Estimating Heart Rate Variation during Walking with Smartphone

Estimating Heart Rate Variation during Walking with Smartphone

Mayu SUMIDA, 〇 Teruhiro MIZUMOTO, Keiichi YASUMOTO

Nara Institute of Science and Technology, Japan

ACM Ubicomp’13, September 8 -12, 2013Zurich, Switzerland

2Estimating Heart Rate Variation during Walking with Smartphone

• Goal: Walking support application for effective walking with appropriate physical load while keeping the walking advantage

• Challenge– Predicting heart rate (HR) with only available functions of a

smart phone to measure physical load

• Idea– Constructing HR prediction model by machine learning

adopting the oxygen uptake as one of input data

• Result– Less than 7 beat per minute mean error for various walking

routes/users

Overview


Outline

1. Background

2. Related work

3. Heart Rate Prediction Method

4. Evaluation

5. Conclusion


Background• Walking is not only simple and convenient

Effective for health promotion and maintenance

It is important to walk with appropriate physical load depending on individual physical condition

Walking with high physical load

Walking with low load

[1] Intensity versus duration of physical activity: implications for the metabolic syndrome. a prospective cohort study, BMJ Open (2012).

However

decrease the walking motivation give the risk of injury (to the elderly people, etc)

may result in no effect[1]


Related Work 1/2

Walking Support System: MPTrain [1]

Regulate HR within an appropriate range during walking

Users have to attach a HR monitor directly on body 　　 Simplicity and convenience of walking are spoiled

[1] MPTrain: a music and physiology-based personal trainer, MobileHCI’06 (2006).

HR monitor


Related Work 2/2

Predict HR from acceleration data by using Neural network

Showed that Neural network is effective to predict HR

Use previous predicted HR to next prediction Error is accumulated every prediction

HR prediction method proposed by Xiao et.al.[2] 　　

[2] Heart Rate Prediction Model Based on Physical Activities Using Evolutionary Neural Network ， ICGEC '10 (2010).

Can apply only to daily living situation as HR variation is rather small


Contribution

• Problem 　　 Existing system requires attaching a HR monitor (costly, bother)

Existing HR prediction method cannot be used in walking

Predict heart rate by only available functions of a smart phone and provide effective walking through pace control

• Goal 　　

Devise a heart rate prediction method by a smartphone


Outline

1. Background

2. Related work


4. Evaluation

5. Conclusion

9

Heart Rate Prediction Method

•How to predict HR?– Construct HR prediction model by machine learning

• What parameters can we use for training data?– Smartphone can measure many information


Light

Acceleration Temperature

HumidityLocation

DirectionStep count

Speed Distance

Gradient


Consideration of Input Data• Heart rate is related to exercise intensity

– Gradient, walking speed and acceleration are available to predict heart rate

• We constructed model and evaluated HR– Model caused more 10 bpm mean error with actual HR

Walking SpeedGradientAccelerationAmplitude

We searched the parameter more related to exercise intensity and HR


VO [ml/kg/min]

Time （ s）

Demand

Case of increment

Time（ s ）

Case of decrement

Demand

• Oxygen Uptake （ VO ） gradually converges to oxygen demand in 2 to 3 minutes[3] 　

Calculate oxygen demand and determine the trend then estimate VO by using oxygen demand, trend and time

VO [ml/kg/min]

This feature is similar to HR feature

Trend changes by whether oxygen demand increases/decreases

[3]Linear and nonlinear characteristics of oxygen uptake kinetics during heavy exercise. J. of Applied Physiology, 1991.

Oxygen uptake

Devised a novel technique to estimate VO


How to calculate Oxygen demand• Can calculate oxygen demand by speed and gradient[4]

WalkingSpeed Oxygen

DemandGradient

[4] Lippincott Williams & Wilkins, Philadelphia, ACSM’s Guidelines for Exercise Testing and Prescription (7th edition.)

Walking speed and gradient during walking vary unexpectedly

⇒ It is difficult to calculate VO by Oxygen demand

Periodically calculate oxygen demand with fixed time interval then estimate VO

Accelerometer GPS Gyro

We use dead reckoning


K1

VO Estimation Method (1/2)

K0

t2t0

VO [ml/kg/min]

time[s]

Current

Previous

Current

（ 1 ） Calculate demand

（ 3 ） Estimate oxygen uptake variation

（ 2 ） Determine trend of variation

by comparing Kc with Kpre





K1

K0

t1t0

VO [ml/kg/min]

time[s]

Current

Previous

Current Previous

Kpre < Kc Kc < Kpre

Apply incremental model to estimate VO

Apply decremental model to estimate VO

V1

V1Current VO

Current VO


VO Estimation Method (2/2)

