Scholarly article on topic 'IoTFLiP: IoT-based Flip Learning Platform for Medical Education'

IoTFLiP: IoT-based Flip Learning Platform for Medical Education Academic research paper on "Computer and information sciences"

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{"Internet of things" / "Cloud environment" / "Flipped learning" / "Case-based learning" / "Medical education"}

Abstract of research paper on Computer and information sciences, author of scientific article — Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Asif Razzaq, Jawad Khan, Sungyoung Lee, et al.

Abstract Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things (IoTs) concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical cases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learing environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastructure is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students' and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students' academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

Academic research paper on topic "IoTFLiP: IoT-based Flip Learning Platform for Medical Education"

Author's Accepted Manuscript

IoTFLiP: IoT-based Flip Learning Platform for Medical Education

Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Asif Razzaq, Jawad Khan, Sungyoung Lee, Muhammad Idris, Mohammad Aazam, Taebong Choi, Soyeon Caren Han, Byeong Ho Kang

Digital

Communications and Networks

www.elseVer.com/locate/dcan

PII: S2352-8648(17)30097-4

DOI: http ://dx.doi. org/ 10.1016/j. dcan.2017.03.002

Reference: DCAN78

To appear in: Digital Communications and Networks

Received date: 19 December 2016 Revised date: 8 March 2017 Accepted date: 15 March 2017

Cite this article as: Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Asi Razzaq, Jawad Khan, Sungyoung Lee, Muhammad Idris, Mohammad Aazam Taebong Choi, Soyeon Caren Han and Byeong Ho Kang, IoTFLiP: IoT-basec Flip Learning Platform for Medical Education, Digital Communications an Networks, http://dx.doi.org/ 10.1016/j.dcan.2017.03.002

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IoTFLiP: IoT-based Flip Learning Platform for Medical Education

Maqbool Alia,e, Hafiz Syed Muhammad Bilala, Muhammad Asif Razzaqa, Jawad Khana, Sungyoung Leea *, Muhammad Idrisb, Mohammad Aazamc, Taebong Choid, Soyeon Caren Hane, Byeong Ho Kange

a Department of Computer Science and Engineering, Kyung Hee University, Yongin, 446-701, Republic of Korea. bIT4BI, Universit Libre de Bruxelles, Belgium. c Carleton University, Canada. d Samsung Thales, Defense Company as Program Manager for C4I system development, Republic of Korea. e School of Engineering and ICT, University of Tasmania, Hobart, Tasmania, 7005, Australia.

Abstract

Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which builds its foundation on persisted patient cases. Flip learning and Internet of Things (IoTs) concepts have gained much attention in the recent years. These concepts with CBL can improve learning capabilities by providing real and evolutionary medical cases. It also enables students to build confidence in decision making, and to enhance teamwork environment efficiently. This paper proposes an IoT-based Flip Learning Platform, called IoTFLiP, where IoT infrastructure is exploited to support Flipped case-based learning in cloud environment with state of the art security and privacy measures for the potential personalized medical data. It also provides the support for application delivery in private, public, and hybrid approaches. The proposed platform is the extension of our Interactive Case-Based Flip Learning Tool (ICBFLT) that is developed based on the current CBL practices. ICBFLT formulates the summaries of CBL cases through synergies of students' as well as medical experts' knowledge. Due to low cost and with reduced sensing devices' size, support of IoTs, and recent flip learning concepts can enhance medical students' academic and practical experiences. To demonstrate the working scenario of proposed IoTFLiP platform, a real-time data through IoTs gadgets is collected to generate a real-life situation case for a medical student using ICBFLT.

Keywords: Internet of things, cloud environment, flip learning, case-based learning, medical education

* Corresponding author

Email addresses: maqbool.ali@oslab.khu.ac.kr,maqbool.ali@utas.edu.au (Maqbool Ali), bilalrizvi@oslab.khu.ac.kr (Hafiz Syed Muhammad Bilal), asif@oslab.khu.ac.kr (Muhammad Asif Razzaq), jkhanbk1@yahoo.com (Jawad Khan), sylee@oslab.khu.ac.kr (Sungyoung Lee ), muhammad.idris@ulb.ac.be (Muhammad Idris), aazam@ieee.org (Mohammad Aazam), taebong.choi@samsung.com (Taebong Choi), soyeon.han@utas.edu.au (Soyeon Caren Han), byeong.kang@utas.edu.au (Byeong Ho Kang)

