IOT based Remote Heartbeat Monitoring Stella Joseph M.E, Dept of Applied Electronics Loyola Inst. of Tech. and Science, Thovalai, TamilNadu, India [email protected] D. Ferlin Deva Shahila, Assistant Professor, Depat. of ECE, Loyola Institute of Technology and Science, Thovalai, 629302. [email protected] Abstract—Deployment of Internet of Things (IoT) has made sensors smarter so that they can interact, collaborate and share experiences. IoT not only reduces human intervention but also assists in taking timely decision. IoT based telehealth is a new paradigm in the health-care industry. Proposed work is an implementation of IoT based heartbeat monitoring system. Proposed method is tested with a photo phlethysmography sensor interfaced to a micro-controller. Micro-controller monitors the heart beat sampling rate and then transmits discrete values over ESP8266 Wi-Fi module. Thingspeak has been used as the remote cloud server for secure data storage and access. Key contributions of the work are, firstly enabling secured cloud platform to keep track of heart beat and secondly facilitating automatic alert system in case of emergency. Besides this, the proposed set up supports made to required sampling rate and investigation. Results have been produced for different acquisition conditions. An analysis scenario, with minimum and maximum thresholds, which can send alert message if the heart beat rate goes beyond the range has been manifested and tested. Proposed method can be deployed for secured and continuous monitoring of heart beat while customizing the analysis. Keywords—Internet of Things (IoT), remote heartbeat monitoring, ThingSpeak, ESP 8266 I. INTRODUCTION IoT is a assemblage of sensors with reliable internet configuration and meaningful simulation technology. By and large IoT concepts produce enormous data collection and aggregation. Multiple devices and sensors lead to sensible analysis and uses. Off late human health monitoring systems has moved from emergency based visits to hospitals to periodical checkups, wearable sensor based data recording and advanced level processing. Remote health monitoring is one of the killing and rapidly growing applications of IoT. IoT in healthcare is a heterogeneous computing, wirelessly communicating system of apps and devices. The goal is to provide recommendation service to patients and health providers remotely, leading to better diagnose and monitor of health related information. There is multitude of benefits of telehealth service. Immediate medical attention, no waiting in long queues, consulting experts remotely, reduced documentation and paperwork, timely release of insurance coverage, expanded reach to various health service provides are some noteworthy outcome of IOT based telehealth service. Heart rate monitoring is a device which allows to measure and record pulse rate in real time. Stored record can also be used for later study. It is largely used in hospitals for checking the health of patients(s) and performance of physical exercise. More than three billion of people are at risk of having heart attack. The focus of this research is to monitor heart beat remotely. The goal is to develop a low power, more reliable, non-intrusive data acquisition system, XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE Suprava Patnaik Professor, School of Electronics Kalinga Inst of Industrial Tech Bhubaneswar, Orissa [email protected] which gather information on the cloud and is accessible to authenticate experts anytime from anywhere. Heartbeat can be used for many applications. A few notable ones are: drowsiness detection system for safe driving, for calculating calories burned during exercise, post surgery health monitoring, health surveillance system for physical or constructive workers, HRV monitoring system for hypertensive patients, monitoring chronic cardiovascular disease, etc. This work addresses an integration of heart beat sensing, reliable wireless communication, and remote authenticated access for appropriate action. Proposed authenticated access includes access for uploading the data and analyzing the data to and from the cloud. II. LITERATURE REVIEW Since the development of IoT concept many researchers have contributed in various ways to the field of e-health care. Key aspects of e-health care are remote study of health record followed by recommending telemedicine. Several publications have been studied and analyzed during the execution period of proposed methodology. In , authors have proposed an m-health acquisition and k-healthcare analysis architecture to use smart phones and deploy IoT. An autonomous IoT based wireless body area network implementation has been discussed in , with a mention about harvesting of solar energy that can be optimally used by the wearable sensors. There are some works towards using Arduino and Raspberry-pi processors for heartbeat sensing [3, 4, 5] and implementing warning systems[6, 7] using IoT. IoT based remote monitoring involves due authorization and