GreenMonitor: Extending battery life for continuousheart rate monitoring in smartwatches

Shravan Aras, Thienne Johnson, Kristoffer Cabulong, Chris GniadyDepartment of Computer Science

University of Arizona, Tucson, AZ, USA{shravanaras, thienne, kcabulon, gniady}@email.arizona.edu

Abstract—Health monitoring applications using smart devicesare becoming increasingly popular due to an expanding numberof new devices available and growing affordability of such devices.Smartwatches provide a new way to acquire data for heart rateand activity levels via accelerometers, gyroscopes, and other built-in sensors, which can enable a full range of health applications toimprove users’ lives. However, continuous heart rate monitoringcan significantly reduce the operating time of a smartwatch,reducing the applicability for continuous monitoring. We proposeGreenMonitor to extend the operating time of a smartwatch whilemaintaining accuracy of heart rate monitoring by leveraging thecorrelation between heart rate and activity level changes indicatedby the accelerometer data. Through detailed implementation andevaluation we show that GreenMonitor can save 26% energy,on average for our traces, while maintaining accurate physicalactivity tracking and evaluation.

I. INTRODUCTION

Lack of physical activity can have a negative impact onhealth in adults as well as children. Children are especiallyat high risk of sedentary lifestyles as an array of games andonline social activities can replace physical activity and socialinteractions. This sedentary lifestyle may continue into adult-hood, significantly degrading an individuals health. Howeverthe exact level of activity that each individual performs is hardto judge without detailed monitoring. Better monitoring and as-sessment of physical activity can lead to better healthy lifestylemanagement [7] and may enrich traditional interventions toprevent and/or manage chronic conditions, for which regularexercise is prescribed, such as cardiovascular and pulmonarydiseases, obesity, and diabetes [8].

Emergence of smartphones that are able to track activitylevels have resulted in the creation of numerous fitness andhealth apps, such as Endomondo, My Fitness Pal, and SamsungS Health. Users of these apps are able to track their activitiesand receive rewards for completing their daily goals. Userscan augment the monitoring by including heart rate moni-toring chest straps and wrist straps to improve assessmentaccuracy and detail. The external monitors communicate withthe smartphone through Bluetooth providing an integratedmonitoring system. Recent popularity of smartwatches canenable less intrusive long-term monitoring of physical activityby combining hart rate monitors and accelerometers in onepackage. This additional functionality of smartwatches makesthem very attractive for users to track their activities and heartrate continuously throughout the day, allowing them to assesstheir general level of fitness as they go about performing theireveryday activities.

However, activity and heart monitoring can be very taxing,reducing the operating time of a smartwatch, which can makeactivity monitoring more obtrusive. Improving activity andheart monitoring energy efficiency is therefore critical for im-proving the usability of smartwatches for long-time continuousphysical activity and heart monitoring. Many optimizationshave been proposed for smartphones to improve energy ef-ficiency of health applications, however none currently existfor smartwatches. This paper focuses on that and is the firstone targeting energy optimization for smartwatches in healthmonitoring applications. The contributions of this paper are: 1)We quantify energy distribution for health monitoring devicesand find that optical heart rate monitoring hardware demandsthe highest power among all other components, such asscreen, Bluetooth, and accelerometer; 2) we show correlationof changes in physical activity tracked via the smartwatchto heart rate changes; 3) we exploit the above correlation toreduce energy consumption associated with continuous heartmonitoring.

II. MOTIVATION

Smartwatches are gaining popularity as health monitoringand/or activity tracking devices, encouraging users to leada more active lifestyle. As smartwatches such as the Nike+SportWatch, Fitbit, JawBone, Basis, and Microsoft Band areworn on the wrist by users, they can take advantage of thisinformation to more accurately track certain activities whencompared to smartphones which usually reside in a user’spocket. Because smartwatches are constantly in contact withthe user’s skin, they can also be used for continuous monitoringof vital signs, like heart rate. However, given the small physicalsize of these devices, they are constrained by their small batterycapacity when it comes to continuous monitoring. This putsa huge burden on smartwatch OS and application developersto come up with better energy efficient algorithms for activitytracking to prolong the battery life of the device [21].