Preprint submitted to Digital Communications and Networks

March 16, 2017

1. Introduction

In medical education, various teaching methodologies have been applied. Among them, Case-Based Learning (CBL) is considered as an effective learning methodology for medical students [1, 2]. It is a shared learning approach for small-group of students to identify and solve the patient's problem [3]. In CBL, authentic cases are used for clinical practice [4] and facilitator's role is active [5] as compared to traditional learning. Moreover, CBL helps students to investigate the fact-based data and provides an opportunity to see theory in practice [6]. On the other side, in CBL, normally formal learning activities are performed, and students hesitate to actively participate due to lack of beforehand practice, clarification of problems, and knowledge. In recent trends, more attention is paid to the online learning environment [3] as well as the flip learning approach for boosting learning capabilities [7, 8]. Currently, the CBL is performed without exploiting the advantages of the flip learning methodology, which has significant evidence to prefer over traditional learning [8, 9]. As defined by Kopp [10], "Flip learning is a technique in which instructor delivers online instructions to students before and outside the class and guides them interactively to clarify the problems. While during class, instructor delivers effective knowledge in efficient manner". Regarding CBL with flip learning concepts, we have designed and developed an Interactive Case-Based Flip Learning Tool (ICBFLT) for medical education [11] to enable the medical students for beforehand CBL practice, which is designed and developed based on the current CBL practices in School of Medicine, University of Tasmania, Australia.

In order to support healthcare improvement, much work has been done to acquire information through IoT devices. However, there is still lack of systems and frameworks to efficiently exploit IoT data and use it for the purpose of extracting knowledge, creating knowledge with partial involvement of the field expert, and using the acquired knowledge for providing real-time patient care and treatment. For knowledge creation and acquisition, various learning models exist that need to be used for the real-time extraction of meaningful information from IoT devices and make it shareable among the caregivers, patients, and doctors/experts [12, 13]. Currently, the CBL lacks development mechanism of real-world clinical case using IoT infrastructure and there is need to exploit existing IoT resources and infrastructure for boosting medical education.

Keeping in view all aforementioned facts, our motivation was to design a platform that can be used for medical, as well as other domains for effective and enriched learning. In this paper, an effective platform, called IoT-based Flip Learning Platform (IoTFLiP), is proposed that integrates the features of existing IoT resources. The IoTFLiP exploits the IoT infrastructure to support CBL in the flipped environment. The ICBFLT lacks the support of acquiring real-life patient cases which can be achieved using IoT concepts. This platform supports flipped case-based learning in cloud environment with state of the art security and privacy measures for the potential personalized data and delivery of application in private, public, and hybrid approaches. As while designing any system, keeping the privacy of information, providing on-demand services, and knowledge sharing among organizations are considered as important parameters [8, 14].

The case-based flip-learning using IoTivity is an innovation approach that helps for an interactive medical education for learning real-world patient cases. We have already implemented its prototype and achieved a success rate more than 70% [11]. The proposed platform is designed for the first-year unit for School of Medicine, University of Tasmania, Australia and will be deployed in our following year.

The possible stakeholders and customers of the proposed platform are : (a) Health Staff, (b) Pharmaceutical companies, (c) Health Insurance companies, (d) Surgical Instrument Industry, (e) Sensor manufacturer, (f) Communication service provider, and (g) Government.

The rest of the paper is organized as follows; Section 2 discusses the state of the art closely related to the current research, whereas the IoTFLiP architecture is presented in Section 3. Section 4 provides an overview of ICBFLT. Section 5 discusses the working scenario for system workflow along-with the experimental setup and results. Section 6 concludes the paper with a summary of the research findings along-with future directions.

2. Related Works

In order to propose the IoT-based Flip Learning Platform, we conducted the literature review in multiple research domains, including IoT, CBL, and Flip learning. This section mainly covers how IoT technology was used in medical domain, and how CBL with flip environment was applied in medical education.

