Artificial Intelligence Based Diabetes Management System

Volume 118 No. 20 2018, 2945-2952 ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Artificial Intelligence Based Diabetes Management System Sathyasri.B Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech, Avadi Alamathi Road, Avadi, Tamil Nadu, India, sathyasri@veltechengg.com Bhuvaneshwari.S, Deepika.M UG Scholar, Department of Electronics and Communication Engineering, Vel Tech, Avadi Alamathi Road, Avadi, Tamil Nadu, India, sbhuvi1996@gmail.com Abstract- This work presents a fresh ehealth stage incorporating humanoid machines to hold an emerging multidimensional attention method for the medicine of diabetes. In this manuscript donates a fresh e-health stage incorporating Raspberry pi ARM11 (BCM2837) processor to sustain an emerging multidimensional attention method for the medicine of diabetes. The design of the stage enlarges the Internet of Things (IOT) to a web-centric concept through using emerging web means to approach and restrain objects of the natural cover. This incorporates vessel networks, both of which includes a set of medicinal sensors correlated wirelessly to a humanoid machine related (via the Internet) to a web-centric disease management hub (DMH). This gives a set of services for mutually patients and their caregivers that carry the full range of the multidimensional attention method of diabetes. The display place s software architecture prototype enables the growth of several appliances without knowing low-level details of the stage. This is achieved through joining the approach interface and procedure of managing service applications through a coated method based on object virtualization and mechanized service release. A fully operating prototype is urbanized and its end-to end functionality and suitability are tested fruitfully through a clinician-led navigator study, given proof that both patients and caregivers are responsive to the initiation of the planned stage. Index terms: Disease Management Hub (DMH), Internet of Things (IOT), Transmitter (RF-TX), Receiver (RF-RX), Advanced RISC Machine [ARM11 Broad-Com (BCM2837) processor], Reduced Instruction Set Computer (RISC). I. Introduction Recent research on diabetes shows that it is increasing at an alarming rate. It also leads to many dangerous diseases such as Heart attacks, etc. It has been approximated that, over 422 million people have diabetes across the World. Among those countries, India ranks 3 rd Place. This is because it has poor facility of healthcare resources. As a result, there is an increasing demand on the number of healthcare resources in India, especially in rural places. Doctors were not be available 24*7 in those rural areas, so approaching towards technological advancements, Internet of Things[4] will lead to a best solution to provide a communication between doctor and patient at their places. It has been reported from various literature works, the model [1], [7] shows the possibility of using various sensors to measure various parameters in the body. It classifies all the wearable sensors that can give the exact result of the required parameters in the patient body. The method [3] implements robot as a personal device in monitoring a children continuously. The model [2] shows a robot is used as a personal assisting device to monitor diabetic children. The robot follows the child s regular activities and suggests certain advices to the child. The model [6] views about the ideas of storage of data, transferring of data. This model ensures various proposals towards advanced patient profile management system. The Efficient method of transferring of data [10] and communication can be done through Bluetooth, GSM. From the impacts of these author ideas, the proposed software model can be implemented in Government hospitals to avoid waiting for doctor consultation and standing in queues. This model benefits usage of wearable sensors to measure diabetes [8]. It also builds IOT as a major source of 2945

medium connecting doctor and patient through the separate server ID. This advanced model has included a secure data encryption model and supports for a high security to patient database. It can be used by multiple patients and it can be able to access multiple patients medical reports at the same time on the server side. This is due to the high capacity of storage specification. Thus it focuses on developing a smart healthcare centres approaching towards making Digital India. II.. Proposed Model Internet-of-Things (IOT)[5] is raising great demand and it is widely used in all over all fields of applications. Due to the high necessity in the health care domain, the proposed work implements IOT concept in the field of medicine [9]. The purpose of the work is to measure BP level and Blood Sugar level at regular interval and update it to the patient database and to the Doctor through Cloud Server. This work implements a new proposed model in the field of Tele-medicine for the purpose of checking the sugar level and pressure level for all diabetic patients in the hospitals. This model builds IOT based software architecture to establish e-health system, which has advanced patient profile management. The ARM11 (BCM2837) processor identifies each individual patient through a face recognition facility in the processor. This ensures that the patients profiles match the patient. Upon successful identification, the processor starts interacting with the Sensors and starts collecting the data from the sensors. If successful, the sensor values will be updated to the database at regular time intervals. The Processor compares the input values from the sensors with the preset threshold values. When it exceeds, it intimate with red coloured data values as well as to doctor through server. Then Processor will convey the suggestion from doctor through audio or visual message to the patients database and also via mail (or) SMS. The proposed model of e-health management system is shown in Fig.1 and Fig.2 below Fig.2 Schematic Diagram of RF-Receiver Section III. Required Specification The requirements were used to implement the software connecting the sensor details automatically to update to the patient medical database. It is the primary stage to take the patients face input for the security. The requirement covers a set of sensors which measures the accurate parameters of diabetic patients. DMH The DMH is called as the Disease Management Hub. It is a hardware kit which can be interfaced with software to provide a set of services between doctor and the patients. This DMH consists of a set of sensors depending on the patient necessity, which is connected to a processor. The proposed model shows a detailed overview of the software module of the patients database accurately. The purpose of the module is to provide an accurate transmission of medication results to the doctor through cloud server. In this prototype model, DMH has been implemented for the treatment of diabetes. A set of sensors namely BP sensor and Blood Glucose Sensor is connected to the ARM11 processor for efficient monitoring of diabetes. It also consists of a Camera for face recognition of various patients and to add their details. 3.1 ARM11 (BCM2837) Processor Fig.1 Schematic Diagram of RF-Transmitter Section Raspberry pi 3 is a single board computer and it supports Linux operating system. It runs at 1.2GHz. Its major connectivity is wireless LAN and Bluetooth which is used to support wireless connection of internet. It has Broadcom BCM2837 64 bit CPU, 1GB RAM, 4 USB ports, 40 pin extended GPIO, Full size HDMI, 4 pole stereo output and video port. It consists of two interface ports: Camera Interface port (CSI) and Display 2946

