recent technological advances in electronic and biomedical engineering are making it possible to move health monitoring away from the hospital and the clinic to portable personal devices . these devices , which are often used in sport - related applications , are gaining an important role in health research and monitoring , enabling fast measurement of health parameters , accurate diagnosis of a number of conditions , and a reduction in hospital admissions . monitoring of heart function , accurate diagnosis of cardiac conditions , and prevention of heart disease are important goals . it is a noninvasive diagnostic technique that records the electrical activity of the heart and enables diagnosis of a number of abnormalities and conditions based on a few simple features . the system described herein does not use a multichannel electrocardiograph but a much less expensive heart rate ( hr ) monitor and provides three types of tests : stress test , based on hr variability ( hrv ) and instantaneous hr;arrhythmia classification , based on r peak to r peak ( rr ) intervals;energy consumption evaluation , based on resting hr and instantaneous hr . stress test , based on hr variability ( hrv ) and instantaneous hr ; arrhythmia classification , based on r peak to r peak ( rr ) intervals ; energy consumption evaluation , based on resting hr and instantaneous hr . reduced hrv has consistently been documented in patients with anxiety disorders and chronic or posttraumatic stress , irrespective of individual features , due to autonomic nervous system alterations . several studies have demonstrated connections between a chronic stress state and the risk of cardiovascular disease ( cvd ) . fast and easy testing of the main parameters of heart activity is therefore quite useful in daily life . a real - time personal stress monitor can provide continuous information on the user 's stress level at all times and in any place . rr intervals are important measures in sinus rhythm classification and identification of arrhythmia episodes and allow detection of sinus arrhythmia , premature ventricular contraction , ventricular tachycardia , ventricular flutter or fibrillation , and second - degree atrioventricular block . a portable system providing hourly testing of heart activity and detecting episodes of arrhythmia would be valuable for patients with cvd . several techniques are capable of providing these results ; some detect a single type of arrhythmia , whereas others detect multiple types . the former techniques include time - domain analysis , sequential hypothesis testing algorithm , threshold - crossing intervals , artificial neural networks , time - frequency analysis , fuzzy adaptive resonance theory mapping , and sequential detection algorithm ; the latter approaches include multiway sequential hypothesis testing , wavelet analysis , artificial neural networks , complexity measure , multifractal analysis , wavelet analysis combined with radial basis function neural networks , and nonlinear dynamic modeling . these methods rely on ecg signal analysis ; however , the analysis is not always technically feasible due to signal noise and the high computational cost of real - time analysis . we describe a validated method that allows real - time classification of sinus rhythm type and detection and classification of different arrhythmia events by an approach based on rr intervals and instantaneous hr . instantaneous hr is an important index of recovery after myocardial infarction and of the effect of medication for a number of neurological diseases [ 6 , 7 ] . it can also be used to estimate the calories burned by any type of activity . currently , the most accurate methods for measuring energy expenditure are based on calorimeters and respirometers , but they are quite expensive and are not suitable for portable devices . the portability requirement is met by approaches based on the hr monitor and the accelerometer , which provide effective means to estimate individual 's energy expenditure during a given activity . we describe a novel hr - based algorithm that despite being less accurate than the methods mentioned above is sufficient for the application , which provides only an index of an individual 's level of activity . a number of similar systems and applications have already been devised , but none integrates powerful algorithms that process hr and the tachogram into an easy - to - use application that provides clear and immediate test reports to the user and to medical personnel . moreover most existing systems merely display the raw data acquired by one or more wearable sensors , whereas others use sensors in the smartphone to show the raw data . another remarkable and innovative feature of the device described herein is that , unlike existing systems , which contain a single type of signal processing algorithm , it provides three different