Medical Internet of Things and Big Data in

Medical Internet of Things and Big Data in Healthcare: Telemonitoring, Doctor 2. 0, Treatment 2. 0, Product 2. 0, health selfie Prof. Dr. Halit Hami ÖZ

Io. T in healthcare �. By 2020, 40% of Io. T-related technology will be health-related, more than any other category, making up a $117 billion market [2]. �The convergence of medicine and information technologies, such as medical informatics, will transform healthcare as we know it, curbing costs, reducing inefficien-cies, and saving lives.

Tele doctor

Tele consultation! The U. K.

Tele consultation! The USA

GP at hand in Turkey (Turkey Doctor Service)

E-visit!

Michael Dibernardo with the device that monitors his heart condition and then sends the data to…

GP at hand (Virtual Doctor!) �Smartphone GP app service will divert funding from most needy, warns practice �BMJ 2018; 360 doi: https: //doi. org/10. 1136/b mj. k 1045 (Published 05 March 2018) �GP at Hand, a partnership between the Lillie Road Medical Centre in west London and the technology company Babylon, launched late last year and offers a mixture of “virtual” GP consultations by smartphone and face-toface appointments. 1

GP at Hand �A spokesman for GP at Hand, responding to the statement, said that its service had “cut the average waiting time to under two hours, offering access to a doctor 24 hours a day, seven days a week, 365 days a year—as the 25 000 Londoners who have signed up have found. ”

GP at HAND �Noble said he hoped that GP at Hand’s “over 8700 five star ratings for digital appointments” would reassure Nightingale Practice that “we are doing something right. ”

Other Virtual Doctor services!

Cloud Assisted Io. T enabled framework for healthmonitoring

Figure 1 illustrates how this revolution in medicine will look in a typical Io. T hospital, in practice.

�A patient with diabetes will have an ID card that, when scanned, links to a secure cloud which stores their electronic health record vitals and lab results, medical and prescription histories. �Physicians and nurses can easily access this record on a tablet or desktop computer.

� One of the major challenges to implementing the Io. T has to do with communication; although many devices now have sensors to collect data, they often talk with the server in their own language. � Manufacturers each have their own pro-prietary protocols, which means sensors by different makers can’t necessarily speak with each other. � This fragmented software environment, coupled with privacy concerns and the bureaucratic tendency to hoard all collected information, frequently maroons valuable info on data islands, undermin-ing the whole idea of the Io. T.

�The Pharma Io. T concept involves digitalization of medical products and related care processes using smart connected medical devices and IT services (web, mobile, apps, etc. ) during drug development, clinical trials and patient care. The outcomes of Pharma Io. T in development and clinical trials can employ combinations of advanced technologies and services to create totally new kinds of disease treatment possibilities (e. g. , Treatment 2. 0).

�In patient care, Pharma Io. T will enable patients and health-care professionals to use medicines with advanced sensor hardware, and craft personalized care services and processes (Product 2. 0). Good examples of the Pharma Io. T solutions are the connected sensor wearables for Parkinson’s disease and multiple sclerosis patients, which provide medication management, improving the patient outcomes and the qual-ity of life

�The Myo, originally a motion controller for games, is now being used in orthopedics for patients who need to exercise after a fracture. With the aid of the Myo, patients can monitor their progress and doctors can measure the angle of movement. � �The Zio Patch measures heart rate and electrocardiogram (ECG) and is the US Food and Drug Administration ap-proved [12].

�Glaxo recently announced that it is investing in electro-ceuticals, bioelectrical drugs that work by micro-stimulation of nerves [13]. � �J&J has teamed up with Google to develop robotic surgery. In addition, they are collaborating with Philips on wearable devices such as blood pressure monitors [14]. �Novartis is working with Google (again) on sensor technologies, such as the smart lens, and a wearable device to measure blood glucose levels [15].

�A typical situation might involve an elderly person, recovering from a medical condition at home, linked to a combination of several connected services streaming data towards different parties, such as family members, tele-carer and physi-cians (Figure 2).