IoTs is no more new to human and it has gained much attention in the recent years [15]. According to the Gartner study1, 26 billion devices could be communicating with one another by 2020 with an estimated global economic value-add of 1.9 trillion. It has changed the concept of virtual world for communication, information exchange, availability, and ease of use. The concepts of device-to-device connectivity is described by IoTivity. In healthcare, IoTivity has been exploited from wellness applications [16] to treatment and patient care such as using sensors for monitoring and real-time status detection [12]. Apart from the wellness applications of IoT, it has been used for medical treatment, identification of diseases, complications, and prevention. IoTivity has been exploited to overcome the challenges of existing healthcare, hospital information and management systems [17, 18]. IoT offers greater promise in healthcare field especially to reduce the cost of care [19]. Due to low cost and with reduced sensing devices size, it can play an important role for boosting the learning capability of medical students by providing real and evolutionary medical cases. In current practices, multiple IoTs platforms exist with particular features. As health is the primary concern for society and has strong impact on all stakeholders: IoTs in healthcare domain not only improve healthcare in society but also is beneficial for macroeconomic conditions2.

CBL is one of the successful approaches in student-based pedagogy. Jones et al. [20] described that CBL arose from research that indicated that learners who commenced by tackling problems before attempting to understand underlying principles had equal or greater success that learners using a traditional approach. CBL is described as active learning that is focused around a clinical, community or scientific problem. Learning starts with a problem, query or question that the learner then attempts to solve. The learner attempts to solve a specific problem while acquiring knowledge on how to solve similar problems.

Because of this advantage, there are several researchers applied CBL in medical education. Fish et al. [21] states Samford University received a grant to apply CBL in undergraduate education. CBL was integrated into the some of the nursing courses. This was successful and as a result CBL was implemented across the entire curriculum. CBL was effectively used in adult health, mental health, pediatric and obstetrical nursing courses. CBL was also used effectively in non-clinical courses such as pathophysiology, statistics and research. Moreover, Students

1 Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units By 2020, http://www.gartner.com/newsroom/id/2636073

2Transforming Economic Growth With The Industrial Internet Of Things, http://www.forbes.com/sites/valleyvoices/2015/01/21/transforming-economic-growth-with-the-industrial-internet-of-things/

studying medicine at the University of Missouri that graduated from 1993 through to 1996 went through a traditional curriculum, whereas students graduating from 1996 through to 2006 went through a CBL curriculum [22]. As part of both curriculums students must pass a 'step 1' test in their third year of study before progressing on to their fourth year. They must complete a 'step 2' test in order to graduate. Since the introduction of the CBL curriculum, these scores have risen significantly and have remained significantly higher.

With the flipped learning environment, the effectiveness of CBL is surprisingly improved. The flipped classroom is a pedagogical framework in which the traditional lecture and assignment elements of a course are flipped or reversed [23]. Students can learn necessary knowledge before the class session, while in-class time is devoted to exercises and discussion by applying the knowledge. Ali et al. proposed the Interactive Case-based Flip Learning Tool [11] which covers formulation of CBL in the flipped learning environment. The evaluation result shows that the level of user satisfaction was quite high (70%).

Authors in [24] presented a resource management and pricing model for IoT through fog computing. The authors emphasized the usefulness and importance of customers' history while determining the amount of resources required for each type of service. However, it is not discussed that how their resource management can be mapped to flipped learning. Same is the case with another study of the authors presented in [25], where a smart gateway architecture is discussed. The authors proposed that several type of services require smart and real-time decision making, which can be performed by a middleware gateway. Our proposed work integrates the features of [24, 25] and is on the top of that works, providing an architecture of how IoT resources and infrastructure can be used for medical education. In addition to that, various other platforms and systems applied to acquire real-time data through IoT devices like Masimo Radical-7, Freescale Home Health Hub reference platform, Remote Patient Monitoring [19], IoT-enabled mobile e-learning platform [26], Remote Monitoring and Management Platform of Healthcare Information (RMMP-HI) [27]. They have been proposed or implemented in specific domains for particular applications without flip learning as well as CBL purpose for medical education. Regarding preferring flip learning in CBL over traditional learning practices, Gilboy et al. [8] showed that students preferred flip learning over traditional pedagogical strategies approach. Similarly, according to Street et al. [9], "The flipped classroom could be a useful and successful educational approach in medical curricula". With the technologies available today, students learn more through active interactions as compared to passively watching the teacher do everything. Lack of such features is one of the main motivations of our proposed flip-based learning for medical education.

3. IoT-based Flip Learning Platform (IoTFLiP)

This section describes the architecture of the proposed platform, called IoTFLiP, as shown in Fig. 1 and the functionalities of its layers. The IoTFLiP integrates the features of existing individual platforms and can be used for medical as well as other domains.