tests , that is , stress test , real - time classification of sinus rhythm type , and detection and classification of different arrhythmia events . this feature allows documenting correlations between stressful situations and arrhythmia episodes and enables cvd patients , who are prescribed exercise , to measure real - time calorie consumption . the test reports can then be sent to and stored in an online database that can be searched by a physician . this paper shows how data about an individual 's health state can be collected by real - time sampling and analysis of a few parameters using noninvasive , inexpensive , and portable devices . the hr monitor used in this work is a zephyr hxm - bt device ( zephyr technology corporation ) that was chosen for its modest cost , high usability , and good performances . an application for android os , which is widely applied in portable devices all over the world , was developed by our team . its main tasks include data processing ; reporting of test results to the user ; test scheduling during the day ; dialing of automatic emergency calls or sending an sms in case of critical user conditions ; and data storage in a telemedicine portal database ( figure 1 ) . the application also provides the controls , settings , views , and bluetooth connection management required for easy system use and clear test reporting . a bluetooth connection between a smartphone or a tablet and the hr monitor provides secure data acquisition . the hr monitor used in this work transmits data packets containing information acquired by biosensors at a frequency of 1  hz ; a routine implemented in the android application extracts from each packet the following information : instantaneous hr , last 15 heartbeat time stamps , and last heartbeat progressive number . the test results are then saved to an internal sqlite database and may be sent to an online mysql database via wi - fi or a 3 g connection of the android device using json format and php web pages . hrv analysis in the time domain is based on the calculation of a number of statistical indices over an appropriate data acquisition window . a first key feature that needs to be calculated is the standard deviation of the rr interval ( sdnn ) : this is the square root of variance and is related to all the cyclical components connected to the variability detected during the observation period . it is important to note that hrv variance depends on the length of the observation period ; measurement repeatability and comparability require this period to be established in advance . a standard duration ( e.g. , 5 minutes ) therefore needs to be set . the next feature to be extracted is the square root of the mean squared differences of successive rr intervals ( rmssd ) . the last collected feature is the proportion that is obtained by dividing the number of interval differences of successive rr intervals greater than 50 milliseconds ( ms ) by the total number of rr intervals , in the observation window ( pnn50 ) . these data and the average hr ( hravg ) are the key stress test parameters and are based on a decision threshold for each feature . since in the case of stress testing the analysis requires the heartbeat time stamps , the algorithm stores these values : a number of r peak time stamps are extracted from each packet depending on the heartbeats that have been detected after the latest packet has been received . this control is enabled by checking the heartbeat number extracted from each packet . then the rr intervals are calculated in real time from the time stamps and stored . at the beginning of the algorithm ( figure 2 ) , there are i = 0 packet received and n = 0 heartbeats detected . after the arrival of the ith packet , the first control is whether the index is < 2 . if this is true the system must wait for the next packet before processing the data ; if it is false the control is on the heartbeat number ( hbn ) . if the difference from last hbn to previous hbn is 0 , no new heartbeats have been detected and the algorithm waits for the next packet . if the difference is 1 , 2 , 3 , or 4 , new heartbeats have been detected and the relevant rr intervals are calculated and stored . the limit is 4 heartbeats because the maximum hr measurable by the hr monitor is 240 beats / minute or 4/second . the next control involves index i which is compared with the predetermined duration of the analysis . in fact , this index is also used as the timer of the algorithm in relation to the frequency of 1 packet received / second . if the index is less than the duration of the observation window the system waits for the next data packet before repeating the steps ; otherwise , it calculates all the statistical features and compares them with the threshold , to determine whether a stress state is present . the decision is reached by majority vote : if three of the four values