a) Power profiling: We have selected a SamsungGalaxy Gear Neo 2 watch for our evaluation. Figure 1presents the hardware setup used for profiling. The watchwas connected directly to a power supply and the batterywas removed to eliminate variability of the battery voltageas it discharges. We used National Instruments PCI-6230digital acquisition board to measure voltage drop across theresistor and calculated the resulting power. To measure powerconsumed by individual components we first measured thebaseline power of the watch in an idle state, with all possiblesystems disabled and the screen off. We found this baseline

Resistor

PCI-6230 Power Supply

Fig. 1: Profiling hardware.

power to be 2.45 mW. Next we independently enabled variouscomponents inside the watch, subtracting the total power fromthe baseline to determine the power consumption for eachindividual component and any processing power required bythat component. We report these numbers in Table I.

Component Power(mW)Accelerometer 13.63

Screen 18.2376Bluetooth 4.64

Heart Rate Monitor 51.60

TABLE I: Power Breakdown.

We can see that the optical heart rate monitoring hardwareconsumes the most power among all other components. To putthese numbers into perspective, the capacity of the battery in-side the watch is 300mAh. In a typical continuous monitoringapplication, both the heart rate monitor and bluetooth wouldremain on, to collect heart rate data and then transmit it to thephone, utilizing a total power of 58.7 mW. Thus the batterywould last only for 20 hours - not even a full day. Further,the heart rate monitor hardware requires around 3.7 timesmore power to operate when compared to accelerometers. Thisdrastic skew in power requirements motivated us to exploreways in which we can use the accelerometer data to reducethe amount of time for which the heart rate monitor remainson.

b) Physical Activity vs. Heart Rate: Since continuouslyreading the heart rate monitor is very expensive, we needto reduce the frequency of heart rate readings in order tosave energy. However, reduced sampling rates can reduce theaccuracy of heart rate readings, potentially making it unsuitablefor fitness assessment. Subsequently, we need to be veryselective as to when to we can safely reduce sampling withoutintroducing inaccuracies. Fortunately, there is a relationshipbetween heart rate and activity intensity [27], [2] and howquickly it changes with the activity intensity [6]. The keyobservation that we exploit in this paper to improve energyefficiency is that change in physical activity will change thesubjects heart rate. Therefore, we need to accurately monitorheart rate when activity levels change, but sampling can beless frequent when the activity level remains constant.

III. GREENMONITOR: ENERGY OPTIMIZED HEARTMONITORING

The key idea of dynamically varying sampling rate basedon intensity of user activity is very intuitive, however it facesseveral challenges that we need to address to provide energyefficiency while maintaining the accuracy of monitoring. (1)The activities detection algorithms have to be computationallyefficient. Inefficient algorithms will increase computationalenergy, potentially negating all benefits of the optimization.(2) The algorithms have to be adaptive to automatically adjust

to different users with different fitness levels, as fit users willnot see significant changes during walking, for example, whilethose that are relatively less fit may see significant changes.Subsequently, the proposed energy efficient heart monitoringhas to be adaptive, energy efficient, and transparent to usersso it does not require any user input and adjustment.

A. Efficient Activities Detection Algorithm

While it is possible to determine whether a user is walking,sitting, running, etc., it can be computationally expensive [19],[20], [13], and may not be necessary for accurate heart ratemonitoring. We follow the intuition that activity changeswill result in changes in heart rate. This makes detection ofactual activity unnecessary only detection of activity intensitychanges which result in heart rate changes are required. Wepropose correlating heart rate changes to changes in intensityof accelerometer readings without the need to specificallyclassify the activity. Sudden changes in activity intensity willrequire higher sampling to account for rapid changes in heartrate, while steady activity level or a gradual change in activityintensity will require a lower sampling rate yet still accuratelycapture the heart rate. This optimization will provide bothdecreased energy expenditure during stable heart rate periodsand high accuracy during changes in heart rate.