Figure 1 is composed of eight layers, which are abstractly divided into 2 blocks on the basis of communication and resources, called local and cloud processing blocks. The first four layers, namely Data Perception, Data Aggregation and Preprocessing, Local Security, and Access Technologies Layers deal communication and resources locally, while remaining four layers, namely Cloud Security, Presentation, Application and Service, and Business Layers deal at cloud level. These layers cover important features including data interoperability for handling data heterogeneity, smart gateway communication for reducing network traffic burden, fog computation for

y Storage Security \ (MD5, RSA) f User Profiling (Admin, Doctor, End User) ^yS

'-------A______y N ^____ S "—"

Access Technologies Layer

2G/3G/4G/LTE/LTE-A/Broadband/Wifi/WiBro/GPRS

Local Security Layer

Security Policing

SecurityTechniques Decision (Encryption/Decryption)

Storage Security (MD5, RSA)

Communication Security (WAP, WPA, TLS)

Data Aggregation and Preprocessing Layer

Data Aggregation

Interoperability J 1 Fog Computation

Smart Gateway J ñm® Storage VM VNetwork.

Data Preprocessing

Filtering

Transcoding

Data Perception Layer

Figure 1: IoT-based flip learning platform (IoTFLiP) architecture

resource management to avoid delay information sharing, multiple levels of storage and communication securities, error handling while transcoding, application delivery policies, and business policies. Moreover, these layers provide with state of the art security as well as privacy measures for the potential personalized data, and give support for application delivery in private, public, and hybrid approaches. The further details for each layer are given below.

3.1. Data Perception Layer

In this layer, the identification of devices is performed, where devices are used to monitor, track, and store patients' vital signs, statistics or medical information. The devices include

Google Gear3, Google Glass4, patient monitoring sensors, smart meters, wearable health monitoring sensors, video cameras, and smart phones.

3.2. Data Aggregation & Preprocessing Layer

This layer is divided into Data Aggregation and Data Preprocessing modules. The Data Aggregation module deals with heterogeneous data interoperability, load balancing, and smart data communication issues i.e. communicating only when required, by either storing the data locally, temporarily, or discarding it when not required. This data aggregation & preprocessing requires resources which are not available in relatively less rich sensor nodes and other perception layer devices. Therefore, fog is incorporated here. Fog computing is a small cloud that acts as an extended cloud to the edge of the network [24]. In order to perform the rich tasks and filtering the communication, which sensors and light IoTs are not capable of doing, smart gateways are used [25]. Similarly, Data Preprocessing module filters the irrelevant data for faster communication and then transcodes it by encoding, decoding, and translation.

3.3. Local Security Layer

Security is the degree of protection from unauthorized user and attacks. Security of patient information is the most ethical issue. Patient always remains cautious about sharing of his/her personal medical data with others. In order to secure the temporary storage and for fog to cloud communication, Local Security Layer is introduced. This layer deals with where security is required and which security technique is chosen. Also, security policies are defined in this layer, in which decision of operations, e.g. have to be encrypted or not, are made. In order to answer the where security is required, if the communication is local, temporary storages are used which require local security. Similarly, based on application requirement, it has been decided whether fast communication will be feasible or slow. For example, for the case of patient monitoring urgency, security may not be affordable. In that case, we need fast communication. For answering which security technique for storage or protocol for communication are chosen, it has been decided based on application requirement. For storage security, Message-Digest algorithm (MD5), Rivest-Shamir-Adleman algorithm (RSA), Digital-Signature-Algorithm (DSA), and so on, while for communication security, Wireless Application Protocol (WAP), Wi-Fi Protected Access (WPA), and Transport Layer Security (TLS) can be used.

3.4. Access Technologies Layer

Various access networks exist for communication with cloud resources like WiFi, WiBro, GPRS, LTE, etc. This layer selects the access technology based on the requirement and availability of services.

3.5. Cloud Security Layer

Once data moves from local processing blocks to cloud processing blocks, then security of data storage is an important aspect in order to secure it from various natures of cloud-users. Secured User profiling can also be an important fact. This layer deals with storage security and user profiling. Security techniques are chosen based on user profiling.

3https://store.google.com/product/samsung_gear_live

4https://en.wikipedia.org/wiki/Google_Glass

3.6. Presentation Layer

The main purpose of this layer is to deal with encoding, decoding, and error handling during data transformation. This layer converts data in proper understandable format e.g. ECG graph, pulse rate, angiography, prescription text, picture, video etc.

