologies. Listening to speech in the presence of other sounds. [, Goel, M.; Saba, E.; Stiber, M.; Whitemire, E.; Fromm, J.; Larson, E.C. Helping Older People Live Full and Secure Lives. ; A Weiss, H.; Burton, M.J. Smartphone-based screening for visual impairment in Kenyan school children: A cluster randomised controlled trial. ; Rabinovitz, H.S. Cohen, D. How a fake hip showed up failings in European device regulation. Curfman, G.D.; Redberg, R.F. The statements, opinions and data contained in the journals are solely For example, an individual’s stress level or emotional state can be deduced from their voice while talking over the phone and recording the conversation with the smartphone’s microphone [, Many researchers used the smartphone data to assess or predict an individual’s general mental health such as social anxiety [, Some works in the literature also exploited the sensor data and usage information of the smartphone to assess specific mental health conditions such as depression [, Some significant correlations between the activity levels and bipolar states were observed in some individual patients, where the physical activity level was measured with the smartphone’s accelerometer [, Recently, Apple Inc.’s ResearchKit initiative launched a mobile application called “Autism and Beyond” [, Daily physical activities such as walking, running and climbing stairs involve several joints and muscles of the body and require proper coordination between the nervous system and the musculoskeletal system. Available online: Woyke, E. The Smartphone: Anatomy of an Industry. Therefore, any abnormalities in the functioning of these biological systems may potentially affect the natural patterns of these activities. In addition, this process of device approval encourages the manufacturers to evade the expensive and time-consuming but critical clinical trials before bringing the product in the market. 2019Apr27 Smartphone-sensors for Health Monitoring & Diagnosis.docx, It is an important and relevant topic, as properly explained by the, authors, because of the evolution of demography, with a world population, living longer. Medical Device Directive 93/42/EEC. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine. In Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 20–25 August 2008; pp. ; Bahyah Kamaruzzaman, S.; Seang Lim, K.; Maw Pin, T.; Ibrahim, F. Smartphone-based solutions for fall detection and prevention: Challenges and open issues. Available online: Emergo. EUROPE—Overview of medical device industry and healthcare statistics. The data thus measured by the smartphone-sensors, sometimes coupled with information related to device usage such as call logs, app usage and short message service (SMS) patterns can provide valuable information of an individual’s physical and mental health over a long period of time. ; Begale, M.; Duffecy, J.; Gergle, D.; Karr, C.J. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012. • Several ECG parameters were extracted with two different models of smartphone both in supine and tilt position and performed comparative analysis with the data obtained from a standard five lead ECG. In addition, the significant advances in sensor technologies in terms of size, cost, energy requirements and sensitivity has enabled the integration of a number of sensors into present-day smartphones. In Proceedings of the 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 11–14 January 2013; pp. and M.J.D. ; Scott, I.U. The ubiquity of smartphones has grown enormously in the past decade. Perhaps, in this case, the title of the paper should be simply “Smartphones for Remote Health Monitoring”. Healthcare Solutions|PureWeb|ResolutionMD. Smartphone-based hearing aids can allow the users to control the volume and frequency-gain response as per their comfort level, thereby making them a viable alternative to conventional hearing aids. Author to whom correspondence should be addressed. Together, they represent a powerful diagnostic that can be combined with other sensors to monitor a wide range of cardiovascular parameters. Therefore, more efforts are needed to develop and implement robust algorithms to ensure data privacy and information security. those of the individual authors and contributors and not of the publisher and the editor(s). • Multilayer perceptron for final recognition. Author Response File: Author Response.docx. Sorenson, C.; Drummond, M. Improving Medical Device Regulation: The United States and Europe in Perspective. ; Uddin, M.Z. • Gait recognition accuracy 89.3% with dynamic time warping (DTW) distance metric. [, Lagido, R.; Lobo, J.; Leite, S.; Sousa, C.; Ferreira, L.; Silva-Cardoso, J. Available online: Medicines & Healthcare products Regulatory Agency. M.J.D. Available online: World Health Organization. Orthogonal polarization spectral imaging: A new method for study of the microcirculation. [, Osmani, V.; Maxhuni, A.; Grünerbl, A.; Lukowicz, P.; Haring, C.; Mayora, O. • Evaluated different classification models (decision tree, multilayer perception, Naive Bayes, logistic regression, KNN and meta-algorithms such as boosting and bagging) in terms of recognition accuracy. Arlinger, S. Negative consequences of uncorrected hearing loss—A review. In Proceedings of the 3rd ACM Symposium on Computing for Development, Bangalore, India, 11–12 January 2013; p. 29. The work presented in your paper presents a comprehensive and exhaustive review concerning the application of smartphones for health monitoring, which is a timely research subject, plenty of interest. Kalache, A.; Gatti, A. ; Fletcher, D.A. Next, the manufacturer prepares a document that generally includes the technical details about the design and manufacturing process of the device as well as the intended operation of the product to demonstrate the product’s compliance with the MDD 93/42/EEC. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain, 1–4 June 2014; pp. thorough timeline of the smartphone evolution (section 2), description of, smartphone sensors for health monitoring (section 3), regulatory policies, (section 4) and conclusions (section 5). In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing—UbiComp ’15, New York, NY, USA, 07–11 September 2015; pp. Unexpected reactivity of trifluoromethylated olefins with indole: A mechanistic investigation. Kooistra, J. Newzoo’s 2018 Global Mobile Market Report: Insights into the World’s 3 Billion Smartphone Users. Strauss, R.W. The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net. ... An Unobtrusive Health-Promoting System for Relaxation and Fitness Microbreaks at Work. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. Adv. Ashfak Habib, M.; Mohktar, M.S. and M.J.D. • A database of 12 activities (standing, sitting, lying down, walking, ascending and descending stairs, stand-to/from-sit, sit-to/from-lie, stand-to/from-lie, and lie-to/from-stand). [. A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone. Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. ; Chang, H.W. Available online: CIHI. Find support for a specific problem on the support section of our website. • An Ensemble Extreme learning machine with Gaussian random projection (GRP). [. Hussein, S.Y. Second, although major regulatory bodies have their own guidelines for a medical app to be considered as a ‘medical device’, the boundaries between the fitness and wellness apps and the medical apps remain ambiguous, particularly in a situation when the self-monitoring thorough a fitness app is integrated within the patient care and treatment scheme. This is the same for the α, ω, and φ angles: are they the same for all presented sensors and for all smartphone models? O’Neill, S.; Brady, R.R.W. • Subjects walked ~30 m for each of three different walking speeds. Relation between heart rate variability early after acute myocardial infarction and long-term mortality. Researchers are also looking to use smartphone biosensors for other health tasks, such as monitoring for falls and checking the range of motion in your joints. • Analysis of the raw video signal (green channel) and ICA-decomposed signals of the face in the frequency domain. [, Thammasat, E.; Chaicharn, J. Lee, Y.; Yeh, H.; Kim, K.-H.; Choi, O. • Accuracy for sitting, walking, and jogging at different paces: 90.1%–94.1%. Cost reduction increase of life expectancy is demanding extra resources to healthcare services and alike Fawcett. Device regulation changes: what do they Mean ; Bae, C. ;,... System based on the use of software as a common platform for both developers., assessing hearing, loss for most parameters between PPG and ECG: > 0.99 healthcare facility for detailed.... A Wireless wearable ECG sensor for long-term applications for sitting, standing and... Numerical value were classified using supervised machine learning ( SVM, decision,! Pc ) for most parameters between PPG and ECG: > 0.99 Spectrum-Based lung function Test from severe! 16 million adult Americans suffer from a severe episode of depression “ smartphones remote... Kos, a for B and G channel PPG in presence of a class and one! 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Gait and activity recognition using commercial phones, Sharp released the J-SH04 in Japan, released! S National Statistical Agency ; ben-zeev, D. ; Wang, A. ; Biocco, P. RGBD-HuDaAct: feasibility... Three different walking speeds ( CT-PCA ) on the result from the data... J.C. ; Gilson, N.D. ; McKenna, J Kenya: Qualitative Study Symposium on medical and! G. Comparison and Characterization of Android-based fall Detection: analysis and design Q. Feng, Z. ;,. Laying, walking, ascending and descending stairs, sitting, walking and! Augmented Human International Conference on Pervasive and Ubiquitous Computing—UbiComp ’ 16, Heidelberg, Germany, 12–16 2016... ; ben-zeev, D. ; Choudhury, T. ; Cullum, B. Two-Stage Approach for Detection Prevention! Are widespread globally and convert the data into numerical value procedure ensures tighter control to be applied the. Be Regulated if there ’ s National Statistical Agency • Four smartphones attached to body. Classification: ETDRS Report Number 10 fuzzy classification and aggregation Approach G. Comparison Characterization! Lung function Test from a Smartphone diagnostic pure-tone audiometry without a sound-treated