Using Accelerometer Data toEstimate Surface Incline and ItsWalking App Potential

Ilyas UyanikComputational Physiology LabUniversity of HoustonHouston, TX 77204 USAiuyanik@uh.edu

Muhsin UgurComputational Physiology LabUniversity of HoustonHouston, TX 77204 USAmugur@uh.edu

Ashik KhatriComputational Physiology LabUniversity of HoustonHouston, TX 77204 USAarkhatri@uh.edu

Dvijesh ShastriUniversity of Houston -Downtown CampusHouston, TX 77002 USAshastrid@uhd.edu

Dinesh MajetiComputational Physiology LabUniversity of HoustonHouston, TX 77204 USAdmajeti@uh.edu

Ioannis PavlidisComputational Physiology LabUniversity of HoustonHouston, TX 77204 USAipavlidis@uh.edu

Permission to make digital or hard copies of part or all of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bearthis notice and the full citation on the first page. Copyrights for third-partycomponents of this work must be honored. For all other uses, contact theOwner/Author. Copyright is held by the owner/author(s).CHI’15 Extended Abstracts, Apr 18–23, 2015, Seoul, Republic of Korea.ACM 978-1-4503-3146-3/15/04http://dx.doi.org/10.1145/2702613.2732764

AbstractWalking is a fundamental human activity and itsdiminution a potential morbidity factor. Recentdevelopments in mobile computing have enabledubiquitous monitoring of walking activity via thesmartphone accelerometers. Typically, walking apps mapaccelerometer values to caloric values through calibrationalgorithms. However, these calibration algorithms assumea flat surface, which is not always true and can introducesignificant errors. In this paper, we outline a novelcalibration method that estimates surface inclination foruphill walking, thus, improving the caloric estimation inwalking apps.

Author KeywordsInclination measurement; energy expenditure; walkingactivity; walking activity monitoring; smartphoneaccelerometer; iPhone

ACM Classification KeywordsH.5.m [Information interfaces and presentation (e.g.,HCI)]: Miscellaneous.

IntroductionSedentary lifestyle is linked to the onset of chronic diseasessuch as diabetes and cardiovascular ailments [10]. One ofthe factors contributing to sedentary lifestyle is diminution

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of walking. Walking is becoming less pervasive in ourdaily routine. Many walking monitoring apps are availableto make the users aware about their walking behavior.Typically, these apps estimate energy expenditure bymapping the smartphone accelerometers’ readings tocaloric values [4][5][6][8]. The mapping assumes that thewalking surface is leveled. This assumption is valid forwalking scenarios in cities established in plain fields.However, for cities situated on hilly (e.g., San Francisco,CA) or mountainous grounds the mapping algorithmsignificantly underestimates the energy expenditure.

Table 1 demonstrates this limitation. It summarizesenergy expenditure results of a pilot experiment that weconducted to study the mapping error in a walkingmonitoring app (iBurnCalorie). A participant was asked towalk on a treadmill at two different speeds (2 mph and 3mph) and at four different surface inclines (0%, 10%,20%, and 30%) for each speed. Each walking session was3 min. Energy expenditure was measured through arespiration gas analyzer [1], a high-grade treadmill [3],and the iBurnCalorie app [4]. The respiration gas analyzermeasures energy expenditure in Respiratory ExchangeRatio (RER) and is the gold standard for metabolicmeasurements. The results show strong agreement(correlation coefficient, R = 0.95) between the treadmillestimations and the RER values. This validates thetreadmill’s energy estimation.

The app’s energy estimations for the 2 mph walking and 3mph walking on the leveled surface (0% incline) are onpar with the treadmill’s estimations. However, estimationdifferences increase with the rise in surface incline. Theculprit is the app’s insensitivity to inclination changes.