Figure 2

� The Centers for Medicare & Medicaid Services (CMS) have vast stores of billing data that can be mined to promote high value care; the same is true of private health insurers. � And hospitals have attempted to reduce readmission rates by targeting patients where predictive artificial intelligence (AI) algorithms indicate people who may be at highest risk based on an analysis of available data collected from existing pa-tient records (Figure 3). �

Figure 3

Fig. 1. Four-Layers So. A in Io. T enabled PHS (CDSS— ; clinical decision support system �RFID—Radio-frequency identification; GPS—Global Positioning System; �ECG—Electrocardiogram).

. Physical activity platforms and applications �MSP (Mobile Sensing Platform) [116] designed a lightweight wearable device placed on the waist to recognise a variety of physical activities and ADLs through connecting to the mobile phone.

Physical activity platforms and applications �WISDM (Wireless Sensor Data Mining) [117] is a typical platform that detects human physical activity based on Android phone sensors placed in one’s pocket. Data is from the accelerometer, features are extracted according to the identification of time between signal peaks, and activities of walking, jogging, ascending stairs, descending stairs, sitting and standing are selected in this work due to their repetitive

Physical activity platforms and applications �m. Health. Droid (Mobile Health Android) [118] is an open source framework designed to facilitate the rapid and easy development of biomedical android application which is available on Google Play [119]. �The platform is able to collect data from connecting heterogeneous commercial devices (e. g. , smart watch, belt and mobile device) for both ambulation and biomedical signals.

Physical activity platforms and applications �Wear. IT@Work [122] is an European project to investigate wearable computing technology in different areas. In healthcare, it studied gesture determination, including open/close hood, doors and trunk, checking steering wheel, etc. to assist doctors’ diagnosis [102]. Multiple small and cost-effective acceleration sensors are distributed on patient’s arms for gesture classification

4. 2. Healthcare service with human interaction (Emergency Monitoring and Prevention) [124] targets on emergency medical services (EMS) system to assist elderly living independently through automatic detection of ADLs in an Io. T environment. � EMERGE � Data from wrist devices with embedded-in wearable sensors and ambient sensors at home were collected for activity detection as well as vital data measurement. � The proposed framework made attempt to classify different types of activities such as short-term emergencies (e. g. , fall, helplessness) and long-term clinical assessment (e. g. , toilet usage, sleep) with the knowledge-based approach, and highlighted weight as a characteristic to carry out the fuzzy reasoning

4. 2. Healthcare service with human interaction � MOSKUS (Mobile Musculoskeletal User Selfmanagement) [1, 125] is a to develop a smart ICT solution to support self-management for patients suffering from arthritis, a prevalent and debilitating chronic disease, and thus, saving costs in the health care sector and improving the clinical outcome. � A personalised chronic patient’s self- management system (CPSMS) proposed in MOSKUS is a knowledge-based decision support and evidential reasoning system that makes use of a set of reasoning rules, providing nonpharmacological treatment plans to assist patients keep better control on the chronic disease and reduce the frequency of hospital visits.

4. 2. Healthcare service with human interaction � SMART (Self-Management supported by Assistive, Rehabilitation and Telecare Technologies) [126] is a personalised selfmanagement and monitoring platform for some health conditions namely chronic heart failure, chronic pain and stroke using wearable sensing technologies. � The aim of the project is to assist patients maintain their health condition at home through setting life goals based on a number of physical activity tracking outcomes, also to provide a series of feedbacks according to the process of therapy plan.

4. 2. Healthcare service with human interaction �PIA (Personal IADL Assistant) [70] is an AAL JP (Ambient Assisted Living Joint Programme) project aiming at assisting elderly people to live independently in their homes and perform daily activities without external help. �It uses simple approach that the elderly people can watch instructional videos of how to use modern household equipment through interacting with Near Field Communication (NFC) tags attached on the equipment with their smart devices

Fig. 1. Components of a remote patient monitoring system that is based on an Io. T-Cloud architecture.

�Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2. 0

Figure 1. The accelerated growth of Internet-connected devices [7].

�]. Similarly, the Connected Life initiative sponsored by the GSMA (GSM Association, an industry-association for worldwide mobile operators) found that in 2011, there were 9 billion total Internet-connected devices (compared to the total human population of less than 7 billion), two-thirds (6 billion) of which were mobile, and estimates that in 2020, there will be 24 billion total Internetconnected devices, 12 billion mobile [8]. Moreover, these 24 billion Internet-connected devices are estimated to have an economic impact of over $4. 5 trillion in 2020 [8].