Accelerometers in the smartwatch provide all necessarydata to detect changes in user activity. Figure 2 shows thesechanges in acceleration (activity) and the heart rate for oneof our studied subjects. We apply a moving average filterwith a window size of 30 seconds on our acceleration datato eliminate noise thus allowing us to better visualize the data.The heart rate data is used as is, without any prior processing.Figure 2 shows the subject performing 5 sets of activities. Eachset consists of 3 activities, annotated respectively as sitting (S),walking (W) and running (R). These activities were chosenso as to give us a broad overview of heart rate variationwith changes in physical activity intensity. Sitting helps usdetermine the baseline values for an individual’s heart rate,while transition from sitting to walking helps us understandactivities which involve a gradual change in activity intensity.On the other hand transitions to and from running allow usto study variations in heart rate on the onset of rapid changesin activity intensity. We can observe that when the subjecttransitions from a physical activity of lesser intensity likewalking to that of a higher intensity like running, there isa distinguishable change in the acceleration magnitude. Thechanges in heart rate closely follow the changes in the activity.On the other hand, when transitioning back to a lower intensityactivity such as from running to walking or sitting, the changein acceleration is instantaneous, while the heart rate decreasesgradually. We observed exactly the same trends in all subjectsin our study. This strong indication of correlation will allowus to automatically detect and correlate activity changes to thechanges in heart rates in individual subjects.

B. Adaptive Correlation and Training

The training phase selects features from the accelerationdata which can then be used to predict heart rate variations andchange the sampling rate. Figure 4 illustrates the idea behindour training algorithm. We use a windowing technique to breakdown both the heart rate and acceleration data into smaller

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Fig. 2: Changes in heart rate closely follow changes in acceleration.

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Fig. 4: Windowing algorithm used during the training phase.

chunks, in order to establish a correlation between them. Weobserved from our experiments that a window size of 60seconds over the heart rate data was small enough to accommo-date for rapid changes in heart rate due to changes in physicalactivity, while being large enough to remain unaffected by anytemporal changes caused by emotional fluctuations. Figure 4shows an example of a heart rate rising marked by a windowW . As we desire to correlate variations in heart rate withchanges in acceleration, we calculate the absolute differencebetween the maximum and minimum heart rate values withinthis window. However, physiologically our heart rate varieseven during rest when we speak, breath, experience a changeof emotions etc [3]. As these changes are not a result ofphysical activities, we filter them out. We do so by setting athreshold value and ignoring the window if the change in heartrate is below this threshold. In our current implementation wehave used a threshold value of 20 BPM, after observing that avariation of 20 BPM was common during a 60 second periodfor our subjects when they were at rest.

Next for each heart rate window, we split the accelerationdata into 2 windows denoted by A1 and A2 as shown inFigure 4. The first window, A1 contains acceleration values30 seconds before the start of the heart rate window, while thesecond contains acceleration values 30 seconds after the startof the heart rate window. In order to get an overview of theacceleration data across these 2 windows we find the meanacceleration values in both these windows. Next we calculatethe absolute difference between the mean acceleration valuesof A1 and A2. This gives us the average change in accelerationbetween these two windows, which translates to change inphysical activity intensity. We refer to this value as Adelta.Further we observed during our experiments that a window sizeof 30 seconds was small enough to see changes in accelerationwhen transitioning between various physical activities whileeliminating noise due to sudden sporadic moments like flickingof a wrist or typing on a keyboard.

In order to further eliminate anomalies which would oth-erwise affect the accuracy of our algorithm, we select a pairof acceleration windows only if its Amean is smaller than apredefined threshold. This is required as there are times whensudden excitement or other emotional changes can lead tovariations of more than 20 BPM in the heart rate window(W in our example figure) in the absence of any change inphysical activity. However, we observed that these changeswere gradual, while those observed during physical activitieslead to a relatively step changes in the heart rate. On furtherobservation we selected this threshold value to be 2m/s2. Fi-nally we slide both our heart rate and its respective accelerationwindows across our training data to find the minimum valueof Adelta. This minimum value Adelta min is the sole outputof our training phase and is subsequently stored and used byour prediction phase.

Throughout the training phase we perform only simple

computation operations. The feature selections involve onlycalculating means and differences. As the only data we need tostore throughout our training phase is a single value which in-dicates the minimum change in acceleration for rapid changesin heart rate, indicated by Adelta min we also maintain anegligible memory footprint. This helps us implement ourapproach on a smartwatch without slowing it down or havinga negative impact on the overall energy savings.