3.7. Application & Service Layer

In this layer, Application Delivery Policies are defined in terms of private, public or hybrid access. Based on the service scope, delivery policies are chosen. Also, services are categorized based on the requirements from ordinary user access to admin user access. For example, one service is categorized into two parts. One part is accessible to every one, while other part is restricted. The same categorization can be applicable for medical center administration and medical institutes.

3.8. Business Layer

This layer deals with the business policies and services packages in terms of free, or subscribed rates. The packages, offerings are according to the usage.

4. Interactive Case-Based Flip Learning Tool (ICBFLT)

This section describes the functionalities of ICBFLT as shown in Fig. 2.

(Admin/Expert/Student)

j Data J

No (i.e. Expert / Student) View Courses

No (i.e. Student) Select CBL Case to Solve

Manage CBL

J CBL 7

7 Cases /

Provide Feedback to Students

.Solved,

View Students Solutions

/ Students ~7

/ Solutions /

Evaluate Students Solution

----Feedback j*-

View Expert Feedback

View CBL Model Solution

Figure 2: ICBFLT flow chart

The ICBFLT is designed to formulate the summaries of CBL cases through intervention of students' as well as medical expertise [11]. In addition to that, it provides easy case-based learning services to medical students through virtual patient cases. There are three types of users that

interact with ICBFLT: Administrator, Expert, and Student as shown in Fig. 2. Using this tool, the Administrator manages the courses including course details, their modules, and all allotments. The Expert manages CBL cases and their model solutions, evaluates the students' solutions, and provides feedback to students. While, the Student formulates the case summaries (e.g. further history, examination, and investigations) to solve the CBL case, views other available solutions, and gets feedback from expert.

The output of this tool is courses' information, real-world cases, cases' summaries formulated by students and expert, assessments of students solutions, and expert's feedback.

5. Working Scenario

In this section, working scenario for case-base flip learning using IoTivity is described through steps as shown in Fig. 3. This scenario covers CBL case creation, case formulation, case evaluation, case feedback, and storing medical knowledge. In Fig. 3, the steps 1 to 5 belong to Data Perception, Data Aggregation & Preprocessing, Local Security, and Access Technologies layers of the IoTFLiP, while steps 6 to 10 belong to Cloud Security, Presentation, Application & Service, and Business layers of the IoTFLiP.

© Expert-Patient Dialogue ® Record Patient History

Analytical software

Patient History | Document

" - (?) Generate CBL Case

------------------------i Evaluate'and provide Feedback

©Record /©

„____Vital Signs IvmsI Visualize Vital

Patient

Virtual Network

Medical Students

Figure 3: Working scenario for case-based flip learning

In order to execute the scenario for generating realistic CBL case, we have prepared a patients' dataset, as illustrated in Table 1, with the help of medical expert and knowledge engineer. This dataset can be easily generated by available IoT gadgets, which are mentioned in Step-3. For patients dataset, over the period of one week, three times a day, data is prepared by considering the valid ranges and important facts from available online resources5,6,7. Expert built 10

5Categories for Blood Pressure Levels in Adults, http://www.nhlbi.nih.gov/health/health-topics/topics/hbp 6Heart rates in different circumstances, https://en.wikipedia.org/wiki/Heart_rate, http://www.medicalnewstoday.com/ articles/235710.php

7Blood Sugar Levels for Adults With Diabetes, http://www.webmd.com/diabetes/normal-blood-sugar-levels-chart-adults, http://www.diabetes.co.uk/diabetes_care/blood-sugar-level-ranges.html

Table 1: Patients' vital signs data

ID Age Gender Systolic BPa Diastolic BP GLb at Fasting GL at Random Heart Rate

1. 65 M 135 89 145 247 90

2. 57 F 130 87 110 160 95

3. 54 M 139 92 90 130 89

4. 16 M 136 85 85 120 79

5. 9 M 123 75 80 125 130

6. 35 F 125 84 90 125 80

7. 3 F 110 78 70 125 130

8. 35 M 110 78 85 115 63

9. 45 M 123 85 80 130 85

10. 43 M 127 85 130 180 84

a Blood Pressure, b Glucose Level

scenarios, in which one is shown as an example in this study. These scenarios were of primary level difficulty and related to general medicine domain.