interface port (DSI). Fig.3 Shows the ARM11 (BCM 2837) hardware board. fully inflated to this pressure, no blood flow occurs through the artery. As the cuff is deflated below the systolic pressure, the reducing pressure exerted on the artery allows blood to flow through it and sets up a detectable vibration in the arterial wall. The Blood Pressure Sensor used is shown in Fig.5 below. Fig.3 ARM11 (BCM2837) [Raspberry Pi 3] 3.2 Camera The Camera used in this model has a specification of USB 2.0 2M and resolution of 1600*1200.This module were connected to PC or mobile for image or video applications. It is built up with PCB layout, image processing circuit, a lens and a CMOS sensor. In this model, the Camera is interfaced with the ARM11 processor and it is used to add the face of the patient to their profile. It captures the image of the patient and sends the result to processor. Thus it is able to differentiate each individual patient. The Camera used is shown in Fig.4 below. Fig.4 Camera 3.3 BP Sensor Blood Pressure & Pulse reading are shown on display with serial out for external projects of embedded circuit processing and display shows Systolic, Diastolic and pulse readings. Compact design fits over your wrist like a watch. Easy to use wrist style eliminates pumping. When the cuff is Fig.5 Blood Pressure Sensor 3.4 Blood Glucose Sensor A Glucose meter is a medical device for determining the approximate concentration of glucose in the blood. It can also be a strip of glucose paper dipped into a substance and measured to the glucose chart. It is a key element of home blood glucose monitoring (HBGM) by people with diabetes mellitus. A small drop of blood, obtained by pricking the skin with a lancet, is placed on a disposable test strip that the meter reads and uses to calculate the blood glucose level. The meter then displays the level in units of mg/dl or mmol/l. Blood glucose monitoring is a way of testing the concentration of glucose in the blood. Particularly important in diabetes management, a blood glucose test is typically performed by piercing the skin (typically, on the finger) to draw blood, then applying the blood to a chemically active disposable 'test-strip'. Different manufacturers use different technology, but most systems measure an electrical characteristic, and use this to determine the glucose level in the blood. The test is usually referred to as capillary blood glucose. A blood glucose meter is an electronic device for measuring the blood glucose level. A relatively small drop of blood is placed on a disposable test strip which interfaces with a digital meter. Within several seconds, the level of blood glucose will be shown on the digital display. Needing only a small drop of blood for the meter means that the time and effort required for testing is reduced and the compliance of diabetic people to their testing regimens is improved. Although the 2947

cost of using blood glucose meters seems high, it is believed to be a cost benefit relative to the avoided medical costs of the complications of diabetes. The Blood Glucose Sensor used is shown in Fig.6 below. face data to the database of the patient. Fig.7 shows the pattern flow of the patient start up and interaction with the module. It first captures an image and it leads to recognition of image and then search for the exact match of the image with the patient profile gallery. Thus it leads to accurate profile matching technique. The steps of the patient face recognition is shown in Fig.8 below Fig.6 IV. Blood Glucose Sensor Software Implementation The proposed software architecture model maintains an accurate report of patient reporting the sensor data to the database and also to their caregivers. It focuses to fill the gap between existing system and future advancements in health care domain. This architecture also leads to maintain multiple patient medical details by using Raspberry pi processor. It can be used to maintain a large set of data in their memory. The Electronic/Mobile health management is becoming increasingly more popular because of its latest software protocol which is more beneficial in the health management. Fig.7 Flow Architecture of face recognition module 4.1 Face Recognition Module Face recognition is the process of identifying a person and compares with the best match for further process. It is important in image processing, security and surveillance, biometric, etc. First the image of the patient is captured using a camera which has an image processing circuit [2]. The Camera which is interfaced to the Raspberry Pi board captures the patients image and it is sent to processor. Based on the image acquired, it detects the face and matches profile of the respective patient with the existing face gallery. If the patients details exist it will open their medication table otherwise it will exit the location or request to add database. Thus, this module acts as a security source for each individual patient in the hospitals, since the respective person only can access their own profile. The doctor has also a password and another IP address to access the data. This module acts as the basic security key element which is used to identify multiple patients continuously based upon the previously updated Fig.8 Steps in Face Recognition 4.2 Patient s Database Upon successful identification of each individual patient, the database table is displayed for the corresponding patient. Using IOT system, it provides a separate IP address to create patient database structure and also it provides the ability to transfer the data over a network. It is created with the SQL Database language. Any new patient can 2948