C. Heart Rate Monitoring and Prediction

GreenMonitor’s prediction phase has low overhead and canrun continuously on the watch without significantly impactingthe device’s performance or energy consumption. GreenMon-itor divides the acceleration data into simple 30 second win-dows, and calculates the difference between mean values for2 consecutive acceleration windows which we have referredto as Amean. We do not use sliding window during normaloperations to reduce monitoring overhead even further. Bydefault we sample heart rate every 1 minute. However, if weencounter a pair of acceleration windows for which Adelta

is greater than Adelta min (the value returned to us by ourtraining phase), GreenMonitor increases the sampling rate tothe maximum rate supported by the device, allowing it toaccurately capture changes in heart rate. Once the intensiveactivity stops, a timeout mechanism brings the sampling rateback to its original value of sampling every minute. Forour implementation we use a timeout window size of 60seconds that captures the heart rate during cool down periods(for example sitting after running). To keep our algorithm incheck we use an accounting method to thwart false positives,awarding points when the system correctly predicts the onset ofheart rate change due to physical activity and subtracting themon wrong predictions. When the algorithm runs out of balance,it must start the training phase again. As mentioned previouslythis occurrence is rare as it takes a significant amount of time,of the order of several months to change how the heart reacts tophysical activities. It is challenging to detect the occurence offalse negatives, where GreenMonitor which relies on changesin accelerometer values fails to account for sudden changes inheart rate. However these changes in heart rate values, withoutany corresponding change in physical activity intensity can beaccounted for by changes in emotional state or certain medicalconditions. As our work is primary focused on physical fitnesswe do not focus on these anomalies.

Figure 3 shows how GreenMonitor samples heart ratevalues for the same subject whose data was shown in Figure 2during its training and prediction phase. The first 2 setsof data, labeled as training in the figure are used to trainGreenMonitor, while the remaining 3 are used for prediction.During the training period GreenMonitor continuously samplesheart rate values in order to generate the Adelta min. Duringthe prediction phase, GreenMonitor is able to dramaticallyreduce the sampling rate of the device when there is nosignificant change in heart rate and increase the sampling rateon the onset of intense physical activities. We can see fromthe figure that rapid changes in activity intensity such as goingfrom walking to running, led to an increase in sampling rateto accurately capture the rise in heart rate. However, switchingfrom sitting to walking, which results in a gradual change inheart rate values (depending on a persons physical fitness) doesnot warrant a sudden increase of sampling rate. This allows

Subject Total Activity (min) Prediction Time (min)Sitting Walking Running Sitting Walking Running

1 20.49 16.97 17.04 10.29 8.7 10.992 22.86 8.99 12.78 13.02 1.55 7.693 14.69 18.46 7.5 5.26 7.15 5.354 15.52 12.27 10.47 7.12 5.46 6.325 12.74 8.14 16.27 5.16 4.07 8.42

TABLE II: Trace statistics showing total time for each subjectperforming various activities, and the amounts of time usedfor prediction.

GreenMonitor to conserve energy while not compromisingheart rate monitoring accuracy.

IV. EXPERIMENTAL RESULTS

For our experiments we collected traces from 5 healthyadults between 20-40 years of age. Each subject was asked toperform 5 sets of activities with each set containing a mixtureof sitting, walking and running. The time distribution foreach subject and activity is shown in Table II. The predictioncolumn shows the amount of trace data that was used inprediction to validate our model, while the total trace datastatistic shows the entire activity distribution used for bothtraining and prediction. We initially used the Samsung GalaxyGear Neo 2 watch for collecting both acceleration and heartrate data. However, we observed that during intense physicalactivities like running, the watch often gave incorrect heart ratevalues [17]. Hence we used a Zephyr HxM heart rate monitorband to augment the acceleration data that we got from thewatch using timestamps from each device to match the data.However our system is designed to be run on the minimalhardware found in smartwatches and can be run without anymodifications when heart rate sensors on smartwatches becomemore accurate, a domain which is under active research [1],[28]

To allow extensive study of prediction models and toevaluate them, we developed a simulator in MATLAB. Weused the first 2 sets of data of each subject for training, whilethe later three were used in the prediction phase to validateour model. The simulator was designed to model power ofsystem components shown in Table I based on the samplingrate chosen by the prediction phase.

