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  1. 1. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515 Heart Pulse Monitoring: The Smart Phone Way Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh (Department of Information Technology, Sinhgad College of Engineering, University of Pune, Pune)ABSTRACT Its never been easier to know what your condition and vital signs during that time willheart rate is! This report is based on designing an enhance the responders ability to assist them in thecell phone application that will measure your best possible way. This report concentrates mainly onheart rate or in simple words a Cardiograph. two vivid regions: one is the diagnosis of vital signsWithout any external hardware, just using a built- of the body and how a mobile phone can help inin sensor of your Smartphone or tablet, you can biotelemetry, and the other is the means of sendingget accurate readings almost instantly. This all the data through remote connection in case of atechnique will enable the user measures your life threatening emergency situation. I have doneheart rate. You can save your results for future extensive studies in vital signs identification from areference, keep track of multiple people with person and propose new methods of using mobileindividual profiles, add notes and locations, and phones sensors for quantifying the vital sign. Theseeven print out your measurements for sharing or methods utilize the accelerometer, video camera insafe keeping. It uses your devices built-in sensors tandem with a LED ash and microphone sensor toto calculate your hearts rhythm - the same detect and deliver a value for the needs. In thisapproach used by professional medical chapter, I provide a general discussion about the vitalequipment. signs and the need for its diagnosis. Knowing how fast your heart is beatingcan be very useful when exercising, if youre 2. VITAL SIGNSunder stress, if out have a heart-related medical Vital signs are the most basic functions thatcondition, or even just out of curiosity. Every can be measured from a person; they indicate theirmeasurement taken can be saved to your personal physical condition and wellness. When thehistory, so you can keep track over time. In measurements tend to move away from normal, anaddition to the date and time of the measurement, abnormality in the physical status can be inferred.you will be able to save the location at which it Most medical conditions can be diagnosed throughwas taken (and see them on a map). This app can vital signs and confirmed with the help of specialbe perfectly tailored to allow multiple people to tests. Each vital sign is measured differently with theuse the app on a shared device. A profile can be use of specialized equipments. These equipments arecreated for each of your family members or not handy and do not come in miniature sizes forfriends, and each of them has their own individual portability. Hence I introduce the concept ofmeasurement history converting a mobile phone, which people use in their day to day life into a vital sign diagnosing tool. There1. INTRODUCTION are four vital signs which are standard in most With the advent of internet, a lot has medical settings:changed in peoples lives. Users can stay in the house _ Pulse rateand do almost any kind of activity like shopping, _ Respiratory ratemovies, entertainment, physical exercise, without _ Blood pressurephysically being at the appropriate place. In short, we _ Temperaturecan tell that our lives are made simpler, faster andefficient, if we leave behind the negative aspects of it. 2.1 Pulse rate (HR): Pulse rate is the rate at whichIn the heart beats, measured either in the wrist or neck The field of telecommunication, video given by beats per minute. The pulse rate isphones have a wide scope in bringing people together influenced by the expansion of the arterial wall forwith face-to-face communication. Furthermore, video every beat. The most prominent spots for the pulsestransmission from mobile phone as a video call are wrist (Radial artery), neck(Carotid artery), insideenhances the mobility of users. In case of a 911 of the elbow (Brachial artery), behind thesituation, this will allow people to make knee(Popliteal artery) and ankle joint (Posterior tibialconversations a lot more understandable even under artery) [1]. The pulse rate varies with age and alsochaos. The responders could arrive at a decision more depends on the physical and psychological effects onquickly, when viewing the live video feed of the the body. Higher pulse rate indicates the presence ofperson. The callers Information about the physical abnormality in the body and can also be caused by other reasons such as anxiety, anger, excitement, 509 | P a g e