exceed ( hravg ) or are lower than ( other features ) the thresholds ( table 1 ) , the stress state is considered to be present . it is important to note that sdnn is calculated at 5-minute intervals regardless of the duration of the analysis and then averaged over that duration , because this parameter changes with the length of the interval and because the relevant threshold is referred to as a 5-minute window . the evolution of the tachogram is plotted on the phone display and the test results are finally shown and stored in a local database . the application also allows alerting a distant person ( e.g. , a relative ) by dialing an emergency call with a prerecorded message or sending an sms to a favorite number if a stress state is detected . the test results can also be stored in the patient 's account in a telemedicine database . the real - time stress analysis algorithm , which includes the formulas and the hrv thresholds mentioned above , has been wholly developed by our research team . it may present as respiratory sinus arrhythmia , a natural periodic variation in hr , or it may be an index of a severe heart condition . an automatic device providing real - time arrhythmia detection and rhythm classification therefore has the potential to be highly useful in the daily monitoring of patients with heart conditions . the method described herein provides sinus rhythm type classification and detection and classification of different arrhythmia events using an approach based on rr intervals and instantaneous hr . the most immediate analysis provided by the system is classification of the supraventricular rhythm as normal , bradycardia , or tachycardia . the algorithm calculates the hravg during the observation window ( figure 3 ) , with the user at rest . it extracts the instantaneous hr of each packet and adds up to a total value hrtot . the hravg is then obtained by dividing the hrtot by the duration of the observation window , that is , the total number of packets received , and is compared with two thresholds : the rhythm is classified as bradycardia if hravg is < 60 beats per minute ( bpm ) , normal if is comprised between 60 and 100  bpm , and tachycardia if is > 100  bpm ( table 2 ) . it is a common supraventricular rhythm and is related to breathing : hr increases during inspiration ( bainbridge reflex ) and decreases during expiration ( vagal effect ) . our algorithm uses rr intervals ( figure 4 ) : since each packet contains k < 4 new beats , the relative rr intervals are calculated and compared with the actual rrmax and rrmin values and if necessary replaced with them . when the total number of packets received matches the predetermined observation window , the difference between the longest and the shortest rr intervals is calculated to check if it is > 0.16 seconds ( s ) . if this is true , sinus arrhythmia is considered to be present and a report is generated on the phone display . the sinus rhythm classification and sinus arrhythmia detection algorithms have been wholly developed by our team for real - time use . the method implemented in our device involves two steps : arrhythmia beat classification and arrhythmia episode classification . the episodes that can be classified include ventricular premature systole , ventricular bigeminy , ventricular trigeminy , ventricular couplet , ventricular tachycardia , ventricular flutter / fibrillation , and 2nd degree heart block . it is based on the algorithm described by tsipouras et al . ; we improved it adding real - time implementation . is based on a 3-rr interval window ( rr1 , rr2 , and rr3 ) that slides one beat at a time , where the rr interval to be classified is the middle one . this beat is initially considered as normal and may subsequently be assigned to another category based on three rules related to medical - clinical data . there are four categories : ( 1 ) normal sinus beat ( n ) ; ( 2 ) premature ventricular contraction ( pvc ) ; ( 3 ) ventricular flutter / fibrillation ( vf ) ; and ( 4 ) 2nd degree heart block ( bii ) . vf : it is activated if rr1 > 1.8rr2 and if the duration of rr2 is less than 0.6  s. in this case rr2 is considered as the beginning of a vf episode and subsequent windows are tested for the following two conditions : condition 1.1 : the duration of each rr - interval in the window is < 0.7  s;condition 1.2 : the overall duration of the rr1 + rr2 + rr3 window is < 1.7  s;if one of the two conditions is met the middle rr interval is classified as category ( 3 ) . the rule is based on classification of the whole vf episode , not a single beat , and is applied to m consecutive windows . a vf episode will be detected only if the m index of windows containing beats classified as category ( 3 ) is 4 . the algorithm continues with the next rule , coming back to the i  m window if neither condition is true for at least 4 consecutive windows or if rule 1 is not verified . condition 1.1 : the duration of each rr - interval in the window is < 0.7  s ; condition 1.2 : the overall duration of the rr1 + rr2 + rr3 window is < 1.7  s ; 