Continue to apply previous trend model to estimate VO

K2 =

K0

t2t0

VO [ml/kg/min]

time[s]

Current

Current

Kc = Kpre

V2

K1Previous

V1

Current VO

t1

Previous







t3

K3=

• We can obtain oxygen uptake variation by repeating this process

time [s]

V0

0 t1

K1

K2

VO [ml/kg/min]

t2 t4

K4K5=

t5

No change

Up

Up Down

No changeV4

Example of VO Estimation

V5


Overview of Input Data

• As result of preliminary experiment– Constructing model by gradient, amplitude

(vertical and horizontal direction) and oxygen uptake was the best

Gradient

Speed

Oxygen uptake (VO)

Amplitude

Location

Acceleration

Input Data

Measured valueDead-reckoning

Calculate Oxygen Demand


Constructing HR Prediction Model

• Construct model by three-layered neural network• There is no liner relation ship between HR and input data


Outline

1. Background

2. Related work


4. Evaluation

5. Conclusion


Purpose and Setting for EvaluationPurpose

Setting

• Evaluate the heart rate prediction accuracy of our method

• 18 subjects （ twenties / 15 male, 3 female ）• Each subject walked 5 different routes to collect data

We extracted accurate altitude from the map published by government


Devices for Collecting Data

Hardware Sensor Sampling time

SUUNTO t6dHeart ratemonitor 2s

Xperia active 3-axesaccelerometer 20ms

GPS 3s

Y-axis

X-axis

• We asked each subject to equip • A smart phone to measure acceleration and location• A heart rate monitor to measure heart rate as training data


Model• Collected 90 training data (18 subjects×5 routes)

acceleration amplitude, gradient, VO and measured HR

• Construct model of each subject of each route– The prediction when a new user walks on a new route

Subject S

Remaining 17 subjects

Route R

Remaining 4 routesUse 68 data as training data

If we evaluate the model of subject S of Route R

×

Use the data of subject S of Route R as test data


Accuracy Definition22

time [s]

HR

[bpm

]

　　　　measured 　　　　　 predicted

• We calculate mean absolute error as accuracy• Absolute error: the difference between measured HR by HR

monitor and predicted HR by our model every 24 seconds

Absolute error

The minimum time that we can use all parameters in same time


• Borg Scale[5] classifies physical load into 15 levels (6 ～ 20) called RPE

[5] Psychophysical scaling with applications in physical work and the perception of exertion, Scandinavian Journal of Work Environment Health (1990).[6] Perceived exertion: a note on ”history” and methods. ACSM J. of Med Sci Sports Exerc.(1973).

• RPE (Ratings of perceived exertion) corresponds to one tenth of HR [6]

Borg ScaleIf error is less than 10 bpm, difference of physical load is low

Physical Load scaling method


Accuracy of Each Subject

1 2 3 4 5 6 7 8 9 1011121314151617180

2

4

6

8

10

Mea

n Ab

solu

te E

rror

[b

pm]

Subject

• All of subject were less than 10 bpm MAE• We achieved average 6.78 bpm MAE

HR is known that it varies 7bpm even during rest situation

6.78 bpm


Accuracy of Each Route

A B C D E0

2

4

6

8

10

7.576.46

5.55

9.05

5.25

MAE

[bpm

]

Route

• All of route were less than 10 bpm MAE• Routes with down slope or flat were more less error• Almost subjects with low accuracy were low accuracy

in route A and D having steep slope, especially 6.78 bpm

Our method can predict HR with low error even if a new user walks on a new route


Example of Heart Rate Prediction

Almost accurately follow HR variation

Route A by subject 103.48bpm mean absolute error 　

Altitude of Route A

Altit

ude[

m]

Predicted


Example of Heart Rate Prediction

Route A by subject 216.69 bpm MAE

Altitude of Route A

Altit

ude[

m]

Predicted low HR in up slope

PredictedPredicted appropriate HR in down slope


Effectiveness of oxygen uptake

• We also evaluated effectiveness of introducing oxygen uptake (VO) by other data set– The MAE without VO and with VO were 16.71

bpm and 6.41 bpm

Using oxygen uptake as a parameter is effective for HR prediction

MAE

16.71

6.41


Conclusion

• Heart rate prediction method for walking support system by a smartphone• Adopt Oxygen uptake similar to the feature of heart rate

variation to train model by neural network

• Our method could estimate the HR with accuracy of about 6.78 bpm on average when 18 subjects walked on 5 routes

We considers user’s condition, weather (temp and humid) , etc.

Future work 　

From evaluation result 　

Presentations & Public Speaking

Estimating Heart Rate Variation during Walking with Smartphone