  2. 2. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515emotion, heart disorders, asthma, a large meal and so blood pressure of a person. Due to various reasons,on. The pulse rate of an individual can help in the average blood pressure differs from eachdetermining various problems within the body, but it individual. The pressure values are categorized intocannot be used solely to diagnose an abnormality. five major divisions. Table 1.2 shows the categoriesPulse rate is just a basic tool for diagnosis and hence of people in accordance to their blood pressure range.can be used only for primary diagnosis. Table 1.2. Categories of Blood Pressure2.2 Respiratory rate (RR): Respiratory Rate is the Category Systolic Diastolicnumber of breaths a person takes within a certain (mmHg) (mmHg)amount of time or more formally, defined as thenumber of chest movements involving inspirationand expiration per unit time. The RR is measured in Hypotension <90 <60units of breaths per minute. It is measured by Normal 90-120 60-80counting the number of breaths (number of times the Pre- 121-139 or 81-89chest rise) for a minute, usually when the person is at hypertension 140-159 or 90-99rest. Respiratory rates will increase as the demand for Hypertension 1 >=160 or = 100oxygen increases; it also increases due to illness, Hypertension 2intensive physical activity, etc. The average RRreported for a healthy adult at rest is usually given as12 breaths per minute (12/60 Hz) [2] and the 2.4 Temperature (T): Temperature is one of theestimates vary between 12-20 breaths per minute, other important vital signs. There is no direct way ofwhereas the respiratory rate is higher in the case of measuring the persons temperature from the mobileyoung adults, children and babies. As an individual device as of now. Mobile phone manufacturers haveage, breathing rate declines. In slow rates, more started incorporating onboard eco temperatureaccurate readings are obtained by counting the sensors to mobile phones. It is just a matter of timenumber of breaths over a full minute. Table 1.1 [3] before the temperature of surroundings and a humanshows the heart rate and respiratory rate at varying body can be measured using the phone.ages showing a gradual decline in the rate with age. 3. MEANING OF PULSE:Table 1.1. Heart Rate and Respiratory Rate for A pulse is a sudden burst of blood to theDifferent Ages circulatory system when the walls of the heartAge HeartRate RespiratoryRate contract. Heart rate or pulse rate is defined as the (beats/min) (breaths/min) number of heart beats or pulses in a minute. TheNewborn 100-160 30-50 human heart comprises the atrium and the ventricles,0-5 months 90-150 25-40 which coordinate to form a complete pumping action.6-12 months 80-140 20-30 Approximately 2000 gallons of blood is pumped by1-3 years 80-130 20-30 the heart every day. A Heart beat cycle consists of3-5 years 80-120 20-30 two components, namely systole and diastole. Systole6-10 years 70-110 15-30 occurs when there is an electrical impulse generated11-14 years 60-105 12-20 by the Sinoatrial(SA) Node, triggering the heart to14+ years 60-100 12-20 contract. Diastole occurs when the heart is relaxed. Systole and diastole alternate each other to produce a heartbeat. The heart rate is not just about how fast the2.2 Blood pressure (BP): Blood Pressure is a force heart is beating; it is a regulatory mechanism forexerted by blood on the walls of arteries, veins and delivering oxygen to the muscles to keep up thethe chambers of the heart. Blood pressure is one the demand.most important vital signs and the body 3 maintain itby interacting with the volume of blood and the force 4. MEDICAL WAY:of contraction of the heart. During each heartbeat, BP Acoustically, the heart rate is measured byvaries between a maximum pressure called systolic listening to the heart beats, which are amplifiedpressure and a minimum pressure called diastolic through the use of a stethoscope. Usually thepressure. It is measured on the inside of an elbow at numbers of beats for a small interval of time, say 10the brachial artery, which is the upper arms major seconds, is observed and obtained for a minute byblood vessel that carries blood away from the heart. multiplying with 6. In the same way, the pulse felt atA persons BP is usually expressed in terms of the the wrist and neck can be measured and directlysystolic pressure and diastolic pressure values. An related to the heart rate. Figure 2.1 shows the regionsaverage healthy adults pressure values read 120 where the pulse can be felt clearly for measurement.mmHg during the systole and 80 mmHg during A more precise method of determining pulse ratediastole. Pumping Rate, blood volume, resistance, involves the use of an electrocardiography (ECG orviscosity, etc. are some of the factors which affect the EKG), pulse oximetry, etc. Shelley[4] discussed 510 | P a g e