Speed[mph]

Incline[%]

RGA[RER]

Treadmill[calories]

iBurnCalorie[calories]

2

0 12.427 11 910 14.434 24 1020 14.528 39 930 18.582 51 9

3

0 12.425 14 1210 12.988 30 1220 18.623 51 1330 21.663 67 13

Table 1: Energy expenditure of treadmill walking measuredthrough a respiration gas analyzer (RGA), a treadmill, and awalking monitoring app (iBurnCalorie).

Kane et. al. investigated this issue for the Nike+ device[8]. They measured energy expenditure at 3 mph treadmillwalking having surface incline of 0%, 5% and 10%. Theyreported that the Nike+ device was unable to detectincreased energy expenditure for increased inclination.

An alternate approach is to extract elevation informationfrom the web in real-time (e.g., Google Elevation). Themajor limitation of this approach is the gross spatialresolution. Google elevation offers an accuracy range of±30 m. Also, it allows only 2500 free URL calls per 24hour period. Additionally, the URL calls consume theuser’s data plan. Therefore, the web-based approach doesnot appear to be a practical solution.

In this study, we propose a linear model to estimatesurface incline from accelerometer readings. Thecomputed surface incline is factored into the energyexpenditure estimation as described in [9]. We simulatedvarious uphill walking scenarios in a lab experiment toconstruct the model.

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Experimental Design

Figure 1: The experimentalsetup. The inset image shows theiPhone attachment.

A total of n = 9 participants (4 normal weight, 4overweight, and 1 obese) volunteered for the experiment.Their ages ranged between 23 and 34 years(µ± σ = 26.77± 3.59) and their Body Mass Index (BMI)ranged between 22 and 32 kg.m−2 (µ±σ = 25.95±3.30).The experiment has been approved by the University ofHouston Institutional Review Board.

Figure 1 shows the experimental setup. A treadmill’ssurface incline is measured as the percentage rise in heightfor every 100 m distance. Standard treadmills incline onlyby 15%. We used a high-end treadmill (Incline Trainerfrom FreeMotion) that inclines up to 30%. Eachparticipant’s body movement was recorded via theaccelerometer embedded in the iPhone 5. The iPhone wasattached to the participant’s thigh location in a portraitorientation to minimize oscillation. The attachment wasrandomized between right thigh and left thigh to ensureunbiased data recording.

The experiment featured a warm-up session of 3 minfollowed by eight exercise sessions of 1.5 min each. Eachwalking session was followed by a 4 min relaxation periodwhere participants rested in a comfortable chair. In thewarm-up session, the participants walked on the treadmillat 3 mph with 0% surface incline. The exercise sessionsincluded two different speeds (2 mph and 3 mph). Foreach speed, the participants walked at four differentsurface inclines: 0%, 10%, 20%, and 30% correspondingto 0o, 5.74o, 11.53o, and 17.45o elevation angles,respectively. We randomized the order of the exercisesessions for each participant to minimize confoundingfactors.

We recorded the accelerometer data during all theexercise sessions. Thus, we collected a total of 72 (9participants x 8 recordings/participant) accelerometersignals in this study.

Data AnalysisFrom each accelerometer signal, we discarded the first andlast 10 sec of the data. We took this precautionary stepto exclude data collected during the transient period whenthe treadmill was gradually reaching its predefined speedand the participants were adjusting to their walkingrhythm. The portrait orientation of the iPhone’sattachment in the thigh position situated theaccelerometer’s Y-axis in the direction of the legs’ motionand the other two axes (X-axis and Z-axis) orthogonal tothe motion (see Figure 1). This arrangement mapped thevertical component of gravity acceleration to Y-axis, andits horizontal components to X-axis and Z-axis. Hence,the vertical component (Gravity Y) captured themaximum amount of motion. We, therefore, included inthe statistical analysis the gravity readings from the Y-axisonly. If the phone was placed in the landscape orientation,we would have used the X-axis gravity component. Thephone orientation can be easily calculated via thesmartphone’s operating system (iOS in this case).