A. Energy

Figure 5 shows the amount of energy consumed by Green-Monitor for each subject. The energy was normalized to theenergy consumed by the watch without running GreenMonitor,utilizing continuous sampling (labeled as Continuous). Totalenergy expended during activities is distributed between theheart rate monitor (Sitting HM, Walking HM, and RunningHM) and the accelerometer in case of GreenMonitor. It is clearthat GreenMonitor trades part of the energy used by heart ratemonitor for continuous accelerometer monitoring and samplesthe heart rate monitor continuously only when indicated by thepredictor . As we can see in Table I the energy consumed bythe accelerometer is approximately one-fourth that of the heartrate monitor and as a result, GreenMonitor is able to save 26%energy on average across the 5 subjects in our combination ofactivities.

The greatest energy savings are achieved by GreenMonitorwhen users are sitting as the heart rate does not change

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Fig. 5: Normalized energy distribution comparison of Green-Monitor and continuous sampling.

frequently and can be sampled at a much lower frequencywhile not compromising accuracy. On the other hand, stren-uous activities such as running demand continuous hart ratemonitoring as the values may change frequently, and in thissituation GreenMonitor consumes the same amount of energyfor the heart monitoring hardware as in standard continuousmonitoring. We observe this as a common trend across all oursubjects. Similarly, walking does not introduce any significantvariations in heart rate and thus the energy efficiency of thewatch can be further improved by GreenMonitor.

Another important physiological factor which affects theamount of energy GreenMonitor can save is the heart’s reactiontime. Reaction time is how fast a person’s heart rate can returnback to normal once a physical activity has been completed,and it depends on the person’s fitness level. If a person hasa rather long reaction time, then GreenMonitor will needto sample longer at a higher frequency to fully capture thechange after the physical activity has finished. Since we askedall our subject to sit and rest after running, GreenMonitorcontinued to sample continuously until the heart rate stabilized,while the subjects were at rest. This explains why the energysavings in sitting were lower than walking for our subjects.However, the opposite is seen for subject 3, who misreadour directions and reversed walking and running activities. Inthis case, the subject was running first and continued walkingbefore sitting down and resting. As a result, the heart recoverytime is capture by walking period and largest energy savingsare achieved during sitting. Thus recovery time is accountedfor by continuous monitoring during the walking phase forsubject 3 resulting in higher energy consumption for walkingas compared to other subjects. However we argue that heartrecovery time and the associated energy are negligible over thecourse of a complete day as users do not frequently changeactivity levels.

Thus the amount of energy saved by GreenMonitor willvary from person to person and also depend on the type ofactivities they perform during the day. According to a survey

Total Activity Energy Battery run timeNon-Strenuous Strenuous Savings extension over 20 hours

23.5 hours 0.5 hour 54.8% 10.7 hours

TABLE III: Battery time and energy saving improvement forGreenMonitor when we consider daily activity time distribu-tion for healthy adults.

conducted by Mathew et.al. [15] in 2003-2004 studying 6,329participants, adults in United States spent on average 7.7 hoursof their work day performing sedentary activities. This wouldprovide GreenMonitor with significantly large opportunity toconserve energy while accurately measuring changes in heartrate when users transitioned from being sedentary to mobile.To get a broad overview, we show the amount of energyGreenMonitor can ideally save throughout the course of a 24hour day in Table III.

According to the American Heart Association, an adultis recommended to perform 30 minutes of cardiovascularexercises such as running each day. We assumed that for theremaining of day the user would perform non-strenuous taskslike sitting, sleeping or walking. Thus if GreenMonitor is onlyrequired to sample at full during these strenuous periods andcan sample every 1 minute otherwise, it can save a maximumof 53.81% energy when compared to the standard monitoringapproach using continuous sampling. These energy savingswould account for an increase of 10.76 hours in the batterylife of the watch, bumping it up from 20 hours to 30.76 hours,thus enabling it to last for more than a day. Hence we observethat GreenMonitor can save a considerable amount of energyby exploiting periods of non-strenuous activities, which greatlyovershadow those of high intensity activities in everyday livesof health adults.

V. RELATED WORK

In the past both automatic and manual methods have beenadopted for physical activity assessment. Manual method haveused a combination of diaries, questionnaires, surveys, clinicalobservations, functional tests, heart rate monitoring etc. [23]while automated methods have employed various sensors suchas GPS, accelerometers [23] and heart rate monitors [9].Accelorometers have been shown to be extremely efficientin correlating physical activities with acceleration values [7],[8]. Subsequently, accelerometers and heart rate monitors havebeen combined to assess physical activity [10], [24] usingclassification algorithms [8].