  3. 3. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515about the effectiveness of pulse oximetry in the 5.1 Detail Working: The model works on thedetection of pulse even under noisy conditions where principle that, every heart beat pertains to a rush ofthe use of stethoscope is hopeless. There are many blood in the blood vessels, even in the capillaries atcommercial heart rate monitors available in the the finger-tips. Whenever the capillaries are rich inmarket which use two tiny blood during a systolic pulse, more light is getting absorbed by the blood, leading to lowFigure 2.1. Prominent places for pulse detection. Figure 5.2. Method of placing the finger over the camera for heart rate measurement Electrode strips to find the heart rate, thesame way an ECG works. These electrodes are reactive index and darker frame intensities.generally attached to some fitness gear or costume, Likewise, during a diastolic pulse, most of the lightdisplaying the measurements on a screen. ECG uses gets reflected leading to bright frames. This change inthe electrical activity of the heart over a period of intensity of light which can pass through the fingertime, measured through the electrodes connected to creates an alternative pattern of waves similar to athe skin. These electrodes induce a tiny current of a pulse. This change in intensity with time gives thefew µA into the body and detect electrical changes heart rate of a person.caused by the heart during each heart beat. Thesechanges are captured, amplified and delivered as anoutput.5. THE SMARTPHONE WAY: Even with the presence of manytechnologies for finding the heart rate of a person,only a few of them are accurate to a certain degree. Anew model for heart rate estimation was proposed,which worked on the concept of Photoplethysmography (PPG), without using thewavelength of light for analysis. Most mobile phonesin todays market come with a stock camera andoptionally a LED flash. These components were usedto define a system, deriving the heart rate of a person. Figure 5.3. Architecture of heart rate system In the proposed method, a video of short duration was recorded, with the finger placed over the lens of the mobile camera. The flash is turned ON, so that adequate amount of light can reach theFigure 5.1 shows the camera with flashlightin a finger for proper measurement. For this experiment,Smartphone an application was developed for Nexus One[5] to 511 | P a g e
  4. 4. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515keep the LED flash consistently ON while recordingvideo from camera. The first three seconds of datafrom the camera were discarded, since the CMOSsensor of the camera tries to focus when turned ON.Also, the camera doesnt need to be focused, as theresults rely only on the amount of light entering thevideo feed. It was generally hard to detect thefluctuations in the frames unless the pulses aredistinct. A similar methodology was used byBanitsas[6] with a slightly different approach in theanalysis of video frames. Figure 5.5. Filtered Data for analysisFigure 5.4. Area under analysis5.2 Implementation: Working of the systemcomprises of six functional modules. Initially thevideo frames were split into four quadrants and onlythe first quadrant (Figure 5.4) was considered foranalysis, since most of the changes and fluctuationsare predominant in that region. Every pixelinformation on each frame was split into individual (a)Red(R), Blue(B) and Green(G) components. In most Figure 5.6. (a) Total Window of Datasamples, the prominent color applied only to R withthe others tending to zero in every frame, hencedifference in the red channel (Rc) intensity to that ofall the channels of a frame was negligible. Foraccuracy of plots, only the Rc in video frames wereconsidered. The average intensity of pixels for everyframe was calculated as its frame intensity. The rawintensity values were filtered with a moving averagefilter to remove rough peaks from the graph for easieridentification of peaks. Figure 5.5 shows the filteredresults from the raw data obtained from finger pulse.The entire frame was split into windows of fixedlength (Wt), for determination of peaks occurring atequal intervals of time as seen in Figure 5.6. If thepattern within Wt matched a sinusoidal pattern, theheart rate was calculated by determining the numberof peaks (n) in the window and multiplying the peakcount with the window length as given by theequation 1. (b) Figure 5.6. (b) Data spilt into smaller Time frames 512 | P a g e