Figure 2 shows raw Gravity Y (GY) signals at differentsurface inclines. The signals were acquired fromparticipant P2’s walking sessions at 3 mph. The figureclearly illustrates that the GY signal’s mean value (µGY )as well as its spread (σGY ) are gradually increasing withthe rise in the surface incline.

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-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Gra

vit

y Y

µGY = -0.94

σGY = 0.03

Surface Incline of 0%

(a)

µGY = -0.92

σGY = 0.05

Surface Incline of 10%

(b)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0 20 40 60

Gra

vit

y Y

Time [s]

µGY = -0.89

σGY = 0.09

Surface Incline of 20%

(c)

0 20 40 60

Time [s]

µGY = -0.84

σGY = 0.13

Surface Incline of 30%

(d)

Figure 2: Y-axis Gravity values from participant P2’s walkingsessions at 3 mph for various surface inclines.

Linearity TestsFigure 3 shows box-plots of the Gravity Y readings for allnine participants. Qualitatively speaking, the resultsconfirm the gradual increasing trend of Gravity Y in bothwalking speeds for all nine participants. To validate thefindings, we computed the correlation coefficient (R2)between the mean Gravity Y (µGY ) and surface incline.Specifically, we first computed the R2 value perparticipant per speed. Then, we computed the mean (µR)of the R2 values for each walking speed. The results arereported in Figure 4. Figure 4(a) illustrates stronglinearity (µR = 0.96) between the mean values (µGY ) andthe surface inclines for 2 mph walking. Figure 4(b) alsoindicates strong linearity (µR = 0.96) between the meanvalues (µGY ) and the surface inclines for 3 mph walking.

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

Mis

sing v

alues

P1

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

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Surface Incline [%]

2 mph 3 mph

P2

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

P3

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

P4

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

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Surface Incline [%]

2 mph 3 mph

Mis

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alues

P5

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

P6

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

P7

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

Mis

sing v

alues

P8

-1.0-0.8-0.6-0.4-0.20.0

0 10 20 30 0 10 20 30

Gra

vit

y Y

Surface Incline [%]

2 mph 3 mph

P9

Figure 3: Box-plots of the Gravity Y readings for the entiredataset.

Significance TestsHaving established the linear relationship between GravityY and surface incline, we explored the specificity propertyof Gravity Y. In particular, we investigated if Gravity Yyields significant differences between two surface inclines

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(10% resolution level). For each walking speed and pair ofsurface inclines, we performed a paired t-test on thecorresponding µGY values. The test was run exhaustivelyon all surface incline pairs. Table 2 summarizes the testresults (p values) for the 2 mph walking sessions. Table 3summarizes the test results for the 3 mph walkingsessions. The results show that all pairs have statisticallysignificant differences (p < 0.05).

Surface Incline 0% 10% 20%0% - - -

10% 0.0080 - -20% 0.0023 0.004 -30% 0.0001 0.0000 0.0006

Table 2: Paired t-test results (pvalues) for the 2 mph walkingsessions. n = 9 for all the tests.

Surface Incline 0% 10% 20%0% - - -

10% 0.0311 - -20% 0.0003 0.0000 -30% 0.0008 0.0001 0.0130

Table 3: Paired t-test results (pvalues) for the 3 mph walkingsessions. n = 9 for all the testsexcept for the 0%-10% pair forwhich n = 6.

Linear ModellingWe modelled the bivariate relationship via least-squareslinear regression. Figure 4(a) illustrates strong linear fit(R2 = 0.99) for the 2 mph walking scenarios.

-1.0

-0.9

-0.8

-0.7

0 10 20 30

GY

Surface Incline [%]

R = 0.96

(a)

y = 0.0034x-0.953,

Walking Sessions at 2 mph

R2 = 0.99

0 10 20 30Surface Incline [%]

R = 0.96

(b)

y = 0.0036x-0.937,

Walking Sessions at 3 mph

R2 = 0.96

P7P8

GYLinear Re gression

P3 P6 P9

Population MeanP1P2

P4P5

P7P8

μ μ

μ

Linear Regression

Figure 4: Means of Gravity Y per surface incline for all nineparticipants walking at (a) 2 mph and (b) 3 mph.