access control of data received from distance environment. A trivial approach towards protecting data and individual information is encryption. It is applied before data being uploaded onto the cloud servers. Encryption reduces privacy risks. However it removes search capabilities and therefore resulting in functionality loss towards finding off the beaten conditions. A searchable encryption scheme for secure remote monitoring (SRM) has been addressed in  and authors have claimed that it can also detect outlier. In  authors have demonstrated a hardware based solution Intel SGX.In one of the papers , the authors have discussed the importance of sharing e-medical records securely and reliably. ThingSpeak  is a password protected cloud based research system to ensure security. One can send data to ThingSpeak from personal Wi-Fi enabled devices, create instant visualizations of live data and send alerts using personalized web services like Twitter. ThingSpeak is a user friendly development platform that permits to create sensor logging applications, tracking applications, and status update through a social network. There are many inspiration factors behind secure healthcare service. Firstly, transforming from paper-based health records to an everlasting Electronic Health Records (EHRs) . Secondly, it can captivate the benefit of large storage capacities . Besides these the increasing importance of experts across organizations in sharing and collaborative use of health data [15, 16] can be made possible by cloud based secured health services. III. PROPOSED METHODOLOGY A simplified schematic of the proposed proposed methodology is shown in figure 2 and the components are discussed in this section. Stethoscope and Electrocardiogram are the two commonly used devices for heartbeat monitoring. While the former requires expertise later is expensive. Apart from the traditional devices heartbeat can be measured in an automated way by means of wearable optical sensors. These heartbeat sensors are based on the principle of photo phlethysmography (PPG) . PPGs are nothing but a set-up of LED that imparts light and photo diode to collect the reflected light from the blood. Any change in the blood volume at any vascular region of the body, usually finger tips, causes a change in the light intensity. PPG is a noninvasive, low cost, and simple optical measurement technique applied at the surface of the skin to measure physiological parameters. Figure.1 (a) PPG Sensor A. (b) ESP 8266 Architecture and Sensing Module connecting smart sensors and micro-controllers to Internet. It has an integrated TCP/IP protocol stack. ESP 8266 maximum working Voltage is 3.6V. It can be used for bidirection data transmission. The ESP8266 is capable of either hosting an application or offloading all Wi-Fi networking functions from another application processor. B. Block diagram and Implementation Fig. 3 is the block diagram of the proposed monitoring system. Key components of the setup are: i) the PPG sensory unit SEN-11574, ii) Local processing unit, iii) Wi-Fi module ESP 8266 for secure access of internet iv) LCD setting for data display and iv) Result analysis and alarming coding for emergency action. In this work Arduino Uno micro-controller is used as the central control unit. Arduino is preferred to Raspberry-pi or Odroid because of its PWM control facility. It supported the requirement of reading an analog signal from PPG sensor and sampling it at a desired baud rate. Heart beat monitoring requirements are case specific. Heart beats leading to intrinsic variation in blood volume in human circulatory system and it is controlled by the Sinoatrial (SA) and Atrio-Ventricular (AV) nodes. For a middle aged person with a healthy heart, rate is between 60100 beats per minute when resting. Several clinical and technical factors can lead to heart rate variability. Dizziness, exercising, breathlessness etc. can cause quicker heart beats known as tachycardia. Syncope, diaphoresis, etc. causes slower heart beats called bradycardia. Like blood pressure heartbeat also changes with age. From clinical perspective heartbeat are strongly influenced by age, gender, functional capacity, chronic comorbidities and therapy. The following figure shows the architecture of the setup. Figure.3 Block diagram Considering the fact that there exist various underlying situations responsible for heart rate variability, result analysis need to be tailored made and application specific. Figure.2: Architecture of heart beet sensing system Arduino in used to receive the data from the PPG sensor and send the discrete samples to cloud server through ESP 8266. ESP8266 is a 3V Wi-Fi module often used for C. IoT Middleware Setting IoT is one of the upcoming concepts. It aims for convergence of technology, applications, things, devices, etc., and provide innovative service at low-cost by using wireless communication protocols. Two key perspective of IoT are; firstly observing data for real time actionable insight and