 rule 2 . pvc , category ( 2 ) : this state is present if one of the following conditions is true : condition 2.1 : if rr1 > 1.15rr2 and rr3 > 1.15rr2 , the rr2 interval is classified as an isolated pvc;condition 2.2 : if |rr1  rr2| < 0.3  s and both rr1 and rr2 are < 0.8  s and 1.2(rr1 + rr2)/2 < rr3 , then we have a pvc couplet;condition 2.3 : if |rr2  rr3| < 0.3  s and both rr1 and rr2 are less than 0.8  s and 1.2(rr2 + rr3)/2 < rr1 , then we have a pvc couplet 1.15rr2 , the rr2 interval is classified as an isolated pvc ; condition 2.2 : if |rr1  rr2| < 0.3  s and both rr1 and rr2 are < 0.8  s and 1.2(rr1 + rr2)/2 < rr3 , then we have a pvc couplet ; condition 2.3 : if |rr2  rr3| < 0.3  s and both rr1 and rr2 are less than 0.8  s and 1.2(rr2 + rr3)/2 < rr1 , then we have a pvc couplet . bii , category ( 4 ) : if conditions 3.1 and 3.2 are both true , the rr2 interval is assigned to this category : condition 3.1 : 2.2  s < rr2 < 3.0  s;condition 3.2 : |rr1 the rules are mutually exclusive and must be applied in the reported sequence . after a beat has been classified by one rule its classification can not be changed by another . condition 3.1 : 2.2  s < rr2 < 3.0  s ; condition 3.2 : |rr1  rr2| < 0.2  s or |rr2 rr3| < 0.2  s. the results of beat classification are used as input of the automaton ( figure 6 ) for detection and classification of the arrhythmia episode . the rhythm classification categories and relevant mit - bih symbols are normal sinus rhythm n , ventricular premature systole vps , ventricular bigeminy b , ventricular trigeminy t , ventricular couplet c , ventricular tachycardia vt , ventricular flutter / fibrillation vfl , and 2nd degree heart block bii . the default rhythm is normal , unless an episode of one of the above rhythms is detected . episodes start with a distinctive beat  for instance , a pvc beat is the first beat for b , t , and vt episodes ; vf for vfl episodes ; and bii for bii episodes  and are characterized by a specific sequence pattern ; they then end with a nonspecific beat . b has a pvc - n - pvc - n--n - pvc pattern , t has a pvc - n - n - pvc - n - n--pvc - n - n - pvc pattern , the pattern of vc is pvc - pvc - not pvc , and the pattern of vt is pvc - pvc - pvc--vc structure ( 3 consecutive pvcs ) . a major challenge to be met by the automaton is discrimination among these arrhythmia types , due to the similarity of the initial phase of each episode . vf episodes start with a vf beat and the number of consecutive vf beats determines its duration . the states of the automaton are as follows.state 1 : start of the automaton : the automaton remains in this state as long as the input is an n beat . it moves to states 2 , 7 , or 8 if a pvc , a vf , or a bii beat occurs , respectively.state 2 : possible b , t , and c and single vps or vt : the automaton goes to state 3 if the next beat is n ; to state 5 if the next beat is a pvc ; or to state 1 if the next beat is neither an n nor a pvc.state 3 : possible b and t and single vps : if the next beat is a pvc and b is detected the automaton returns to state 1 . if the next beat is n , it moves to state 4 ; if the beat is none of the above types , it moves to 1 and no episode is detected.state 4 : possible t and single vps : if the next beat is a pvc , a t is detected , whereas if the beat is n , a single vps is detected ; otherwise , nothing is detected and the automaton returns to state 1.state 5 : possible c and vt : if the next beat is not a pvc , c is detected and the automaton returns to state 1 ; if it is a pvc , the automaton moves to state 6.state  6 : vt is detected : if the beat is a pvc , the automaton remains in state 6 until the vt episode has ended . if the next beat is a vf or a bii , the automaton moves , respectively , to state 7 or 8 ; if it is different , it moves to state 1.state 7 : vfl is detected : if the next beat is a vf , the automaton remains in state 7 and the episode is considered to continue . in any other case the automaton returns to state 1.state 8 : bii is detected : if the next beat is a bii , the automaton remains in state 8 and the episode is considered to continue . in any other case the automaton returns to state 1.the automaton returns to state 1 , and the previous episode is considered as completed ( and is reported by the system if it has reached the minimum required hbn ) ; if an unexpected beat occurs , the new episode is ignored . state 1 : start of the automaton : the automaton remains in this state as long as the input is an n beat . it moves to states 2 , 7 , or 8 if a pvc , a vf , or a bii beat occurs , respectively . state 2 : possible b , t , and c and single vps or vt : the automaton goes to state 3 if the next beat