Recently, mobile sensors have been used for severaleHealth monitoring applications [11], such as monitoring res-piratory rate, posture, steps, and falls [4]. Wrist-worn motiondetectors also enable new monitoring applications, such asdetection of epileptic seizures [14] and stress [5]. Heart ratemonitoring can be used to measure anomalous heart rates,heart rate irregularities (arrhythmias) or blockages, and post-processing of the data can be used to verify trends or singleevents, providing information vital for an accurate patientdiagnosis [22][12]. In addition, heart rate monitoring maybe used as an indicator of activity level when assuming arelationship between activity intensity and heart rate [27],[2]. Heart rate monitoring appears to be sufficiently valid foruse in creating broad physical activity categories (e.g. highlyactive, somewhat active, sedentary) [26]. While it is possible to

monitor physical activity using only heart rate data - the mainproblem lies in dealing with different fitness levels betweenusers, which naturally results in different heart rate valuesfor the same activity [23], thereby presenting the challengeof creating one classification dataset for all users. Finally,observations in increases / decreases in HR on activity changes,allows a person to assess their level of physical fitness [6].

Efforts for energy conservation in the past have beenlargely focused on smartphones. Misra and Lim [16] used anasynchronous querying technique to save 70% energy. Pandeet al [18] proposed the use of smartphone sensors at reducedfrequency for detecting Energy Expenditure Estimation. On theother hand the work by Wang et al [25] offload data processingto the cloud for better energy management. Finally, the authorsare not aware of any works examining energy expenditure ofsmartwatches for health monitoring applications. We believeour contribution can lead to new energy management proposalsin this research area.

VI. ACKNOWLEDGMENTS

This material is based upon work supported by the NationalScience Foundation under Grant No. 0844569.

VII. CONCLUSIONS

We have shown in this paper that it is possible to utilizeauxiliary sensors such as accelerometers in smartwatches tosignificantly improve the energy efficiency of heart monitor-ing applications while maintaining high accuracy. To exploitthis observation we proposed GreenMonitor that learns andcorrelate changes in user activity levels to changes in aheart rate. This correlation allows GreenMonitor to adjust thesampling rate of the heart rate monitoring hardware based onactivity level resulting in over 50% saving in energy duringdaily activity monitoring and extending the battery live bymore than 10 hours as compared to the continuous heart ratemonitoring by the heart rate monitor. This significant increasein device usage time can allow users to monitor their physicalactivities for extended periods of time when compared tonaively tracking user activity in a continuous manner.

REFERENCES

[1] G. Appelboom, E. Camacho, M. E. Abraham, S. S. Bruce, E. L.Dumont, B. E. Zacharia, R. DAmico, J. Slomian, J. Y. Reginster,O. Bruyere, et al. Smart wearable body sensors for patient self-assessment and monitoring. Archives of Public Health, 2014.

[2] T. V. Barreira, M. Kang, J. L. Caputo, R. S. Farley, and M. S. Renfrow.Validation of the actiheart monitor for the measurement of physicalactivity. Int J Exerc Sci, 2009.

[3] J. F. Brosschot and J. F. Thayer. Heart rate response is longer after neg-ative emotions than after positive emotions. Int J of Psychophysiology,2003.

[4] A. M. Chan, N. Selvaraj, N. Ferdosi, and R. Narasimhan. Wirelesspatch sensor for remote monitoring of heart rate, respiration, activity,and falls. In EMBC, 2013.

[5] J. Choi and R. Gutierrez-Osuna. Using heart rate monitors to detectmental stress. In Wearable and Implantable Body Sensor Networks.IEEE, 2009.

[6] C. R. Cole, E. H. Blackstone, F. J. Pashkow, C. E. Snader, and M. S.Lauer. Heart-rate recovery immediately after exercise as a predictor ofmortality. New England Journal of Medicine, 1999.

[7] M. Dencker, T. Tanha, P. Wollmer, M. Karlsson, L. B. Andersen, andO. Thorsson. Tracking of physical activity with accelerometers over atwo-year time period. Physical Activity & Health, 2013.

