Estimating Heart Rate Variation during Walking with Smartphone

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Estimating Heart Rate Variation during Walking with Smartphone

Mayu SUMIDA, Teruhiro MIZUMOTO, Keiichi YASUMOTONara Institute of Science and Technology, Japan

ACM Ubicomp13, September 8 -12, 2013Zurich, Switzerland

Thank you, chairman,Good morning everyone, welcome to my presentation.

Im Teruhiro Mizumoto, from Nara Institute of Science and Technology. Japan.

This work mainly done by mayu sumida,She is good researcher.But she already jointed company.

So I will present her interesting work today.

And He is keiichi yasumoto.He is our boss.

Today, Id like to talk about Estimating Heart Rate Variation during Walking with Smartphone

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

ChallengePredicting heart rate (HR) with only available functions of a smart phone to measure physical load

IdeaConstructing HR prediction model by machine learning adopting the oxygen uptake as one of input data

ResultLess than 7 beat per minute mean error for various walking routes/users

Overview

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Estimating Heart Rate Variation during Walking with Smartphone

Our goal is to realize walking support application for effective walking with appropriate pysical load while keeping the walking advantage

And our challenge is predicting heart rate with only available functions of a smart phone to measure physical load.

The idea to predict HR is constructing HR prediction model by machine learning adopting the oxygen uptake as one of input data

As the result of evaluation, our method achieved 7 beat per minute mean error for various walking routes and uses.

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Outline

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BackgroundRelated workHeart Rate Prediction MethodEvaluationConclusion

Estimating Heart Rate Variation during Walking with Smartphone

This is outlineNext, I will explain about background and related work of this study.

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Background

Walking is not only simple and convenient

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Effective for health promotion and maintenance

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

Estimating Heart Rate Variation during Walking with Smartphone

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]

The walking is not only simple and convenient,But also it is effective for health promotion and maintenance

However, walking with high physical loadmay decrease the walking motivationand give the risk of injury, for example, to the elderly people. And so on.

Conversely, walking with low may be of no effectSo it is important to walk with appropriate physical load depending on individual physical condition

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Related Work 1/2

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Estimating Heart Rate Variation during Walking with Smartphone

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, MobileHCI06 (2006).

HR monitor

As related work,A walking support system called MPTrain is proposed.

It regulates heart rate within an appropriate range during walking.However, users have to attach a heart rate monitor directly on the body like this figureSo, simplicity and convenience of walking are spoiled

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Related Work 2/2

Predict HR from acceleration data by using Neural networkShowed that Neural network is effective to predict HR

Use previous predicted HR to next predictionError is accumulated every prediction

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Estimating Heart Rate Variation during Walking with Smartphone

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

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

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

As other related work,Xiao proposed a HR prediction method.This method predicts HR from acceleration by using Neural Network.And they showed that neural network is useful to predict HR.

However, since the method uses previous predicted HR to next prediction,Error is accumulated every prediction,

So the method can apply only to daily living situation as HR variation is rather small.

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Contribution

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Problem

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

Estimating Heart Rate Variation during Walking with Smartphone

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

So, existing system requires attaching a heart rate monitor and existing heart rate prediction method can not be used in walking.

So, Our goal is to predict heart rate by a commodity device and provide effective walking through pace control to keep adequate load

In order to achieve this goal, we devise a heart rate prediction method by smartphone

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Outline

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BackgroundRelated workHeart Rate Prediction MethodEvaluationConclusion

Estimating Heart Rate Variation during Walking with Smartphone

next, I will explain about heart rate prediction method.

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Heart Rate Prediction Method

How to predict HR?Construct HR prediction model by machine learningWhat parameters can we use for training data?Smartphone can measure many information

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Estimating Heart Rate Variation during Walking with Smartphone

Light

Acceleration

Temperature

Humidity

Location

Direction

Step count

Speed

Distance

Gradient

At first, we considered how to predict HR?Then, we decided to construct HR prediction model by machine learning as other methods.Because, related work showed the neural network is useful to predict heart rate.

And Next, we considered what parameters can we use for train model. .Smartphone can measure many information such as location acceleration direction.In addition, we can calculate many information by these information. For example, step count, walking speed distance and gradient.

For example, we can directly measure acceleration and location by a smartphone.In addition, we can calculate step count--------and so on by existing method with acceleration and location.

So, we constructed several models using these factors and evaluate performance as preliminary experiment,As its result, these models predicted heart rate by more than 10 bpm error from actual heart rate.It is very large error.

,

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Consideration of Input Data

Heart rate is related to exercise intensityGradient, walking speed and acceleration are available to predict heart rate We constructed model and evaluated HRModel caused more 10 bpm mean error with actual HR

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Estimating Heart Rate Variation during Walking with Smartphone

Walking Speed

Gradient

AccelerationAmplitude

We searched the parameter more related to exercise intensity and HR

However, since heart rate is related to exercise intensity, we considered that gradient waling speed and acceleration are available to predict heart rate.

Then, we constructed model by these input data, and evaluated HR.However, the model caused 10bpm mean error with actual HR.

So, we searced the parameter more related to exercise intensity and HR

6.71

Heart rate is related to exercise intensity so we considered that gradient, walking speed, acceleration amplitude are available.We can calculate these information by a smartphone.

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Estimating Heart Rate Variation during Walking with Smartphone

VO [ml/kg/min]

Times

Demand

Case of increment

Times

Case of decrement

Demand

Oxygen UptakeVO 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

So, we found Oxygen Uptake.The Oxygen uptake is the amount of oxygen that people actually take at the time.

The Oxygen Uptake gradually converges to oxygen demand in 2 to 3 minutes shown in these figures.This feature is similar to heart rate feature.So we decided to adopt oxygen uptake as one of training data

However, as shown in these figures, Trend of oxygen uptake variation changes by whether oxygen demand increases / decreases.the oxygen demand increases or decreases.So, we cannot directly measure oxygen uptake. So, we devised novel technique to estimate oxygen uptakeIn the technique, at first, we calculate oxygen demand and determine trend then estimate VO by using oxygen demand and time.

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