HEALTH MONITORING ROBOT SYSTEM. SRM Institute of Science and Technology, Chennai, India

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1 Volume 118 No , ISSN: (on-line version) url: ijpam.eu HEALTH MONITORING ROBOT SYSTEM 1 R.Lavanya, 2 M.Maria Dominic Savio 1 Embedded Systems and Technology, SRM Institute of Science and Technology, Chennai, India 2 Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India 1 lavanya.lkd@gmail.com, 2 mariadominicsavio.m@ktr.srmuniv.edu.in Abstract: Now-a-days there is an increase in elder adult population that should be capable of taking care and need a close monitoring in an emergency situation. There is more demanding for smart system like health monitoring emergency system. This paper proposes a smart health monitoring robot system to provide a secure emergency solution to elder people that is living alone at their home. An intelligent smart real-time connected video communication between patient and doctor and there is a wireless Bluetooth connectivity or wifi connected robot system which can be controlled by the remote. The smart robot system will detect the health, alarm in emergency situations. Keywords: Bluetooth, emergency situation, health monitoring robot, video communication. 1. Introduction The world increasing population of elders at age 65 and above who are living independently in their home without help of their relatives and caregivers. Even the caregivers and relatives may can t take care of them 24-hours by monitoring and providing the basic firstaid if he/she gets hurts. people at the age of 65 or above may have health problems like cause of sudden death, heart attack, chronic lower respiratory disease, lose consciousness, strength, body pains, bones weakness etc., If he/she is in serious condition and they are unable to call an ambulance, due to that there may be cause of death situation. To overcome these situations the real-time smart health monitoring robot assistant is provided to protect them. This paper presents a smart real-time health monitoring robot system will detect his/her daily health condition like his/her blood pressure, heart rate, temperature. In emergency situation it detects alarm to the relative so that they can protect and they can communicate and see in which conditioned person is through the video communication. The smart health monitoring robot system protects elders in emergency situation. Now-a-days the most occurring health problem is falls in elders it is a major issue due to which above 60 age people don t live alone. Falls cause disabling fractures and near-fall [1] events are more difficult then fall events because they do not recognize an event by themselves. Falls can be recognized by the evaluation of their posture, vital signs, and balance through their daily activity. But near-falls can t be determined such as cardiac attack, change in blood pressure combined with poor balance. To detect the location of the human target, the plan is to project a tracking algorithm based on pyroelectric infrared sensor[2] network and video technologies. The PIR node detects the human movement by using PIR sensors. According to the change in angle of human body movement and also based on the space-time conversion model, then PIR nodes complete the tracking and recognition of human target. The mobility is common problem for persons with stroke[3], by measuring daily physical activity of a person is helpful to know the effectiveness of rehabilitation interventions. The smart wearable shoe-based sensor system was shown to be capable of classify the three major postures and their daily activities like sitting, standing, and walking these are from people who live alone with stroke by using Artificial Neural Network. The wearable sensor will sense the patient and record their vital signs into an electronic advanced technology which records the patient s[7] database. The wearable sensor provides vital signs monitoring, tracking patient s location and data storage of medical report. There are two types of vital signs sensors they are pulse rate and blood pressure sensors. The pulse oximeter is attached to the patient s finger and it measures the patient s heart rate, blood pressure they use a cuff pressure over the patients upper arm it measures systolic and diastolic blood pressure and also the integrated two types of location sensing capabilities, a GPS [4] to provide geolocation and detect the indoor location system to provide location where the GPS signal cannot be reached. The growth of data sent produced by the medical community it requires a storage[10] capabilities. In order to know the medical report of any patient s, to improve the advanced new technology innovation. The new technologies in E- 19

2 healthcare [6] are of medical treatment where it starts with patient s registration and update the data of all medical reports of patient and any other information and store the data. 3. Proposed System 2. Existing System This project will present a robot-assisted intelligent emer- gency system for individual elderly independent living. It as an innovative senior independent living emergency assistance platform through a robot- sensor system as shown in the below figure Figure 2. Block Diagram of Wearable Device Figure 1. Overview of robot-assisted emergency system The emergency assistance capabilities, wearable sensors will be implemented. Motion sensors are used to monitor different activities of the senior citizen. In case of emergencies, such as falling on the ground, will be detect in real-time. Due to the acceleration rate of person s positions exceeds a certain threshold, etc., it will automatically determine that is not sitting on sofa, or lying on bed, but a falling accident. We integrated the robot and wearable device together and achieve a seamless hardware / software integration. The wear- able device is wirelessly (Wi-Fi or Bluetooth) connected to robot. When an alarm is fired by wearable device, the robot will initiate actions, for example, automatically call a relative who can remotely tele-control the robot with video communication to investigate the situation and respond accordingly. In this case, we can reduce the false alarm rate which limits the effectiveness of other solutions. The wearable device can also send an alert directly to relatives or doctors in case acknowledgement is not received from robot. It is very important to provide health-care service for elder s who live alone at their home especially people who have chronic diseases and heart problem they need 24/7 healthcare where it can t be provided by others or by a medical staff of hospital. so to overcome these emergency situations making the health-care service available for everyone mostly for elder s, and to avoid the public transportation problems while reaching the particular destination too late. To prevent these kind of emergency problem we use cloud storage service to store the data information of elders daily health activities like blood pressure, pulse rate, temperature, mems and data can be monitored by their relatives and doctors. A. ARM7-LPC Hardware Used ARM7 is a architecture which was used in many processors and microcontrollers.arm7 is a successful architecture and most widely used processor family in embedded system application, it perfectly suits for beginners to get in-depth idea about hardware and software implementation. LPC2148 is manufactured by NXP Semiconductor (Phillips) and it has many in-built features and peripherals. It has 32KB of SRAM and 512KB of on chip Flash memory and it supports upto 2KB of USB RAM. This memory is more than enough for almost all applications. This memory may used for both code and data storage. It has two IO ports each of 32bit wide, provided by 64 IO pins. Ports are named as P0 and P1. 20