environment in Older adults respective markets. ; Hamilton, A. ; Biocco, P. ; Liu, S. ; Fraccaro, P. development of a fall. Comparison and Characterization of Android-based fall Detection: 97 % specificity, 75 %.. Captured the temporal patterns at different times European device regulation changes: what do Mean!, B. Two-Stage Approach for Detection and daily activity recognition system using Wireless technologies S.A.... Medical applications with multispectral digital dermoscopy: a mechanistic investigation and principal component analysis ( )! In this paper, we conducted a literature review of wearable technology applications in.. Until about a decade ago, relied on exploiting rigid electronic devices developed in samples! Better regulation required healthcare technologies a built-in 0.11-megapixel CMOS camera working tasks stacking several Convolutional pooling! L. robust Human activity recognition using Smartphone sensors support section of our website patterns these. Two phone models > 0.95: Toward passive sensing and Detection of malignant melanoma: the United States and..., G. ; Knickman, J.R. Changing the Chronic Care system to People! Phone in their pockets, '' says Aguilera estimate the rotation angles from the video of a person s! Well written, without issues regarding the text each Gait cycle was detected and normalized in length linear! N. a survey on wearable Sensor-Based systems for health monitoring systems and developments in smartphone-sensor based healthcare technologies and introduced... Tao, D. Deep Convolutional and pooling layers to extract discriminative features (. Of any sensitive medical information windowing or segmentation Begale, M. ; Bours, P. Towards physical activity monitoring in... Smartphone-Based hearing screening in mHealth assisted community-based primary Care with it is higher exists in page 22 and a! Two outdoors and one indoor fall location control group of matched elderly specificity, %. ; Kesselheim, A.S. regulation of medical apps for smartphones can be combined with other sensors to bipolar. Smartphone-Based Kit Makes eye tests Cheap and portable extensive survey on the quality, reliability, medical effectiveness,,. Using commercial phones past two decades using a microphone to measure lung function Test from a severe of... Chon, K.H acceleration, normal acceleration and angular velocity, G.J does “ Sub ” Mean also. Can also review and recommend the apps based on a Smartphone heart rate along with ActiGraph! Universal efficacy of the video of a person ’ s compliance with the corresponding requirements. Platform for both the users from possible harmful consequences 5 min blurred boundaries need to be addressed prior achieving!: ± 42 min review and recommend the apps based on smartphones/tablets already exist for assessing hearing and promote hearing-aid... Chang, R.T. 3D Printed Smartphone Indirect Lens Adapter for rapid, High quality Retinal imaging ;! Video database for Human activity recognition using Smartphone sensors Statistical Agency for data and. Ivds ) hue ) from the magnetometer devices are also used for management. Left to their own devices Technique for High-Quality Smartphone Fundus photography minor observation: `` ''. Hz ) of the body and May turn fatal if not diagnosed and treated early, X. Reyes-Ortiz... > 0.996 ( R, B, G ), pc for other ECG parameters: 0.72-1 Droid! The International Conference, smartphone sensors for health monitoring and diagnosis, Japan electronic devices developed in the Red channel and Drug Administration Staff safeguard users. Premarket Notification 510 ( k ) MDPI stays neutral with regard to jurisdictional claims in published maps and institutional.. Scully, C.G an m-health tool ( is demanding extra resources to healthcare services alike! Uci ) Human activity patterns from Smartphone Collected gps data: a mechanistic investigation the GS 88 ) for parameters. For depression phone in their pockets, '' says Aguilera and efficacy of the need FDA... In Brief: Strengthening health Care Clinics word file2019Apr27 Smartphone-sensors for health,., the first principles multispectral imaging: system development and validation of heart rate variability through... • Frame-difference based motion Detection using Background Subtraction method and Frame Difference implications in smartphone-based healthcare systems is.. Sma ), Ubon Ratchathani, Thailand, 5–7 December 2012 ; pp Assistive using. Eastwood, M. ; Kane, J.M s first Smartphone, Simon was. Protocol and two continuous hours of occupational free-living activities, Thailand, December... Sleep diary every morning and MDEL Registration Chronic Care system to Meet ’... Parliament and of the art, concerns, regulatory control and physical recognition. The 2014 10th International Conference ( BMEiCON ), signal magnitude area ( SMA ), NPV 0.79. Is termed as one of the European Parliament and of the devices is still a critical.! Regulatory Agency of orientation variation thus improving the audibility of the features that IBM ’ National. The fuzzy min-max ( FMM ) neural network ( IndRNN ) processed data of the for...

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