UbiActive UbiActive Smartphone-Based Tool for Smartphone-Based Tool for Trip Detection and Travel-Related Trip Detection and Travel-Related Physical Activity Assessment Physical Activity Assessment

Yingling Fan, yingling@umn.edu

Qian Chen

Chen-Fu Liao

Frank Douma

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Sensing – Survey – Assess & ReportSensing – Survey – Assess & Report

UserWear smartphone

on her right hipBeing Sensed by

smartphone sensors

Auto SensingLocation & Speed (every 30 seconds); acceleration (1Hz)

Daily Assessment% of active & happy travel;

% of energy expenditures related to travel

After-trip surveyTrip mode, activity, companionship & experience

Enable movement/trip detection

Compile daily survey data

Compile daily sensing data

Report to userdaily travel experience

& travel-related PA

Raw sensing outputs

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Timestamp lAx lAy lAz Lat Lon Speed2011-11-01 15:41:47 -0.336726 -0.046676 0.133635 44.971064 -93.244507 1.0000002011-11-01 15:41:48 0.035131 -0.005836 0.104520 44.971064 -93.244507 1.0000002011-11-01 15:41:49 -0.295038 0.006505 0.259984 44.971064 -93.244507 1.0000002011-11-01 15:41:50 -0.086559 -0.254355 0.191731 44.971064 -93.244507 1.0000002011-11-01 15:41:51 -0.022146 0.079066 0.011211 44.971064 -93.244507 1.0000002011-11-01 15:41:53 0.053333 -0.013562 -0.002895 44.971064 -93.244507 1.0000002011-11-01 15:41:53 0.079704 -0.013553 -0.122060 44.971064 -93.244507 1.000000

How to detect a trip?How to detect a trip?

• Counter A is for judging the start of a trip – Every 30seconds, if the detected movement is larger than 30 meters,

counter A would automatically add one.– When counter A reaches 20 counts, indicating there is a 10-minute

continuous movement, a valid trip is considered to be happening.

• Counter B is for determining the end of a trip.– Every 30 seconds, if no “location change” is updated, count B will

automatically add 1. – When counter B reaches 10 counts, meaning there is no significant

movement for 5 consecutive minutes, the trip is considered.

• Note: – Both counters A and B have default value at zero. – Counter A will be reset to zero if location change is not detected before

reach 20 cts. – Counter B will be reset to zero if location change is detected before reach

10 cts. 4

After-Trip SurveyAfter-Trip Survey

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Evaluation and TestingEvaluation and Testing

• Lab testing– Network usage: data size is less than 1 KB per day

– Memory storage requirement: collect around 7Mb of raw sensor data and statistics per day

(150mb for 3 weeks)

– Battery life: around 12-15 hours without additional voice/text/data usage

– Trip Detection: almost 100%

• Testing among 17 real smartphone users recruited from the University of Minnesota campus

– Time: October-November, 2011

– $100 cash reward upon completion of 3 weeks of compliance.

– Initial background survey, exit survey, and requirement to fill out paper version diary.

– 23 Participants recruited

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Participant summary statisticsParticipant summary statistics

• Of the 17, 12 males, 9 White, 5 Asian, average age 23.• 8 undergraduate, 8 graduate students, 1 alumni• 7 car owners.

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A Case StudyA Case Study

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Trip Information of a Participant on November 3, 2011 – Part I

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Trip Information of a Participant on November 3, 2011 – Part II

Findings: What went Findings: What went wellwell??

• Phone based survey collected info on 509 trips occurred in 256 person-days with

valid data.

• 36% were made on foot, 1% by bike, 26% by private car, and 37% by transit

• 29% were back-to-home trips, 30% school-related, 10% work-related, 11% eating-

related, and 9% were shopping/errands.

• 56% were made alone, 34% with friends, and the rest with family.

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Participation Experience & ComplianceParticipation Experience & Compliance

• 76% participants reported “satisfied”

• 88% reported increased travel behavior awareness.

• 98% at least “somewhat agree” that they felt comfortable having smartphone detecting travel behavior

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Caveats: What went Caveats: What went wrongwrong??

• Some reported poor trip detection rates. Trip detection rates (range 0-90%) depend on

– phone brands & phone newness – GPS signal strength at trip origin, destination, route.

• Converting acceleration outputs to energy expenditure estimates is much more complex than expected. Hardware differences exist.

• Battery consumption issue is a key challenge.

• No behavioral differences between intervention and control groups.

• Issues of missing data

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HTC EVO

Motorola Droid

Google Nexus

Samsung Galaxy

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Missing Data Plots

Next step: UbiActive → SmartTrAC•Sensing + survey → Sensing + data mining + survey•After-trip survey → end-of-the-day activity or trip survey

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walk

school

walk

shop

careat car homehome

walk

school

walk

shop

careat car homehome

This project and subsequent work are supported by – the ITS Institute, and – the Center for Transportation Studies at the University of

Minnesota.

yingling@umn.edu

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