Accurate Caloric Expenditure of Bicyclists using Cellphone

SenSys2012Andong Zhan, Marcus Chang, Yin Chen, Andreas Terzis

Computer Science DepartmentJohns Hopkins University

Baltimore, MD 21218

NSLab study group 2012/11/12Speaker : Chia-Chih,Lin

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

Motivation

• Diverse benefits• Especially from a health perspective• People usually care about caloric expenditure • sensor such as – power meter– cadence sensors– and heart rate monitor

• but expensive (above $1000) and cumbersome

Motivation cont.

• Want to calculate accurate caloric expenditure Without high cost and cumbersome devices• Can we just use a smart phone in pocket to

solve the problem?

Challenges

• Existing apps do not directly measure the cyclist’s activity

• Errors in GPS measurement• Do not consider the slope• Energy consumption

contribution

• Pocket sensing approach replace on-bike hardware– Measure cadence less than 2% error– Overall caloric estimation error is 60% smaller than other

apps– Reduce energy consumption by 57%

• Compare and analyze major elevation service– Find and minimize error on both USGS and Google Map

caused by bridge• Show that leveraging detailed map information from

USGS and OpenStreetMap can save a significant amount of energy

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

How to calculate?

• Four caloric expenditure estimators– Search Table– Cadence and Speed Sensing– Heart Rate Monitoring– Power Measurement

Search Table• Input : average speed, trip duration, biker’s

weight• Low accuracy(do not consider slope)

Cadence and Speed Sensing

• Use sensor to measure pedaling speed(RPM)

• VO2 : oxygen consumption(liter per minute)– V : bike velocity– S : pedaling speed

• Estimate caloric by VO2*5 (Kcal/min)• Drawback : underestimate during uphill trips

Heart Rate Monitoring

• Takes heart rate and VO2 max as input and adjusting for age, gender, body mass, and fitness level

• VO2 max is a good measure of aerobic condition, requires 12 minutes rush to test

• where D is distance (m)

Heart Rate Monitoring cont.

• Then,

where BPM is the heart rate in beats/min• High accuracy but cumbersome for daily use

Power Measurement

• Related to the total amount of work necessary to move the combined mass of the biker and bike from start to finish

• Where Vg is a constant ground speed and F is the force generate by the rider along the direction of movement

Power Measurement cont.

Power Measurement cont.

Fr : rolling resistance from the bikeFg : component of gravity along the direction of movementFa : force of aerodynamic dragm : mass of bike and bikerCr : lumped coefficient of rolling resistanceS : slope ,where Vw is wind vector : temperature dependent air densityCa : lumped constant for aerodynamic drag

Power Measurement cont.

• Then• One can estimate the calories burned/s• Calories burned = P*25%

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

System design

Data Collection

• 15 bike routes located around Jonh Hopkins University’s Homewood campus in Baltimore

• All the routes can be complete in 20 mins• Samples GPS, pressuresensor, and heart rate monitor once per sec.Accelerometer 50Hz

Cadence Sensing in the Pocket

Elevation measurement

• Digital barometric pressure sensor– – where p0 is pressure at sea level

• Phone’s GPS receiver(estimate altitude indirectly)– Use latitude and longitude to query GIS• US Geological Service, USGS(3-meter resolution

dataset)• Google Maps(19-meter resolution dataset)

Elevation measurement Cont.

• Have to minimize GPS error first– Assume that all bike trips take place on either

marked paths or roads– Use OpenStreetMap to match the nearest roads

and project each GPS coordinate to the nearest point on this road

Bridge Error

• Altitude return not correct when biking on bridge

• Pressure sensor is more accuracy• USGS and Google Map are fail

Bridge Error cont.

• Smooth the curve using a robust local regression method

• Use a quadratic polynomial model to fit the elevation data and set the span to be nine data points

• Weights for each data point in the span

• Where ri is the residual of the i-th data point, MAD = median(|r|)

Calibration of Power Measurement

• Lumped coefficients of rolling resistance Cr and aerodynamic drag Ca

• Ca : use an empirical reference value 0.26• Weather condition(temperature, wind

speed,wind direction)

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

Cadence Sensing

Elevation Services

Caloric Expenditure Estimation• Single biker :– Search Table(TAB)– Cadence and Speed Sensing(CAD)– F for fitting, W for weather, S for smoothing

Caloric Expenditure Estimation

• Multiple biker :

• Multiple biker :

• Multiple biker :

Reduce GPS Power Consumtion

• Only consider two extreme case:• Reconstruct the missing bike route points by :

– Interpolating between the known point– Apply the state-of-art rout reconstruction mechanism

called EnAcq algorithm

Reduce GPS Power Consumtion

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

Discussion

• Feasibility– Can implement the cadence sensing and power

measurement approaches in a real-time app– Can upload the raw trace to server instead of

offline data analysis– Ideally, calibration only needs to be done once

when first start to use

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

Conclusion

• On-bike sensor, although expensive, can significantly improve the overall biking experience

• This work, get all information by using smart phone only

• Extensive result from 20bikers over 70bike trips confirmed that it is accuracy and feasible

Outline

• Introduction• Background• System design• Evaluation• Discussion• Conclusion• Comment

Comment

• Good architecture• Interesting approaches• Complete analysis and evaluation

Q&A?

• Thanks for your listening !