  5. 5. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515Equation:1 window size. If the algorithm misses counting a peak that should be present butHR = n _ 60=Wt moved to the next window in a small fraction of time, the obtained results could vary a lot. There are two ways of calibration:6. ACCURACY: (i) Average This method showed encouraging results (ii) Window time calibrationwith high percentage of accuracy. The collected data Of the two, measuring the average of windows is thewas validated with a commercial heart rate monitor easiest of the calibration methods. Two or moreavailable at a fitness center. To prove the windows could be taken and the average of numbereffectiveness of the proposed system, higher heart of peaks could be computed to give a better result.rate to the subject with excessive physical activity However, there is a small disadvantage inwas induced. From Table 6.1 it can be seen that the incorporating this method. If there is not enoughmethod gives a high percentage of accuracy from the legible data available for calculating peaks inobtained data in finding the HR of the person. multiple windows, the results will be erratic. HenceTable 6.1. Accuracy of results at varying heart window time calibration more suitable for thisrate for a single subject application was considered. Figure 7.1 shows a 5 sec Window 10 sec Window simulated heart pulse wave. The wave could be different in case of irregular heartbeat and illness. In Actual this technique, it was assumed that the heart rate data HR Value Accuracy Value Accuracy set is a perfect sinusoidal wave with equal interval between peaks. Based on my observations from the data, an algorithm was proposed.1.In summary, given 102 108 94.11% 102 100.0% the frame intensity value of a fixed time window, the 108 96 88.89% 102 94.44% number of peaks 114 108 94.74% 114 100.0% 132 132 100.0% 132 100.0% 154 144 93.51% 150 97.40% It can also be seen that, with the increase inwindow size for analysis, the error propagationdecreases to a very minimum. For better accuracy ofthe result, the user should hold the finger over thecamera lens for a longer time. Table 5.2 shows theexpected error in the system when varying thewindow size. For less error in the results forconsidered samples, the window size should be keptlarge. Even more, the accuracy of the heart rate for afull minute of data is precise with the actual heart ratemeasured manually, with a 100% accuracy all thetime. Based on the need, it is possible to measureevery single heart beat with precision.Table 6.2. Expected error from the system basedon window sizeWindow Size Expected Error Error %(sec)5 ±12 0 - 11.110 ±6 0 - 5.6 Figure 7.1. Calibration Of heart rate15 ±4 0 - 3.720 ±3 0 - 2.8 Algorithm 1 Window time Calibration30 ±2 0 - 1.87. WINDOW TIME CALLIBERATION FOR ACCURACY The obtained results look promising, butthere is a lot of error introduced due to the smaller 513 | P a g e
  6. 6. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515 8. IMPACT ON HEART RATE WITH AGEINPUT: Video Intensity for a Time window (T) & PHYSICAL INTENSITY The approach uses observational data andOUTPUT: Calibrated Heart Rate(CHR) does not require much computational analysis, however a sensitivity analysis is very helpful inN ← no of peaks in Window /*N x 60/TGives predicting the outcome of the decision based on theuncalibrated HR value*/ situational parameters. In the heart rate measurement, the key parameters involved in the determination ofpk2pk ← AvgPeaktoPeaktime /*Timebetweentwo heart are the age and the intensity of work beingconsecutive peaks*/ done. Even though there are some more biological factors involved with it, they cannot be quantified atEx ← T – N x pk2pk / *Ex is non considered time this point. Researchers from Oakland University[7]for Datapoints*/ have predicted the maximum heart rate of people by age based on records collected over 25 years, givingif Ex >= pk2pk then rise to a non-linear equationN ←N+1end if Table 8.1 Calibration results for Heart rateif Ex >= pk2pk / 2 thenN ← N – (pk2pk - Ex ) x 60 / Tend ifCHR ← N x 60/ T for the window are counted and the timetaken between consecutive peaks is calculated. Thenthe average value for peak to peak time (pk2pk)computed. If the occurrence of the peaks is harmonic,the average will be same as an individual consecutivepeak time. This gives the time taken for one completecycle of systole and diastole. The wave marked in redin Figure 2.7 is the region that is neglected during thecalculation of heart rate. Hence the total time of datawhich is not being considered is denoted as Ex.When Ex exceeds the time taken for a completecycle, one more peak is added to the calculatednumber of peaks and the difference between Ex andthe cycle time is taken as the new unconsideredvalue. If Ex exceeds half the cycle time, calculate thefractional calibration time value for the peaks,otherwise the number of peaks is taken as the heartrate. The calibrated heart rate values showed greateraccuracy when compared to the un-calibrated data.Commercial heart rate monitors had the same amountof error induced due to time window computation. HR is the exact heart rate estimated for 60Hence, the HR for a full 60 sec or 2 x 30 is seconds in a dataset; C - Calibrated result; UC – Un-considered as the heart rate of a person for calibrated result; Acc.