easily add their details through accessing the IP address. The Existing Patient can update their profile automatically. A video demo is created to show all the procedures of using the software prototype of patient database. It consists of list of data ensuring the sensor details of the patient at different time intervals. Similarly it updates the health results automatically whenever the patient is accessing the sensors. The patient medical data can also be sent to their Email or SMS by creating a block in their data which is shown in Fig.10 below. The Sample database module of a patient is shown in Fig.9 below which has list of medical measurements, updated with time and date respectively. Fig.11 and Fig.12 displays a chart with respect to Blood Glucose Level and Blood Pressure Level with corresponding time and date. Fig.11 Chart of Glucose Level at Varying Time Intervals Fig.9 Sample Screenshot of Patient Database Fig.12 Chart displays Blood Pressure Level at Varying Time Intervals V. Conclusion Fig.10 Database sent through SMS or Email to the patient Thus the Proposed e-health care model using IOT would act as an efficient aid to sick peoples resulting to minimize queues in hospitals and also direct consultation with doctors. It also reduces the dependency on caretakers. Promoting to benefit for all classes of people, who can access the device through pre-registration process and patients can use it regularly to measure the BP level and Blood Sugar level and it prevents from taking to serious stage. It also has advanced patient profile management with secured encryption and decryption. Therefore it can be used as both a personal device all well as a common device, since it can allow multiple users. The proposal would also contribute to make smarter India because of the internet application needed thoroughly. 2949

VI. References [1]A. Mosenia,S.Sur- Kolay, A. Raghunathan and N.K. Jha, Wearable medical sensor - based System design : A survey, in IEEE Trans. Multi - Scale Computing Systems, vol.3, no.2, pp.124-138,2017. [2] Majid A.Al- Taee, Waleed Al- Nuaimy, Zahra J. Muhsin, Ali Al- Ataby, Robot Assistant in Management of Diabetes in Children Based on The Internet of Things, IEEE Internet of Things Journal,2016. [3] M. A. Al- Taee, R. Kapoor, C. Garrett and P.Choudhary, Acceptability of robot assistant in self - management of type 1 diabetes in children, J. Diabetes Technology and Therapeutics,vol. 18,no. 9, 2016, pp.551-554. [4]C. Sakar, S. N. A. U. Nambi, R.V. Prasad and A. Rahim, A Scalable distributed architecture towards unifying IOT applications, IEEE World Forum on Internet of Things, Seoul, 6-8 March 2014, pp. 508-513. [5]Van Nguyen and Audrey Gendreau, A Vision of a Future IoT Architecture Supporting Messaging, Storage, and Computation, International Journal of Future Computer and Communication, vol. 3, No. 6, Dec 2014. [6]M.Li,S.Yu,Y.Zheng,K.Ren&W.Lou, Scalable and secure sharing of personal health records in cloud computing using attribute - based encryption, IEEE Transactions on Parallel and Distributed Systems, 2013, vol. 24, no. 1, pp.131-143 [7]Patel.S,Park.H Bonato.P, Chan.L, Rodgers.M, A review of wearable sensors and systems With application in rehabilitation Journal of Neuroeng Rehabilitation, 20 Apr 2012. [8]A.J. Jara, M.A. Zamora & A.F. Skarmeta, An internet of things-based personal device for diabetes therapy management in Ambient Assisted Living (AAL), Personal and Ubiquitous Computing,15(4),2011, 431-440. [9]Istepanian RSH, Jara A, Sungoor A, Philips N, Internet of Things for M-health applications (IoMT), AMA IEEE medical Technology conference on individual health- Care, 2010, Washington. [10]J.S.Lee,Y.W.Su and C.C.Shen, A Comparative Study of wireless protocols: Bluetooth, uwb, Zigbee and wi-fi, Annual Conference of the IEEE Indust. Elect. Society, IECON 2007, Nov 2007, pp.46-51. 2950

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