People carry their phones in different locations1, but activity tracking studies often consider only one, prespecified location

Wearing the phone in different locations may affect classification accuracy

Can tracking algorithms generalize to multiple locations and maintain accurate predictions?

Which location gives the most accurate predictions of a person’s activity?

1. Cui YQ, Chipchase J, Ichikawa F. A cross culture study on phone carrying and physical personalization. Usability and Internationalization, Pt 1, Proceedings, 2007; 4559: 483-92.

Results Results continuedMotivation

Discussion

Activity tracking with smartphones: phone location mattersStephen Antos1, Mark V. Albert2, 3, Konrad Kording3

1Department of Biomedical Engineering, Northwestern University; 2Department of Computer Science, Loyola University3Department of Physical Medicine and Rehabilitation, Northwestern University

Methods

Objectives

Acknowledgements

Determine how well algorithms generalize to new phone locations

Determine the best phone location to recommend for future studies

Phone Locations Sequence of Activities

standsitstandwalkstandwalkstandwalkstandsitstand

Methods continued

b c c a c

b a c c c

c a b c c

c c b a c

c c c b a

a = 0.897b = 0.100c = 0.001

12 subjects carried a smartphone in pocket, belt, hand, or bag

Performed a sequence of activities for each location and had accelerations recorded.

HMM transition matrix for activities

Three ways to train and test classifiers to examine the effect of phone location on activity

tracking accuracy

1. Location known: best caseclassifier trained and tested with data from the same location

2. Location assumed: worst caseclassifier trained with one location and tested on all four locations

3. Location varying: practical caseclassifier trained and tested with all four locations

This work was supported by the National Parkinson Foundation, the U.S. National Institutes of Health, and the Washington Square Foundation.

A classifier trained for one location does not generalize to new locations

We recommend collecting data from all potential locations if possible

If only one location is feasible, we recommend using the belt or pocket

Adding a hidden Markov model to the support vector machine results in higher classification accuracy

activ

ity tr

acki

ng a

ccur

acy

BagSVM HMM

Location known

Location assumed

Location varying

Location known

Location assumed

Location varying

100

80

60

40

activ

ity tr

acki

ng a

ccur

acy

100

80

60

40

Assuming the phone is carried in one location decreases tracking accuracy

Sit to stand

Sitting

Stand to sit

Standing

Walking

Sit t

o st

and

Sitti

ngSt

and

to s

itSt

andi

ngW

alki

ngTo

From

next

loca

tion

activ

ity tr

acki

ng a

ccur

acy

Location known

Location assumed

Location varying

Activity sequence

Data clip

Features

Support vector machine

Probabilistic estimates

Hidden Markov model

Activity predictions

SVM

HMM

f1, f2, ..., fN-1,fN

p1, p2, ..., pM-1,pM

time (sec)

0 100 200-20

20

0

m/s

2

-20

20

0

m/s

2

time (sec)0 1 2

St

St-1

Carrying the phone on a belt or in a pockethas more reliable tracking for new users

References

predictedactual

pocket/walking

pocket/standing

pocket/stand to sit

pocket/sitting

pocket/sit to stand

hand/standing

hand/misc

not wearing

0 200 400 600

time (secs)Belt

Pocket

Hand

Example of activity tracking for a new sequence

Classification process

100

80

60

20

40

Within subject cross-validation

Across subject cross-validation

2. Albert MV, Toledo S, Shapiro M, and Kording KP (2012) Using mobile phones for activity recognition in Parkinson’s patients. Frontiers in Neurology 3:158. doi: 10.3389/fneur.2012.00158