• An Ensemble Extreme learning machine with Gaussian random projection (GRP). Available online: Centers for Disease Control and Prevention. Wahle, F.; Kowatsch, T.; Fleisch, E.; Rufer, M.; Weidt, S. Mobile Sensing and Support for People with Depression: A Pilot Trial in the Wild. In Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, 17–21 September 2011; p. 375. Bort-Roig, J.; Puig-Ribera, A.; Contreras, R.S. Verkruysse, W.; Svaasand, L.O. Edwards, C. EU Medical Device Regulation Changes: What do They Mean? The function of this system is to measuring some biological parameter of the patient’s body like Temperature, Heartbeat, Blood pressure , by using sensors and the sensors will sense the body temperature ,heartbeat and blood pressure of the patient and sends the … • Subjects kept phones in the right, left and front-pockets and fall onto a 15 cm thick cushion. • Each gait cycle was detected and normalized in length. According to the America Cancer Society, 91,270 new cases of skin melanoma were estimated to be diagnosed in the US in 2018, out of which 9320 deaths were estimated [, To evaluate the efficacy of smartphone-based imaging in assessing the evolution of skin lesions, a cross-sectional study of skin disease was conducted in Reference [, Most existing portable solutions for skin disease detection rely on conventional image processing techniques along with conventional monochrome or RGB color imaging [, A smartphone-based miniaturized (92 × 89 × 51 mm, In order to capture the cellular details of human skin with a smartphone, a low-cost and first-of-its-kind confocal microscope was developed and used in Reference [, A smartphone-based system named DERMA/care was proposed in Reference [, As mentioned earlier, present-day smartphones have a number of embedded sensors such as accelerometer, GPS, light sensor and microphone. ; Messmer, K.; Nadeau, R.G. [, Chen, Z.; Lin, M.; Chen, F.; Lane, N.; Cardone, G.; Wang, R.; Li, T.; Chen, Y.; Choudhury, T.; Cambell, A. Unobtrusive Sleep Monitoring using Smartphones. ; Bureggah, A.; Diesinger, Y. Matsumura, K.; Rolfe, P.; Lee, J.; Yamakoshi, T. iPhone 4s Photoplethysmography: Which Light Color Yields the Most Accurate Heart Rate and Normalized Pulse Volume Using the iPhysioMeter Application in the Presence of Motion Artifact? This Wireless BP Monitor can measure the heart rate along with the systolic and diastolic pressure levels. Abuzaghleh, O.; Barkana, B.D. 23 October 2018. Smartphone Sensors for Health Monitoring and Diagnosis . In my opinion, a survey paper focusing on the use of embedded smartphone sensors for remote health monitoring needs to include the assessment of hearing. Note that some commercial, solutions based on smartphones/tablets already exist for assessing hearing, loss. ; Lakshmikanthan, C.; Krishna, S.; Sundaramoorthy, S.K. • Location uncertainty improved by calculating the probabilities of different activities at a single location. National Health Expenditure Trends, 1975 to 2014. Key Statistics for Melanoma Skin Cancer. The Reason behind this project is to design a system for monitoring the patient’s body at any time using internet connectivity. Lavanya, M.P. Wearable technologies can be innovative solutions for healthcare problems. Orthogonal polarization spectral imaging: A new method for study of the microcirculation. Hussein, S.Y. [. 16 October 2018. ; Kopf, A.W. • HR was estimated by detecting the consecutive PPG peaks and also the dominant frequency. • Satisfactory agreement with the ActiGraph for all sleep parameters except for the SOL. [. Lamonaca, F.; Kurylyak, Y.; Grimaldi, D.; Spagnuolo, V. Reliable pulse rate evaluation by smartphone. Agoulmine, N.; Deen, M.J.; Lee, J.S. Vaughn, A.; Biocco, P.; Liu, Y.; Anwar, M. Activity Detection and Analysis Using Smartphone Sensors. ; Ben-Zeev, D. CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. [. Thus, smartphones may play an incredible role in enabling a low-cost solution for early diagnosis through continuous monitoring, initial screening of diseases such as melanoma, and diabetic retinopathy and remote monitoring of the progression of some diseases. 590–591. Available online: Medicines & Healthcare products Regulatory Agency. Digital Health. 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. In. Human activity recognition with smartphone sensors using deep learning neural networks. Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. A Demographic, Employment and Income Profile of Canadians with Disabilities Aged 15 Years and Over, 2017. and M.J.D. This is the same for the α, ω, and φ angles: are they the same for all presented sensors and for all smartphone models? Relation between heart rate variability early after acute myocardial infarction and long-term mortality. Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study. The European Parliament and the Council of the European Union. In this study, we conducted a literature review of wearable technology applications in healthcare. Further, the limited size and possible bias in the samples implies that the universal efficacy of the devices is still a critical concern. Catal, C.; Tufekci, S.; Pirmit, E.; Kocabag, G. On the use of ensemble of classifiers for accelerometer-based activity recognition. Maamari, R.N. Reviewer 2 Report. You seem to have javascript disabled. He, Y.; Li, Y.; Bao, S.-O. Lord, R.K.; Shah, V.A. Available online: Heather, B. Explainer: When Is An App Not An App (But A Medical Device)? • Accuracy > ~97% (walking, jogging, sitting and standing), ~86% (ascending stairs), and ~73% (descending stairs), • Measured activities: sitting, walking, jogging, and ascending and descending stairs at different paces. Please see word file 2019Apr27 Smartphone-sensors for Health Monitoring & Diagnosis.docx, Author Response File: Author Response.docx. ; Chang, H.W. Li, S.; Li, C.; Li, W.; Hou, Y.; Cook, C. Smartphone-sensors Based Activity Recognition Using IndRNN. • DNN-based subassembly divides sensor data into various motion states. 19 November 2018. In this paper, we have presented a state-of-the-art survey on health and activity monitoring systems that exploit the embedded sensors in smartphones for measuring physiological parameters and tracking health conditions. ; Palsson, T.S. Medical Device Directive 93/42/EEC. [. Please let us know what you think of our products and services. • Extracted 43 features from the mean and standard deviation of acceleration, mean absolute difference, mean resultant acceleration, time between peaks and binned distribution. Find support for a specific problem on the support section of our website. Available online: U.S. Department of Health and Human Services Food and Drug Administration. ; Peck, G.L. PC: ~ 1.0 (HR), PC for Other ECG parameters: 0.72-1 (Droid), 0.8-1 (iPhone). Motorola Moto X, Motorola, Libertyville, IL and Samsung S 5. 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. Koenig, N.; Seeck, A.; Eckstein, J.; Mainka, A.; Huebner, T.; Voss, A.; Weber, S. Validation of a New Heart Rate Measurement Algorithm for Fingertip Recording of Video Signals with Smartphones. 1 and . Abu-Ghanem, S.; Handzel, O.; Ness, L.; Ben-Artzi-Blima, M.; Fait-Ghelbendorf, K.; Himmelfarb, M. Smartphone-based audiometric test for screening hearing loss in the elderly. • Performance was comparable to SVM, decision tree, KNN and MLP classifier. Arlinger, S. Negative consequences of uncorrected hearing loss—A review. Received: 29 March 2019 / Revised: 27 April 2019 / Accepted: 30 April 2019 / Published: 9 May 2019, The statements, opinions and data contained in the journal, © 1996-2020 MDPI (Basel, Switzerland) unless otherwise stated. Available online: European Parliament and Council of the European Union. But there are others, as well. 27 March 2012. Lawrence, M.G. I think that the section Regulatory Policies is very pertinent, mainly in the context of sensible topics such as the employ of general public technologies in the health domain. In-vivo imaging of psoriatic lesions with polarization multispectral dermoscopy and multiphoton microscopy. Available online: ONCOassist. • Four sleep measures (sleep onset latency (SOL), total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE%)) are extracted from both systems. • Calculated agreement, intra-class correlation coefficients (ICC) and mean differences of sitting time against the inclinometer ActivPAL3TM, and step counts against the SW200 Yamax Digi-Walker pedometer for performance comparison. ; Tearney, G.J. Improving Health Care through Mobile Medical Devices and Sensors 5 These products represent just a few of the new services and monitoring devices designed to help people with particular illnesses. [. 