is n ; to state 5 if the next beat is a pvc ; or to state 1 if the next beat is neither an n nor a pvc . state 3 : possible b and t and single vps : if the next beat is a pvc and b is detected the automaton returns to state 1 . if the next beat is n , it moves to state 4 ; if the beat is none of the above types , it moves to 1 and no episode is detected . state 4 : possible t and single vps : if the next beat is a pvc , a t is detected , whereas if the beat is n , a single vps is detected ; otherwise , nothing is detected and the automaton returns to state 1 . state 5 : possible c and vt : if the next beat is not a pvc , c is detected and the automaton returns to state 1 ; if it is a pvc , the automaton moves to state 6 . state  6 : vt is detected : if the beat is a pvc , the automaton remains in state 6 until the vt episode has ended . if the next beat is a vf or a bii , the automaton moves , respectively , to state 7 or 8 ; if it is different , it moves to state 1 . state 7 : vfl is detected : if the next beat is a vf , the automaton remains in state 7 and the episode is considered to continue . in any other case state 8 : bii is detected : if the next beat is a bii , the automaton remains in state 8 and the episode is considered to continue . in any other case the approach described herein requires knowledge of the user 's weight , height , gender , age , and resting hr . the algorithm used in our application is evolution of the algorithm developed by lee and coworkers   and exploits the computational abilities of current smartphones : hr is continuously monitored instead of being sampled at predetermined time points . the method developed by lee and coworkers requires entering resting hr , initial hr , final hr , and total duration of the analysis . this entails a number of limitations.the requirement of knowing total analysis duration and final hr precludes calculation in real time.it assumes that the final hr is the peak value in the entire exercise ; any pauses or final cooling down may thus affect the calculation.the previous method   makes integration to calculate energy consumption : three types of instantaneous energy consumptions are computed by functions that are linearly dependent on hr and are subsequently integrated over the total duration of the analysis . the method involves three calculation steps : calorie consumption at rest over the entire activity;calorie consumption after starting the exercise and up to the peak hr ( 10% of total duration);calorie consumption during maximum sustained hr ( 90% of total duration ) . the requirement of knowing total analysis duration and final hr precludes calculation in real time . it assumes that the final hr is the peak value in the entire exercise ; any pauses or final cooling down may thus affect the calculation . calorie consumption at rest over the entire activity ; calorie consumption after starting the exercise and up to the peak hr ( 10% of total duration ) ; calorie consumption during maximum sustained hr ( 90% of total duration ) . the method developed by our team analyzes 10-second windows and involves two steps : calculation of calorie consumption at rest;calculation of calorie consumption during exercise based on the mean hr sampled in the interval.the two steps provide the final calorie consumption value , which is updated every 10  s by adding the two new values . the equation for estimating energy expenditure at rest ( step ( i ) ) is 
 ( 1)k1i[kcal]=t[(hr1hr0 + 20.25)+c ]  (0.01808s+0.00895(1s ) ) , 
 where k1i is the energy consumption of the ith interval in kilocalories , hr0 is the hr at rest , hr1 is the hr at the beginning of analysis , c is a coefficient of basal metabolic rate ( per second in this case ) , s is a value depending of gender ( 1 for male , 0 for female ) , and t is the interval duration ( in s ) . calculation of calorie consumption at rest ; calculation of calorie consumption during exercise based on the mean hr sampled in the interval . the basal metabolic rate allows calculating energy expenditure based on user physical features : 
 ( 2)bmr=(655 + 9.6w+180h4.7a)(1s )  + ( 66 + 13.7w+500h6.8a)s , 
 where w is the user 's weight ( in kg ) , h is the height ( in m ) , and a is the age ( in years ) . the equation calculating calorie consumption per second of exercise in steps ( ii ) and ( iii ) is 
 ( 3)y[kcals]=[bm(hrxhr0)+c+0.3645]s  + [ bf(hrxhr0)+c+0.1812](1s ) , 
 where hrx is the hr at the start or at the end of exercise , whereas bm and bf are 
 ( 4)bm=0.0109lbmh20.0023%fat0.0007a0.0211,bf=0.014lbmh20.0012%fat0.01254 . the calculation involved in step ( iii ) is as follows ( step ( ii ) is not used in our method ) : 
 ( 8)k3i=(y(hri)y(hrs))t , 
 where hri is the mean hr value in the ith interval and t is interval duration . total k1 and k3 values are then added to obtain ktot : 
 ( 9)ktot=i=0n(k1i+k3i ) 