secondly proactive or predictive monitoring. Cloud computing along with IPV6 provide everything to promote, develop and integration IoT. There are so many people in the world who will like to use e-health check up support and get rid from suffering because they do not have proper access to hospitals and health monitoring. Every object which connects to IoT requires a unique address or identification from IPv6. Key requirements of IoT based health monitoring are scalability, privacy and security, distributed and decentralized monitoring, reliable real time communication. Apart from things unique identification and data visualization the cloud based software acts as a bridge between an operating system or database and applications, especially on a network, plays crucial role in any IoT app. There are many middleware platforms. Thingspeak, Ubidots, AWS IoT, Temboo, Corlysis, Horavue are some well known IoT platforms that are widely available on the internet. Selecting an IoT platform can be based on important aspects like easy user interface, connectivity compatibility, the delay involved, inbuilt features of the platform, ease of sensor integrity and finally the service cost. These platforms support many of the common IoT protocols like TCP, UDP, MQTT, CoAP and HTTP. This work is based on experimentation on thingsspeak as the middleware. Thingspeak has better visualization and warning mechanism that are supported by MATLAB. IoT supports for placing things anywhere, its the challenge for middleware to make resources usable securely from everywhere. ThingSpeak based on HTTP protocol and supports two way transfer of information from various device. It is highly flexible when new and advanced functions need to be added. It requires each user to create individual accounts and create channels for every sensor. Microprocessors can be connected to ThingSpeak cloud through ESP 8266 by using ‘AT’ command and appropriate baud rate . Sensors need to be assigning with individual channels accessed by means of API Key. Role of ESP module is to establish Wi-Fi connection by using the AT Commands. Arduino then establishes TCP connection to the ThingSpeak cloud so that the sensed discrete values are transmitted at a pre-defined baud rate. Fig.4 shows the complete set up used for this experimentation. Figure.4 Physical setup for heartbeat monitoring Remote health monitoring involves patient’s private information. Records need to be maintained confidentially however allowing access to experts and also for trustworthy analysis. Privet information can’t be shared publicly in order to avail the service. Advanced cryptography tools, such as homomorphic encryption, secure multi-party computation, searchable encryption can assure security of Middleware cloud. However, they may cause delay in communication by imposing substantial computation overhead. ThingSpeak allows blindfold computation. Only the processed results are decrypted and made available by using secret keys. IV. RESULTS AND CONCLUSION The proposed methodology has been tested for various situations and results are produced in this section. In order to compare the accuracy of cloud reading another LCD display is configured locally. Average beats per minute (BPM), computed over every minute and rounded to nearest integer, is found to be accurate for all the set ups. A few cases have been shown here, to ensure that locally sensed information are saved in the middleware without any loss. Figure. 5 (a) Figure 5 (b) Fig. 5(a) and (b) shows the middleware plots and LCD display of average heartbeat for a person aged 25 years. Fig. 6(a) and (b) are the similar results but for an aged person. Both are for resting condition with an average rate 88 and 111. Middleware displays are matching exactly with the on site LCD observations. A simplistic analysis observed during this experimentation is average heart rate is higher for the aged person. Information communication delay is insignificant and there is no error noticed due to discretization or communication over internet. Figure 7 (b) Figure 6 (a) Fig. 7(a) and 7 (b) are the results for the earlier 25 years person but after trade-mill exercise. Goal was to monitor functioning of heart under stress. BPM is monitored during the purposeful physical activity and after an hour of exercise. This time the average heart beat is more than that of exercise state. Therefore the proposed set-up can also be used to monitor how long it takes for the heart to resume its resting rate, that is heart-rate recovery time. Generally, faster a person's heart rate recovers, or reaches its resting rate, the healthier heart condition he or she is in. This work concludes that real time heart beat monitoring is possible and remote analysis wherever required can lead to better health monitoring. IoT and Cloud computing will revolutionize e-health care. However it needs experimentation on large-scale IoT based data storing, processing and retrieval. 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