% is accuracy of Calibratedcomparison with the calibrated results of smaller data; Imp.% is improvement of accuracy in calibratedwindow sizes. A single users data set, taken at data from un-calibrated data for determining thedifferent time of the day, calibrated for different HRmax .window sizes is shown in Table 8.1. From Table 8.1,significant improvement in the results can be seen, Equation 2much closer to the actual heart rate. Most of the data HRmax = 191:5 - (0:007 x age2)items had improved accuracy, except a few marked inred in the table which show decreased accuracy fromthe actual result. This happens due o the irregular Gellish[8], proved the predicted heart rateheart cycle, where the peak to peak interval varies lies in a tight range between ± 2 - 5bpm for averagelargely to yield errors in calibration. The longer the individuals. However, the values vary within a widerdata gets, the results will be more accurate. With range for athletes. The actualshort data lengths, the chances for error propagationwill be high. 514 | P a g e
  7. 7. Mayur Lunawat, Aquibjaved Momin, Varun Nirantar, Abhishek Deshmukh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.509-515 [2] T. Gerard and A. Nicholas, Principles of Anatomy and Physiology, Harper-Collins, New York, sixth edition, 1990. [3] Wikipedia, “Variation of vital signs with age", http://en.wikipedia.org/wiki/Vital signs. [4] S. Kirk, “Photoplethysmography:Beyond the Calculation of Arterial Oxygen Saturation and Heart Rate", Anesthesia & Analgesia, vol. 105, no. 6S Suppl, pp. S31-S36, December 2007. [5] Google Inc., “Nexus one: Technical speci_cations", http://www.google.com/ phone/static/en US-nexusone tech specs.html. [6] K. Banitsas, P. Pelegris, T. Orbach, D. Cavouras, K. Sidiropoulos, and S.Figure 8.1. Sensitivity analysis for 10 to 80% Kostopoulos, “A simple algorithm toincrease in Age and Work Intensity monitor hr for real time treatment applications", in Information Technology heart rate depends on the intensity of work and Applications in Biomedicine, 2009.involved by the person. Resting heart rate is the ITAB 2009. 9th International Conferencelowest heart rate, which can be achieved by a normal on, 4-7 2009, pp. 1 -5.healthy person. Even at rest, the intensity of work [7] “Medicine & science in sports & exercise",will be a little more than 25 % and the heart rate The Official Journal of the Americanincreases as the intensity of work increases. There College of Sports Medicine, vol. 39, no. 5,exists a direct relationship between the heart rate and pp. 749-898, 2007.the work intensity, hence the percentage a work [8] G. Ronald, G. Brian, O. Ronald, M. Audry,intensity for age gives the heart rate at that moment. R. Gary, and M. Virinder, “LongitudinalFrom Figure 8.1, it can be seen that the HR gradually modeling of the relationship between agedecreases with age and increases with work intensity. and maximal heart rate", Medicine &The resting heart rate of people can go well below Science in Sports & Exercise, vol. 39, no. 5,normal and reach 45-60 bpm [9] for normal pp. 503-508, 2007.individuals. The results of sensitivity analysis show [9] “What should you know about your heartthe normal range of heart rate which a person can rate or pulse", http://www.nemahealth.org/achieve based on the age and work percentage. It is programs/healthcare/heart rate pulse.htm.also evident that, the proposed methodology followsthe pattern and most of the values lie within the rangeof the graph, proving its effectiveness.9. INFERENCE This chapter we have discussed over thenormal medical protocol and described a new systemthat uses the camera on a mobile phone to find heartrate. From the results, it was inferred that for longerduration of data collection, there was a better chanceof achieving more accurate heart rate. It was alsoobserved that the accuracy could be improved from87% with a 5 sec data to 99 % with a 30 sec datawithout any calibration. After using the calibrationmethod, it was found that there was an improvementin the accuracy up to 5 or 6 % for even small sizeddata. This method worked well for 15 sec data andhelped achieve 98 % accuracy most of the time.REFERENCES [1] N. Abhijit, “Normal pulse rate", http://www.buzzle.com /articles/normal- pulserate. html. 515 | P a g e