 assuming n as the windows number . the other functionalities developed by our team include a bluetooth connection , graphical interfaces , user settings , and alarm management . the bluetooth connection between smartphone and hr monitor is established using android bluetooth apis and the java library of the pulse sensor . the only requirement for the connection is pairing the two devices via the android settings menu . after selection of a user command that prompts the appearance of a connection button on the screen , the application searches for the device , which is listed in the bonded device list , and a bluetooth socket is established . finally , the required information is extracted from the packets and sent to the class assigned to the processing using the obtainmessage method ( handler class ) . the user can interact with the application through a main interface , a menu interface , and the settings interface ( figure 8) . the main interface contains buttons for device connection and disconnection via bluetooth and shows several items of information : the connection status , the battery status of the connected pulse sensor , the instantaneous hr , and the progress and results of current tests . it may also show the hr trend or the tachogram ( rr interval trend ) ; these charts are created using the achartengine library and implement zooming and scrolling functionalities . the menu interface allows activating / disabling the different analysis algorithms , clearing the chart , accessing the settings menu , viewing data history , and quitting . using the service class , a background part of the application reminds the user to perform any scheduled tests during the day . the settings interface makes it possible to enter user features ( age , weight , height , resting hr , and gender ) , set test duration , manage alert messages and calls ( activation , phone number , and preconfigured text messages ) , and schedule tests . finally hr , rr interval signals , and test reports can be saved to .txt files and to a local sqlite database , ready to be sent in json format to a telemedicine portal consisting of a mysql database and php web pages . the stress test was assessed in a trial involving 20 healthy individuals ( 6 female and 14 male university students aged 2327 years without neurological disorders ) who had abstained from coffee and alcoholic beverages for 12 hours . participants underwent analysis by our application for 10 minutes and the test results were not shown . the test data and the results of the questionnaire are reported in table 3 , where subjects are divided into stressed and nonstressed based on test results and the mean pss score is reported for the two groups . the same data are shown in figure 9 in a traditional box - plot , where the dots are the median values and the squares the mean values . the resulting t value is 4.49 , p = 0.0003 ( student 's t - test ) . the mean pss scores of the two groups were significantly different ( p < 0.01 ) . sinus rhythm classification and sinus arrhythmia detection were tested in 25 healthy students aged 2329 years . the algorithm output simply described the subject 's cardiac rhythm and whether he / she presented sinus arrhythmia . the ecg tracings were assessed by a cardiologist and compared to the test results reported by our application . the two sets of test reports matched , demonstrating that the algorithm is 100% accurate in sinus rhythm classification and sinus arrhythmia detection . as regards the sinus rhythm , 18 participants had a normal rhythm , 5 had a bradycardic rhythm , and 2 had a tachycardic rhythm . moreover , the detection and classification of arrhythmia events have previously been tested by tsipouras and coworkers , who found very high sensitivity and specificity . we tested our real - time java implementation by an offline simulation using as input the values available on the mit - bih database rather than the signal acquired from the heart monitor . we found 94% accuracy in detection and classification of arrhythmia episodes , the same result obtained by tsipouras et al . . the energy estimation algorithm described herein was compared with the method used by garmin ltd . this method was chosen as the reference because garmin is widely considered a leader in its sector . in addition a large amount of data that can be used for comparisons is available on the garmin connect platform . for this test we used offline implementation of our algorithm that simulates real - time device operation . the algorithm was applied to 10 different hr acquisitions relating to 10 subjects and three different types of activity ( available on the garmin connect web platform ) . the results obtained by our device are very close to those obtained by garmin 's method ( figure 10 ) . since the test used merely indicative information about user activity levels , the results are quite acceptable . to do