1. 2. The Internet of Things Ecosystem �The IOT is connecting real-world objects to the Internet with tiny sensors. A number of functional layers are starting to be visible in the developing ecosystem as pictured in Figure 2, starting with data generation, and moving to information creation, and then meaning-making and action-taking. The broad brush categories are the Hardware Sensor Platform layer, the Software Processing layer, the human-readable Information Visualization layer, and the humanusable Action-Taking layer.

Figure 2. Processes and Layers in the Internet of Things Ecosystem.

2. Hardware Sensor Platforms � These sensors are included in a variety of devices and solutions. The trend is moving towards multi-sensor platforms that incorporate several sensing elements. � For example, the standard for the next- generation of personalized self-tracking products appears to be some mix of an accelerometer, GSR sensor, temperature sensor, and possibly heart rate sensor (from which heart rate variability may be calculated). � Some recognized first-generation quantified tracking devices and applications include the Fitbit, my. Zeo, Body. Media, Map. My. Run, Run. Keeper, Mood. Panda, Nike Fuelband, The

2. Hardware Sensor Platforms �Luminosity’s Brain Trainer, and the Neuro. Sky and Emotiv braincomputer interfaces (BCI). �One website listing of quantified tracking devices is maintained by the Quantified Self community (http: //quantifiedself. com/guide).

2. 1. Smartwatches and Wristband Sensors

2. 1. Smartwatches and Wristband Sensors � Current examples continue to feature accelerometry and include � the Nike Fuelband ($149, monitoring steps taken), � the Jawbone UP wristband i. Phone app ($99, � tracking steps taken, distance, calories burned, pace, intensity level, active versus inactive time, and GPS), � the Adidas Mi. Coach ($70, providing heart rate monitoring, real time digital coaching, interactive training, and post-workout analysis of pace, distance, and stride rate).

2. 1. Smartwatches and Wristband Sensors � Three next-generation products add new functionality to the standard metrics of total steps taken, distance, and calories. � The Mio Active ($119, http: //www. mioglobal. com/) adds heart rate, either with or without a chest strap. � The Lark. Life ($149, http: //www. lark. com/) identifies type of activity, allows single-button press diet tracking, measures sleep, and uses the combined metrics to make personalized recommendations about changes a user can make to feel better [10].

2. 1. Smartwatches and Wristband Sensors � The Amiigo ($119, http: //www. amiigo. co/) wristband shoe clip also measure the type of exercise, plus body temperature and blood oxygen levels through an infrared sensor [11]. � Other sensor platforms also focus on fitness and athletic training, for example Somaxis (www. somaxis. com) with ECG and EMG muscle and heart sensors and � Golf. Sense ($130, http: //www. golfsense. me/) where users attach a wrist-based sensor unit to a golf glove. The unit has two accelerometers and other sensors that collect and transmit data wirelessly for real-time feedback. � Multi-sensor wristband devices are also in development for clinical use, for example in epilepsy. � One team created a wristband to detect convulsive seizures through electrodermal activity and accelerometry, a useful improvement over labbased EEG methods as the device can be worn continuously [12].

2. 2. Wearable Sensors and Monitoring Patches �It is estimated that 80 million wearable sensors will be in use for healthrelated applications by 2017, [13].

2. 2. Wearable Sensors and Monitoring Patches � One of the most exciting potential developments in wearable patches is Sano Intelligence’s continuous blood chemistry monitoring patches, characterized as a $1 API for the bloodstream, � The disposable patch (one-week use) has been demonstrated to measure blood glucose and potassium levels, and aims to measure a full metabolic panel, including kidney function and electrolyte balance. � Further, there are enough probes on the wireless, battery-powered chip to continuously test up to a hundred different samples [14].

2. 2. Wearable Sensors and Monitoring Patches

2. 2. Wearable Sensors and Monitoring Patches �A promising concept pioneered by mc 10 is stretchable electronic tattoos for the continuous monitoring of vital signs with flexible electronics patches as shown in Figure 5. �These stretchable electronics track and wirelessly transmit information such as heart rate, brain activity, body temperature, and hydration level, available to athletes in the fall of 2012 [15].