[8] F. Garcıa-Garcıa, G. Garcıa-Saez, P. Chausa, I. Martınez-Sarriegui, P. J.Benito, E. J. Gomez, and M. E. Hernando. Statistical machine learningfor automatic assessment of physical activity intensity using multi-axialaccelerometry and heart rate. In Artificial Intelligence in Medicine.Springer, 2011.

[9] M. J. Gregoski, M. Mueller, A. Vertegel, A. Shaporev, B. B. Jackson,R. M. Frenzel, S. M. Sprehn, and F. A. Treiber. Development andvalidation of a smartphone heart rate acquisition application for healthpromotion and wellness telehealth applications. International journalof telemedicine and applications, 2012.

[10] H. P. Johansson, L. Rossander-Hulthen, F. Slinde, and B. Ekblom.Accelerometry combined with heart rate telemetry in the assessmentof total energy expenditure. British journal of nutrition, 2006.

[11] T. Johnson, E. Cardozo, and E. G. Guimaraes. Pervasive computingapplications, technologies, and challenges for e-health. DesigningSolutions-Based Ubiquitous and Pervasive Computing: New Issues andTrends, 2010.

[12] W. M. Jubadi and S. F. A. Mohd Sahak. Heartbeat monitoring alert viasms. In ISIEA, 2009.

[13] J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognitionusing cell phone accelerometers. ACM SIGKDD, 2011.

[14] J. Lockman, R. S. Fisher, and D. M. Olson. Detection of seizure-likemovements using a wrist accelerometer. Epilepsy & Behavior, 2011.

[15] C. E. Matthews, K. Y. Chen, P. S. Freedson, M. S. Buchowski, B. M.Beech, R. R. Pate, and R. P. Troiano. Amount of time spent insedentary behaviors in the united states, 2003–2004. American Journalof Epidemiology, 2008.

[16] A. Misra and L. Lim. Optimizing sensor data acquisition for energy-efficient smartphone-based continuous event processing. In MDM.IEEE, 2011.

[17] J. Nikolic-Popovic and R. Goubran. Impact of motion artifacts onheart rate variability measurements and classification performance. InMeMeA. IEEE, 2013.

[18] A. Pande, Y. Zeng, A. K. Das, P. Mohapatra, S. Miyamoto, E. Seto,E. K. Henricson, and J. J. Han. Energy expenditure estimation withsmartphone body sensors. In Body Area Networks. ICST, 2013.

[19] J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, andI. Korhonen. Activity classification using realistic data from wearablesensors. Information Technology in Biomedicine, 2006.

[20] D. M. Pober, J. Staudenmayer, C. Raphael, P. S. Freedson, et al.Development of novel techniques to classify physical activity modeusing accelerometers. MSSE, 2006.

[21] R. Rawassizadeh, B. A. Price, and M. Petre. Wearables: has the age ofsmartwatches finally arrived? COMMUN, 2014.

[22] L. Scalise. Non contact heart monitoring. INTECH Open AccessPublisher, 2012.

[23] D. Stankevicius and V. Marozas. A brief review of accelerometry andheart rate measurements based physical activity monitoring. BiomedicalEngineering, 2013.

[24] E. M. Tapia, S. S. Intille, W. Haskell, K. Larson, J. Wright, A. King,and R. Friedman. Real-time recognition of physical activities and theirintensities using wireless accelerometers and a heart rate monitor. InWearable Computers. IEEE, 2007.

[25] X. Wang, Q. Gui, B. Liu, Y. Chen, and Z. Jin. Leveraging mobilecloud for telemedicine: a performance study in medical monitoring. InNEBEC, 2013.

[26] A. Waninge, A. van der Putten, R. Stewart, B. Steenbergen, R. vanWijck, and C. van der Schans. lsch. Measuring physical fitness, 2011.

[27] M. Yuchi and J. Jo. Heart rate prediction based on physical activityusing feedforwad neural network. In Convergence and Hybrid Infor-mation Technology. IEEE, 2008.

[28] Z. Zhang, Z. Pi, and B. Liu. Troika: A general framework for heartrate monitoring using wrist-type photoplethysmographic signals duringintensive physical exercise. BME, 2015.