this we administered to the 20 participants involved in stress test validation an additional questionnaire , the 10-item system usability scale ( sus ) , which is a platform - independent model widely used in subjective software evaluation . the mean values , converted values , and standard deviation ( std ) for each question as well as total sus scores are reported in table 6 . the total per cent score is 75.625 , which corresponds to a b grade and is therefore a good result . the std values are quite small , entailing narrow spread around the mean . in more general and objective terms , users and patients who have used the application during the test expressed their satisfaction in the use . importantly , prolonged use of the application did not evidence bugs or unexpected system crashes . this paper describes a real - time heart monitoring system suitable for a variety of users including elderly individuals and patients with cvd . the system is based on a simple , inexpensive , and wearable device whose functionalities are comparable to those of more expensive and sophisticated clinical devices . the system 's high - level performances are related to a set of complex algorithms that have been implemented in the android device . the stress test , heart rhythm classification , and sinus rhythm detection algorithms have been developed entirely by our group , whereas the arrhythmia classification and detection algorithm and the algorithm evaluating energy expenditure employ known methods that were improved to suit the real - time and mobile conditions of the present application . the stress test and the arrhythmia algorithms proved to be able to identify a stress state and episodes of arrhythmia . clearly , the computer software can not replace a physician , but it does provide information and alerts . the energy expenditure algorithm calculates calorie consumption in real time ; it is based on an existing method that was improved by our team and extended to cover a number of general conditions ( exercise with breaks , exercise with cooling down , and normal daily activities ) . these features make the device suitable for users who need to monitor calorie consumption during the day due to health reasons . an additional novel feature of the device is that it integrates three different algorithms for hr and tachogram processing in a single mobile application that ( i ) provides remote monitoring of patients outside the hospital , ( ii ) is easy to use , and ( iii ) provides clear test reports to users and medical staff . all the data about the patient 's health status can be stored in an online database . the hrv and arrhythmia algorithms provide useful information about patients with a range of diseases . for example it has been reported that there may be a close association between reduced hrv and the severity of parkinson 's disease ( pd ) . indeed , pd affects the diurnal autonomic cardiovascular regulation , whose alteration seems to be more profound in patients with more severe disease . the result of these changes is a reduced hrv , but current knowledge of the long - term consequences of certain hr features and hrv , for example , circadian regulation , is limited . we are currently collecting heart activity data from pd patients at the regional hospital department of rehabilitation ( ancona , italy ) with a view both to validate our system and to test the hypothesis of a correlation between pd and abnormal hrv . we are also testing the algorithms developed for our device and the scope for its application to a number of conditions and uses in view of its marketing in the near future .
the current trend in health monitoring systems is to move from the hospital to 
 portable personal devices . 
 this work shows how consumer devices like heart rate monitors 
 can be used not only for applications in sports , but also for medical research and diagnostic 
 purposes . 
 the goal pursued by our group was to develop a simple , accurate , and inexpensive 
 system that would use a few pieces of data acquired by the heart rate monitor and process them on 
 a smartphone to ( i ) provide detailed test reports about the user 's health state ; ( ii ) store report 
 records ; ( iii ) generate emergency calls or smss ; and ( iv ) connect to a remote telemedicine portal 
 to relay the data to an online database . 
 the system developed by our team uses sophisticated 
 algorithms to detect stress states , detect and classify arrhythmia events , and calculate energy 
 consumption . 
 it is suitable for use by elderly subjects and by patients with heart disease ( e.g. , 
 those recovering from myocardial infarction ) or neurological conditions such as parkinson 's 
 disease . 
 easy , immediate , and economical remote health control can therefore be achieved 
 without the need for expensive hospital equipment , using only portable consumer devices .