Figure 4(b) illustrates strong linear fit (R2 = 0.96) for the3 mph walking scenarios. The regression equations shownin the figure map Gravity Y to surface incline.

Once the surface incline is estimated from the statisticalmodel, its value is factored in the following equation toestimate the metabolic consumption of walking [9]:

V O2 = 3.5 + 0.1(speed) + 1.8(speed)(incline). (1)

The speed parameter of this equation can be estimatedfrom the accelerometer [7]. Finally, the metabolicestimation (V O2) is mapped to caloric consumption viathe following equation [2]:

E = (V O2 −BMR) ∗ 5/(Body Mass). (2)

BMR in Equation (2) is the basal metabolic rate.

Conclusion and DiscussionIn this paper we present an innovative approach forestimating surface inclination via a smartphone’saccelerometer. Specifically, we show that theaccelerometer’s gravity component linearly correlates withthe surface incline. Furthermore, we outline a calibrationmethod that incorporates incline estimation to caloricmapping algorithms, thus, improving caloric estimation inwalking apps. Although we used the iPhone platform inthis study, the proposed method is generic and can applyto any smartphone platform.

Admittedly, the thigh location is not the most convenientsite for smartphone attachment. The reason for selectingthe thigh location is because it gives the best results dueto walking kinematics. Currently, we are collecting datafor other body positions. In particular, the arm locationhas favorable kinematics and may produce comparableresults, while it is more user-friendly.

This feasibility investigation is limited to uphill walkingonly. In the near future we plan to expand the

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investigation to a full-scale study that will include a largerparticipant pool, and a full range of walking speeds (2mph, 2.5 mph, 3 mph, 3.5 mph) for both uphill anddownhill walking.

References[1] ADInstruments. Exercise Physiology. http:

//www.adinstruments.com/products/metabolic.[2] Armstrong, L., et al. ACSM’s guidelines for exercise

testing and prescription/American College of.Lippincott Williams & Wilkins, Philadelphia, 2006.

[3] Cardiamed India. FreeMotion Incline Trainer. http:

//www.cardiomedindia.com/inclinetrainer.htm.[4] Fujiki, Y. iphone as a physical activity measurement

platform. In CHI’10 Extended Abstracts on HumanFactors in Computing Systems, ACM (2010),4315–4320.

[5] Hong, Y. J., Kim, I. J., Ahn, S. C., and Kim, H.-G.Mobile health monitoring system based on activityrecognition using accelerometer. SimulationModelling Practice and Theory 18, 4 (2010),446–455.

[6] Lester, J., Hartung, C., Pina, L., Libby, R., Borriello,G., and Duncan, G. Validated caloric expenditureestimation using a single body-worn sensor. InProceedings of the 11th international conference onUbiquitous computing, ACM (2009), 225–234.

[7] Park, J., Patel, A., Curtis, D., Teller, S., and Ledlie,J. Online pose classification and walking speedestimation using handheld devices. In Proceedings onUbiquitous Computing, ACM (2012), 113–122.

[8] Saponas, T. S., Lester, J., Hartung, C., and Kohno,T. Devices that tell on you: The Nike+iPod sportkit. Tech. rep., University of Washington, 2006.

[9] Swain, D. P., Brawner, C. A., et al. ACSM’sResource manual for guidelines for exercise testingand prescription. Lippincott Williams & Wilkins,2012.

[10] Tremblay, M. S., Colley, R. C., Saunders, T. J.,Healy, G. N., and Owen, N. Physiological and healthimplications of a sedentary lifestyle. AppliedPhysiology, Nutrition, and Metabolism 35, 6 (2010),725–740.

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