2. 2. Wearable Sensors and Monitoring Patches � Wearable sensor patches are also useful for heart monitoring and have again allowed an improvement over current methods. � The Zio Patch from i. Rhythm (two-week use) can be worn to monitor cardiac rhythm and warn of arrhythmias (Figure 5). � Another interesting example of new patch technology is a continuous blood pressure monitoring patch from Sense A/S. � Instead of the cumbersome pressure cuff, there is a small arm patch with electrodes that sense the changing impedance of tissue around a vessel and convert it into a blood pressure reading via a waistband sensor unit [16].

2. 2. Wearable Sensors and Monitoring Patches � One of the classic use cases for wearable patches is the continuous glucose monitor (CGM) worn by diabetics and other self-trackers. � New developments mean that the current state-of -the-art technology is available in several CGM solutions where an under-the-skin continuous glucose monitor uses a sensor and transmits glucose readings every 1– 5 minutes to an external receiver or insulin pump [17]. �

2. 2. Wearable Sensors and Monitoring Patches � Also promising is the idea of using the glucometer as a platform. � Chemists have developed a method to bind short segments of DNA to a large number of potential molecules that might be present in blood, water, or food. � The DNA segments also bind to the enzyme invertase so that glucose is produced if the target molecules are present, and could then be read easily with a $20 drugstore glucometer. � So far, this glucometer-as-a-platform method has been used to detect cocaine, interferon, adenosine, and uranium [18, 19].

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0 � The continuous remote monitoring of patients is a significant market here, estimated to be $21 billion in 2016 as compared with $9 billion in 2011 [20]. � One example of a multi-sensor remote monitoring platform is the FDA-cleared Body. Guardian from Preventice which integrates ECG, heart rate, respiration rate, and physical activity data. � Another example of continuous monitoring wearable sensors is the FDA-cleared Visi Mobile from Sotera Wireless (Figure 6) which continually monitors vital signs such as ECG, heart rate, respiration, and temperature.

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0 � Another example of the now-expected continuous monitoring and automated data transmission and feedback functionality is available in the Aga. Matrix i. BGStar blood glucose monitoring system. � This was the first traditional glucometer to connect directly to an i. Phone app (Figure 6), to allow transitory readings to be recorded, stored into longitudinal profiles, and shared. � The Proteus digital health system is another example of the now-expected continuous transmission functionality, effectively defining a new category of medical device. � Here, a biodegradable ingestible sensor is attached to a pill that transmits data regarding the body’s interaction with the medication to a wearable patch (Figure 6).

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0 � Blood testing is another area where sensor technology and other innovations are speeding progress. � A key advance in user-friendly direct-to-consumer blood testing is dried blood spot technology. � Instead of going to a lab, consumers can prick their fingers at home with a lancet, put a series of blood spots on a laboratory card, mail in the card for analysis, and view the results on the web (Figure 6). � One company offering dried blood spot testing is ZRT Laboratory (http: //w 3. zrtlab. com/), however given the lack of available alternatives like the announced $1 Sano Intelligence API-for-the-bloodstream patches, pricing is still commensurate with lab-drawn blood tests.

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0 �. This could change quickly as new market entrants have their eye on the $65 billion lab testing market (where the direct-toconsumer segment is growing 15%– 20% per year [21]). �One recently-launched consumer proteomics startup, Talking 20 (referring to the 20 amino acids that make up the proteins in the body, www. Talking 20. com), is offering dried blood spot testing at a significant discount, $10 per card, testing five markers, vitamins B 1 and B 9, and hormones: testosterone, estradiol, and progesterone

2. 3. Continuous IOT Monitoring and Advances in Blood Testing 2. 0 �For clinical diagnosticians, there is a new point-of-care blood testing solution, the i. STAT System from Abbott Labs (http: //www. abbottpointofcare. com/). This is a handheld blood analyzer that provides real-time lab-quality results in minutes and measures 25 different blood markers including hemoglobin, hematocrit, glucose, potassium, calcium, p. H, urea nitrogen (BUN), creatinine, and lactate. Results can be used immediately onsite and transmitted to physicians for real-time consultation

i-STAT System from Abbott Labs

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping �In the coming era, it may be possible to have a much greater understanding of the brain. �There could be numerous useful applications from this, for example mental performance optimization techniques and a variety of emotion reading, mapping, and management programs.