The process of creating a real-life situation case for medical students is described through steps [28], as shown in Fig. 3, that are explained as follows.

Step-1:

Expert dialogues with patient to get the basic information of a patient, such as patient name, gender, age, etc. Patients' names are not revealed in the Table 1 but we collected that in order to distinguish the patients. The exact age and gender will be used in clustering them into a specific age and gender group.

Step-2:

During dialogue, experts note down the patient's history information, including review of symptoms, medication history, family history, which would make to understand the hereditary issue.

Step-3:

After advised by expert, patient uses the wearable devices to record his vital signs of blood pressure, glucose level, and heart rate. These vitals are helpful for patient's treatment and for disease diagnosis [27, 29]. To measure these vitals, multiple IoT gadgets are available that are illustrated in Table 2.

Table 2: IoT gadgets to collect vital signs Vital Sign Available Devices

1. Blood Glucose iHealth's Blood Glucose Monitor, iHealth Align, iBG Star, etc

~ Ar. iHealth Wireless Blood Pressure Monitors, Omron BP786, Microlife WatchBP home A, QardioArm

2. Blood Pressure

Blood Pressure Monitor, etc

3. Heart Rate LG gear watch, Wellograph, Polar V800, Mio LINK, Epson Pulse Watch, Spree Headband, etc

Step-4:

Once vital signs are collected, medical expert analyzes the patient's data by viewing through graphical interfaces that are shown in Fig. 4.

Figure 4: Weekly pattern of patient

Step-5:

With analysis and processing of this data, the medical expert deduce the vital signs information, which are one-week average values such as Systolic Blood Pressure = 135.38 mmHg and other vitals shown in Fig. 4.

Step-6:

Expert integrates patient's history and his vital signs to generate a new real-world CBL case as represented in Table 3.

Table 3: The example of a real-world CBL case

Case Outline

Mr. X, a 65 years old corporate sector person, came to a medical expert with a few complaints. On inquiring, he told that he is providing finance consultancy to the clients. He added that his office hours are 8:30 am to 6:00 pm. As his job is related to office work. He has no physical activities. He used to drink regularly and likes to eat fatty and oily food. According to him, he used to exhaust quite early from the last few weeks. He felt fatigued and breathlessness after even a small walk of 100 meters. He reported a problem of blurred vision along with weight-loss. He said that he has never been in such a problem before. He was on no medication. His physical information such as height was 183 cm and weight was 196 lbs. He had a family history for hypertension and hyperglycemia. The expert was worried about his health and alarmed him to be conscious towards his health. For observing vital signs, the expert suggested him to use wearable devices to register his blood pressure, glucose level, and heart-rate.

On Examination: Systolic Blood Pressure = 135.38 mmHg, Diastolic Blood Pressure = 89.33 mmHg, Heart Rate = 90.14 bpm, Glucose Level in fasting = 145.43 mg/dL, Glucose Level in random = 247.36 mg/dL

Step-7:

Medical students solve the new real-world created case by interpreting the patient's problems. They create a significant medical story within the context of his or her life and then submit their interpretations.

Step-8:

Expert reviews/evaluates their interpretations and provides the feedback to each student. Step-9:

The ICBFLT persists the students' interpretations along with tutor's opinion that will be helpful for computerized feedback in future [30, 31].

Step-10:

Students get the expert's feedback to improve their concepts towards better learning for evolving their knowledge.

6. Conclusions

Due to low cost and with reduced sensing devices size, support of IoTs for providing real and evolutionary medical case as well as support of recent flip learning concepts can enhance medical students' academic and practical experience. To exploit the IoT infrastructure to support flipped case-based learning in cloud environment, we have introduced a realistic IoT-based Flip Learning Platform, called IoTFLiP, where with state of the art security and privacy measures for the potential personalized medical data. It also provides the support for application delivery in private, public, and hybrid approaches. The proposed platform integrates the features of existing individual platforms and can be used for medical as well as other domains.

The IoTFLiP is a scalable and designed to absorb futuristic requirements of the system. Currently, it is designed for the first-year unit for School of Medicine, University of Tasmania, Australia and will be deployed in our following year.

Acknowledgment

This work was supported by the Industrial Core Technology Development Program (10049079 , Develop of mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) NRF-2014R1A2A2A01003914. This work was supported as part of the the Asian Office of Aerospace Research and Development (AOARD) grant FA2386-16-1-4045.

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