3 B. Temperature Sensor A temperature sensor is device which measure the temperature. The sensor works by sending voltage across the diode. If the temperature is cool then the diode will have less resistance and if the temperature is high then the resistance will be more. Then the resistance is measured and converted to the corresponding temperature like Fahrenheit, Celsius, Centigrade. Normal body temperature varies by persons age, activity, and time of day. The average body temperature is 98.6 F (37 C). For 10mv the change in temperature will be 1. The range of temperature is from -55 C to 150 C. C. Blood Pressure Sensor For measuring the blood pressure we use Sphygmomanometers monitoring machines which measure a patient s blood pressure by keeping the cuff over an arm and then inflate the cuff first due to the pressure the blood flow is blocked at artery and when the is reduces the blood starts flowing and then record the sound and noted is known as systolic blood pressure and when cuff it is in relax state that is known as diastolic blood pressure. D. Heart Rate Sensor The heartbeat sensor consists of a light emitting diode and a light detecting resistor or a photodiode. The heart beat pulses have variation in the flow of blood in all regions of the body. When a light is emitted by an led, it will either reflect the light or transmits the light. The light detector receives the transmitted or reflected light when some of the light is absorbed by the blood flowing through artery. The amount of light absorbed depends on the blood volume in that tissue. The detected light form output is an electrical signal and it is proportional to the heart beat rate. E. MEMS Acceleration Sensor The acceleration sensor is a 3-axis accelerometer. The rotation of gravity, accelerometer of 3-axis when a person is going to fall then there will be change in an angle. The position of person may be in sitting, standing or sleeping etc., due to that the angle may be changed. Accelerometer is measured in units of g. where g is the acceleration measurement for gravity equal to 9.81m/s2. 5. Results & Discussion A. Simulation Result Figure 3. Simulation 21

4 C. Working Principle B. Hardware Results Figure 4. Simulation Output There are number of sensors which are placed at there respective locations on the human body and they are connected to ARM7-LPC2148 board. The output of LM35 temperature sensor is converted to digital form with the help of ADC pins present in LPC2148 board. The pulse rate sensor works when the blood is pumped from heart through the blood vessels then the finger becomes slightly opaque and the detector will detect less light. so for each heartbeat the detector signal varies and that particular signal variation will be converted into an electrical pulse. The pulse will be indicated by an LED which blinks for each heartbeat. The Blood Pressure is monitored by Wrapping the cuff around the upper arm and there should be one inch above the antecubital fossa and by pressing the bulb rapidly inflate the cuff to 180mmHg and then release the air inside the cuff at a moderate rate (3mm/sec). Then we ll have systolic blood pressure and diastolic blood pressure. Then these values are transmitted and they can see it on the HyperTerminal monitor. 6. Conclusion and Future Work Figure 5. Hardware Figure 6. Hardware Output In this project, the advanced personal health safety telemonitoring system that offers many critical features which are missing from contemporary home security and personal emergency response system at a significantly lower cost. The telemonitoring application is presented which allows others to view the patient s vital parameters remotely and dynamically in a Web page in real time. In future this work can be extended by adding smart robot vehicle. The smart robot plays a core role for emergency assistance of senior individual, In order to automatically recognize the emergency situation. References [1] N. Noury, A. Fleury, P. Rumeau, A. K. Bourke, G. O. Laighin, V. Rialle, and J. E. Lundy. Fall detection - principles and methods. In th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages , Aug [2] J. Lu, J. Gong, Q. Hao, and F. Hu. Multi-agent based wireless pyroelectric infrared sensor networks for multi-human tracking and self- calibration. In 2013 IEEE SENSORS, pages 1 4, Nov [3] T. Zhang, G. D. Fulk, W. Tang, and E. S. Sazonov. Using decision trees to measure activities in people with stroke. In th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages , July

5 [4] S. S. Al-Majeed, I. S. Al-Mejibli, and J. Karam. Home telehealth by internet of things (iot). In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), pages , May [5] Tia Gao, D. Greenspan, M. Welsh, R. R. Juang, and A. Alm. Vital signs monitoring and patient tracking over a wireless network. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pages , Jan [6] S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K. S. Kwak. The internet of things for health care: A comprehensive survey. IEEE Access, 3: , [7] L. Catarinucci, D. de Donno, L. Mainetti, L. Palano, L. Patrono, M. L. Stefanizzi, and L. Tarricone. An iot-aware architecture for smart healthcare systems. IEEE Internet of Things Journal, 2(6): , Dec [8] M. S. Jassas, A. A. Qasem, and Q. H. Mahmoud. A smart system connecting e-health sensors and the cloud. In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), pages pages , May [9] C.O. Rolim, F. L. Koch, C. B. Westphall, J. Werner, A. Fracalossi, and G. S. Salvador. A cloud computing solution for patient s data collection in health care institutions. In 2010 Second International Conference on ehealth, Telemedicine, and Social Medicine, pages 95 99, Feb [10] Zhen-Yu He and Lian-Wen Jin. Activity recognition from acceleration data using ar model representation and svm. In 2008 International Conference on Machine Learning and Cybernetics, volume 4, pages , July [11] M. Rajesh, Manikanthan, ANNOYED REALM OUTLOOK TAXONOMY USING TWIN TRANSFER LEARNING, International Journal of Pure and Applied Mathematics, ISSN NO: , Vol-116, No. 21, Oct [12] T. Padmapriya, V.Saminadan, Performance Improvement in long term Evolution-advanced network using multiple imput multiple output technique, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, Sp-6, pp: ,

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