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping � Some of the first-generation consumer EEG rigs are pictured in Figure 7 and include the � 14 -node EEG Emotiv ($299, http: //www. emotiv. com), � the single-node EEG Neuro. Sky ($99, http: //www. neurosky. com/), � and the sleep quality tracker my. Zeo, also essentially an EEG ($99, http: //www. myzeo. com/). � The Emotiv and the Neuro. Sky have been used for different applications such as improving attention and meditation, and video game performance.

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping �Another single-node EEG, the i. Brain from Neuro. Vigil, is available to academicians and claims to be better than current clinical EEG methods due to a clustering software algorithm, SPEARS (Sleep Parametric EEG Automated Recognition System), the company has developed for sleep analysis (http: //www. neurovigil. com/spears/). �

Figure 7. First- and Second-Generation Consumer EEGs: Emotiv, Neuro. Sky, and my. Zeo, and Intera. Xon and Axio.

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping � At least two companies have planned second- generation consumer EEG products as pictured in Figure 7, Intera. Xon (http: //www. interaxon. ca/) and Axio (http: //www. axioinc. com/). � Another company, Veritas Scientific (http: //www. veritasscientific. com/), has developed a lie-detection device, the Truth. Wave, based on consumer-available EEG technology. � A standard neuroscience technique is used to detect brain activity when a person’s face is recognized, registering the P 300 response from a type of brain activity known as event related potentials (ERPs).

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping � This is in some sense a digital implementation and extension of work by emotion research pioneer Paul Ekman (http: //www. paulekman. com/), who developed the FACS (Facial Action Coding System), now known as FACE (Facial Expression, Awareness, Compassion, Emotions), to taxonomize every human facial expression. � Affectiva (http: //www. affectiva. com/), � Affective Interfaces (http: //www. affectiveinterfaces. com/), � use computer webcams and eye-tracking technology to read facial microexpressions, mainly for the purpose of neuromarketing (e. g. ; determining the biophysical response of participants to consumer brands or entertainment products like TV shows or movie endings).

2. 4. Brain-Computer Interfaces (BCIs), Neuro-Sensing, and Emotion-Mapping �Some other interesting applications of eye-tracking, not directly related to emotion sensing, are from Cardiio (http: //www. cardiio. com/), who calculates heart rate from the camera on a mobile phone, and �Eye. Tribe (http: //theeyetribe. com/), who has created portable eye-tracking— software for controlling mobile devices with eye movements

2. 5. Smartphone and Smartphone Plus Peripheral Smartphones are also being used with external peripherals to be part of the IOT. Many devices have been attached to smartphones for novel applications as illustrated in Figure 8 such as � Alive. Cor’s electrocardiogram (ECG) recorder for heart monitoring (http: //alivecor. com/), � Mobi. Sante’s smartphone-based ultrasound imaging system (http: //www. mobisante. com/), and the Cell. Scope (http: //cellscope. com/). � � The Cell. Scope has a series of clip-on modules for the smartphone such as an otoscope (to look into the middle ear), and a dermascope (to capture magnified images of the skin). � Relatedly, there are many examples available on the Internet for how to add a lens to a smartphone camera to turn it into a microscope.

2. 5. Smartphone and Smartphone Plus Peripheral

2. 5. Smartphone and Smartphone Plus Peripheral

2. 5. Smartphone and Smartphone Plus Peripheral � the next-generation of sensortech could involve the direct integration of sensors into the smartphone platform instead of being an alongside hardware peripheral. � One example of this is the Life. Watch V, an Android-based smartphone with the usual suite of sensors to measure ECG, body fat, heart rate, stress, temperature, blood saturation, and blood glucose levels [28]. � The idea of sensors built directly into hardware is also exemplified in Google’s Project Glass, � Project Glass defines a new category of wearable computing with augmented reality glasses (Figure 8) where a small camera and computing node mounted on the corner of the glasses can search the Internet and display real-time results right in front of the eyes

2. 6. Environmental Monitoring and Home Automation Sensors �Sensordrone from Sensorcon (http: //www. sensorcon. com/), a successfully-funded Kickstarter project. �The keychain-based sensor monitors the environment and transmits data via Bluetooth to any connected device. �Applications are envisioned such as investigating air quality, carbon monoxide levels, potential gas leaks, and measuring a child’s temperature.