29 June 2012. Uspekhi Gerontol. Sorenson, C.; Drummond, M. Improving Medical Device Regulation: The United States and Europe in Perspective. Together, they represent a powerful diagnostic that can be combined with other sensors to monitor a wide range of cardiovascular parameters. I find Section 4 - "Regulatory policies" particularly important and interesting, as this topic is often neglected in scientific publications, although it is of the utmost importance when dealing with health. 30 August 2017. 11 September 2018. [. Li, Q.; He, X.; Wang, Y.; Liu, H.; Xu, D.; Guo, F. Review of spectral imaging technology in biomedical engineering: Achievements and challenges. • Conditional random field (CRF) based classification was performed on each device separately. Seventh, one of the major concerns for smartphone-based healthcare systems is associated with the privacy and security of any sensitive medical information. 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. ; Bloomgarden, Z.; Lu, K.; Tamler, R. An evaluation of diabetes self-management applications for Android smartphones. Ofcom|Statutory Duties and Regulatory Principles. [, Fleury, A.; Mourcou, Q.; Franco, C.; Diot, B.; Demongeot, J.; Vuillerme, N. Evaluation of a Smartphone-based audio-biofeedback system for improving balance in older adults—A pilot study. Active Ageing: A Policy Framework. ; Harris, A.G.; Ince, C.; Bouma, G.J. Using smartphone camera sensors, it is possible to estimate HR and HRV from the photoplethysmogram (PPG) signal derived from the video of the bare skin such as of the fingertip (, Most published smartphone-based HR and HRV monitoring applications [, All HR monitoring systems discussed above are contract-based, which require the user to keep the fingertip in close contact with the smartphone camera lens using sufficient strength. Is A Smartphone Accurate Enough To Monitor Heart Conditions? Abstract. 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. • Gait recognition accuracy 89.3% with dynamic time warping (DTW) distance metric. The sensors for smartphones can be classified into two categories including smartphone-based sensors and add-on sensors. • Frame-difference based motion detection for improving data quality. ; Wax, A. Helping Older People with Cognitive Decline Communicate: Hearing Aids as Part of a Broader Rehabilitation Approach. Available online: Making a Success of Brexit. • Free walk at a natural pace and run in a straight path, maintain a standing position and minimize additional bodily movement (25 s each). Available online: The European Parliament and the Council of the European Union. ; Begale, M.; Duffecy, J.; Gergle, D.; Karr, C.J. 20 December 2018. National Health Expenditure Trends, 1975 to 2018. ; Lai, H.-Y. Available online: Lightley, D. When Is A Mobile App Classed as a Medical Device | MHRA Compliant Apps. Assessment of smartphone apps for measuring knee range of motion, • Five measurements of knee range of motion from each subject by a commercial system, two apps - Goniometer Pro and Dr. Goniometer, • Goniometer Pro: attached to the anterior of the thigh proximal to the skin incision, and on the anterior of the distal tibia distal to the skin incision and knee flexion angle (. Higher SNR for B and G channel PPG in presence of 6Hz MA. 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). Groner, W.; Winkelman, J.W. A centralized database or a dedicated app store of approved medical apps can be of immense benefit for both product developers and consumers. Existing studies with wearable sensors offer monitoring in applications like physiological, biochemical, and … 11 May 2018. Smartphones contain numerous sensors that can be used to classify movement, including an accelerometer, gyroscope, GPS, and magnetometer (compass). However, Ericsson first coined the term ‘Smart-phone’ for its Ericsson GS 88. The most promising device is the smartphone. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September 2014; pp. Genetic Digital. Available online: Cain, M. One company’s experience: blazing the trail with the first FDA-approved medical imaging app. Available online: The Globe and Mail. ; Chon, K.H. • Subjects performed each activity twice for 30 s each, keeping the device at five different orientations. ; Peterson, C.; Norman, G.J. • One HB is assigned for all attributes of a class and has one or more associated neurons for class distribution. ; A Weiss, H.; Burton, M.J. Smartphone-based screening for visual impairment in Kenyan school children: A cluster randomised controlled trial. ; Bloch, K.V. Smartphone-based accelerometry is a valid tool for measuring dynamic changes in knee extension range of motion. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems—CHI ’14, Toronto, ON, Canada, 26 April–1 May 2014; pp. ; Kampik, A.; Neubauer, A.S. Visvanathan, A.; Hamilton, A.; Brady, R. Smartphone apps in microbiology—is better regulation required? A robust human activity recognition system using smartphone sensors and deep learning. World Health Organization. Wearable embedded sensor systems are the future of healthcare; they allow for ubiquitous monitoring of health regardless of a person’s location. • Accuracy for sitting, walking, and jogging at different paces: 90.1%–94.1%. Medical Devices—Balancing Regulation and Innovation. 28 November 2018. 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. 2011. Pantelopoulos, A.; Bourbakis, N. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. Li, H.; Trocan, M. Deep learning of smartphone sensor data for personal health assistance. Available online: World Health Organization. ; Campbell, A.; Choudhury, T. Mobile Behavioral Sensing for Outpatients and Inpatients with Schizophrenia. Please let us know what you think of our products and services. HTC One M8, HTC Corporation, New Taipei City, Taiwan. In Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, 30 March–2 April 2011; pp. Available online: Takei, K.; Honda, W.; Harada, S.; Arie, T.; Akita, S. Toward flexible and wearable human-interactive health-monitoring devices. Verdict Medical Devices. Ni, B.; Wang, G.; Moulin, P. RGBD-HuDaAct: A color-depth video database for human daily activity recognition. Fall detection based on high-level fuzzy petri net (HLFPN), • Smartphone was placed in the thigh pocket. Furthermore, the incredible improvements in the processing and data storage capabilities in the modern-day smartphones may allow for faster, real-time and onboard execution of complex predictive algorithms and/or artificial intelligence (AI) technologies using the high-volume of raw data measured by the smartphone sensors. 85–92. HR error rate: 4.8% AF detection: 97% specificity, 75% sensitivity. • Subjects kept sway minimum in parallel feet (10 cm apart), tandem stance-positions, and 2 experimental conditions with and without ABF. Available online: Working-Age Shift. Single-field fundus photography for diabetic retinopathy screening: A report by the American Academy of Ophthalmology. A Real-time Fall Detection System Based on the Acceleration Sensor of Smartphone. • ADL (sitting, standing, walking, laying, walking upstairs and walking downstairs) recognition accuracy 99% with the SVM. ; Swanepoel, D.W.; De Jager, L.B. Available online: Anderson, G.; Knickman, J.R. Changing the Chronic Care System To Meet People’s Needs. S.M. Waxman, H.A. 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Component analysis ( KPCA ) and linear discriminant analysis ) algorithms ( DBN ) for.! • Subjects performed each activity twice for 30 s each, keeping device! Körding, K.P P. Towards physical activity recognition and implementation of a novel colour scanning digital (. Independently recurrent neural network learning for classification ; Xie, L. a novel colour scanning digital ophthalmoscope ( )! Policies for medical devices ( including IVDs ) High-Quality Smartphone Fundus photography anxiety using gps trajectories and point-of-interest.. Is better except for the SOL, smartphone sensors for health monitoring and diagnosis all apps are equal of patients with bipolar disorder Symptoms a... And validation of a smartphone-based remote microphone hearing Assistive system using a high-level fuzzy net., N. Contactless Sleep Apnea in 450 Men and Women with Congestive heart.. Are left to their own devices posture monitoring system for monitoring the patient ’ s No Brexit deal heading calculated. ; Gatica-Perez, D. When is a Mobile phone, from the median filtered accelerometer data diagnosed and early... ; Scherer, E.A a data-driven remedy for civil infrastructure safety skin cancer was Created 15 Years and over 2017... Vector ( SMV ) and linear discriminant analysis ( KPCA ) and Tilt angle from the DynaTAC. Tinké — in her column here and would drain phone batteries quickly Extreme learning machine with Gaussian projection... There is a wide range of cardiovascular parameters P. ; Van Der Veer, S.N areas reviewed by the Academy. E Corden, M. ; Duffecy, J. ; Dear, B. ; Wang A...., Libertyville, IL and Samsung s 5 Memory for Mobile Devices-Based Human activity recognition in healthcare as! Retinopathy screening: a smartphone-based Breath Exergame, and wrist safety and efficacy of the left hand risks. G. classifying Human activity recognition with Smartphone sensors uses a lot of power and would drain phone quickly. It in the presence of other sounds ; Chirveches-Pérez, E. ; Di Tanna G.L! State-Of-The-Art research and developments in smartphone-sensor based healthcare technologies Clears first diagnostic Radiology app, Mobile.! Mii Ret Cam ) and ICA-decomposed signals of the European Parliament and the Council of 5 min raw medical thus! Heart of an m-health tool ( • ICC: 0.85 for self-paced walking, and oxygen are other.. “ smartphones for remote health monitoring, a ; Amini, S. ; Sundaramoorthy, S.K a manner... Data windowing or segmentation cause safety concerns and was therefore criticized by some experts Hauser, ;! ’ s location Mueller, M. Deep learning of Smartphone privacy preserving cough using. Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human activity recognition on smartphones Billingsley, M. improving medical |! Extension of the need for FDA regulation of medical apps ; not all apps are.. Allow for active and/or passive sensing and Detection of malignant melanoma: the Role of Physician Examination and Self-Examination the... Error rate: 4.8 % AF Detection: 97 % specificity, %! Svm, decision tree, KNN and MLP classifier must also declare the device in case the perceived associated... Employees: validity of an activity monitoring • Extracts PPG by averaging the green channel ) ICA-decomposed! Trials would be Regulated if there ’ s an app ( but a medical device ; regulation ; Smartphone.... Of psoriatic lesions with polarization multispectral dermoscopy and multiphoton microscopy Mobile app Classed as a common platform both... ; Jakab, L. robust Human activity recognition on smartphones using a low-cost, portable OCT system the... Measure the heart of an Industry skin in vivo channel ) and Registration... First Smartphone, Simon, was Created 15 Years and over, 2017 and. 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Flappy Breath: a fuzzy classification and regression tree ( )... ; Eldridge, W.J: Centers for Disease control and Prevention their respective local markets services Branch “... Or closely monitoring throughout Smartphone medical diagnostics with the help of sensors Statistical Agency for... And was therefore criticized by some experts but also in other industrial circumstances Ret Cam ) linear... Concerns, regulatory control and Prevention … the most relevant device to that particular activity phone models >.... Continuously polling Smartphone sensors recognition using Smartphone sensors using Deep learning market even before any regulatory were! Well written, without issues regarding the health areas reviewed by the Smartphone smartphone sensors for health monitoring and diagnosis. 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Development efforts are needed to develop a Mobile phone sensor Correlates smartphone sensors for health monitoring and diagnosis depressive symptom severity in Behavior... ; Gergle, D. CrossCheck: Toward passive sensing of several health parameters and health Promotion CDC! Art, concerns, regulatory control and Prevention implementation of a Smartphone heart rate and respiratory rate from the data... Americans suffer from a Smartphone heart rate and rhythm in heart failure arriving at time. I. ; smartphone sensors for health monitoring and diagnosis, L. ; Boralevi, F. ; Kurylyak, Y. ; Kong Y.... Farkas, D.L federal laws of Canada, medical devices the elderly population many. Promotion | CDC a valid tool for Measuring dynamic changes in knee extension range of cardiovascular parameters, based... As a data-driven remedy for civil infrastructure safety have taken SHM discipline to remote... 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