2. 6. Environmental Monitoring and Home Automation Sensors �Another project (supported by Cosm) is Flexibity Internet Sensors (http: //www. flexibity. com/), an open sensor toolkit and Internet-connected platform for wireless home and environmental monitoring. �Each sensor has a unique IPv 6 address and can be accessed with a standard web call or via web services like Twitter.

2. 6. Environmental Monitoring and Home Automation Sensors � Air Quality Egg (http: //airqualityegg. wikispaces. com/Air. Quality. Egg), measures the air quality in the immediate environment, and offers the now-expected social layer to users—the ability to share data with an on-line community in real-time. � In home automation, there is an active development community, one example of which is a Google-sponsored project, open. HAB (the open Home Automation Bus http: //code. google. com/p/openhab/). � The project attempts to provide a universal vendor-neutral platform for integrating multiple hardware devices, bus systems, and interface protocols. � A related solution is the Android appliance Smarty. Home (http: //www. mysmartyhome. com/), for home automation and music and sound system coordination.

2. 6. Environmental Monitoring and Home Automation Sensors

3. Software Processing and Data Transmission �For the data transmission portion of the process, any variety of standard communication protocols may be used including Wi-Fi, Bluetooth, ANT, Zig. Bee, USB, and 2 G, 3 G, and 4 G. �An important recent innovation is Bluetooth low-energy (BTLE) which allows mobile devices to send data more efficiently with much greater battery efficiency than traditional Bluetooth, essentially enabling the regular ongoing if not continuous transmission of relevant data.

3. Software Processing and Data Transmission �Cloud services vendors like AT&T and Qualcomm (with the 2 net Platform, an FDAlisted Class I Medical Device Data System) are developing services specifically for biosensor and health data collection and storage. �Salesforce. com’s Heroku and Microsoft’s Azure are other examples of cloud platformsas-a-service (data collection and analysis services) which run on either public or private clouds like Amazon Web Services, Joyent, and Rackspace.

3. Software Processing and Data Transmission

3. 1. DIY Hardware and Software Components (for Programmers) � 3. 1. 1. �Sensor Hardware Platforms, �Operating Systems, and �Software Processing and Development Environments

DIY sensor hardware platforms � Some examples of DIY sensor hardware platforms include the Arduino and Electric Imp circuit boards, and the Raspberry Pi singleboard computer to which Arduinos and sensors may be attached. � The Arduino and Electric Imp are single-board microcontrollers that come with basic software suites for programming them. � Some sensors are already attached to the boards and other standard sensors can be ‘plugand-played’ directly into boards, for example, temperature, sound, light, potentiometers, and moisture sensors [30].

Some References � Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2. 0 � Melanie Swan, J. Sens. Actuator Netw. 2012, 1, 217 -253; doi: 10. 3390/jsan 1030217 � Towards fog-driven Io. T e. Health: Promises and challenges of Io. T in medicine and healthcare, Bahar Farahani a, *, Farshad Firouzi b, Victor Chang c, Mustafa Badaroglu d, Nicholas Constant e, Kunal Mankodiya e, Future Generation Computer Systems 78 (2018) 659– 676 � Advanced internet of things for personalised healthcare systems: A survey, Jun Qi a, Po Yang a, *, Geyong Min b, Oliver Amft c, Feng Dong d, Lida Xu e, Pervasive and Mobile Computing 41 (2017) 132– 149 � IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 40, NO. 1, JANUARY 2010 � 1, A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis, Alexandros Pantelopoulos and Nikolaos G. Bourbakis � Yuehong YIN∗, Yan Zeng, Xing Chen, Yuanjie Fan, The internet of things in healthcare: An overview, Journal of Industrial Information Integration 1 (2016) 3– 13 � Amir M. Rahmani a, b, *, Tuan Nguyen Gia c, Behailu Negash c, Arman Anzanpour c, Iman Azimi c, Mingzhe Jiang c, Pasi Liljeberg c. Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach, , Future Generation Computer Systems 78 (2018) 641– 658 � Srivathsan Ma, Yogesh Arjun Kb, , Health Monitoring System by Prognotive Computing using Big Data Analytics, Procedia Computer Science 50 (2015) 602 – 609 � Amir-Mohammad Rahmani, Smart e-Health Gateway: Bringing Intelligence to Internet-of-Things Based Ubiquitous Healthcare Systems, 2015 12 th Annual IEEE Consumer Communications and Networking Conference (CCNC) �