Learning Human Context through Unobtrusive Methods Yanyong Zhang - - PowerPoint PPT Presentation

Learning Human Context through Unobtrusive Methods

Yanyong Zhang

WINLAB, Rutgers University yyzhang@winlab.rutgers.edu

Yanyong Zhang yyzhang@winlab.rutgers.edu

We care about our contexts

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Glass Necklace Watch Phone Wristband Meeting Vigo: your first energy meter Fitbit: Get Fit, Sleep Better, All in the one Fall detection for the elderly

Yanyong Zhang yyzhang@winlab.rutgers.edu

But,

Can we learn contexts in an unobtrusive manner?

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 No need to wear a device  No need to report status  No extensive calibration  It naturally takes place as we live our life

Yanyong Zhang yyzhang@winlab.rutgers.edu

SCPL

Radio-frequency (RF) based device-free localization: location, trajectory, speed

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[1] C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods, In ACM/IEEE IPSN, 2012. [2] C. Xu, B. Firner, R.S. Moore, Y. Zhang, W. Trappe, R. Howard, F. Zhang, and N. An. Scpl: indoor device-free multi-subject counting and localization using radio signal strength. In ACM/IEEE IPSN, 2013.

Yanyong Zhang yyzhang@winlab.rutgers.edu

Device Free Passive Localization

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Empty room

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DfP Localization

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Geometry to the Rescue?

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No! Because of Multi-path effect

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Empty room

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Fingerprinting

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Yanyong Zhang yyzhang@winlab.rutgers.edu

Cell-based Fingerprinting

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Link 2 RSS (dBm) Link 1 RSS (dBm)

k = 1 k = 2 k = 3

Yanyong Zhang yyzhang@winlab.rutgers.edu

Linear Discriminant Analysis

 RSS measurements with person’s presence in each cell

is treated as a class/state k

 Each class k is Multivariate Gaussian with common

covariance

 Linear discriminant function:

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Link 2 RSS (dBm) Link 1 RSS (dBm)

k = 1 k = 2 k = 3

Yanyong Zhang yyzhang@winlab.rutgers.edu

Evaluation Platform

 Hardware: PIP tag

 Microprocessor: C8051F321  Radio chip: CC1100  Power: Lithium coin cell battery

 Protocol: Unidirectional heartbeat (Uni-HB)

 Packet size: 10 bytes  Beacon interval: 100 msec

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Yanyong Zhang yyzhang@winlab.rutgers.edu

Localization in a cluttered room

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Size: 5 × 8 m Cell Number: 32 97% cell estimation accuracy (16 devices) 90% Cell estimation accuracy (8 devices)

Yanyong Zhang yyzhang@winlab.rutgers.edu

Less training is OK

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Only 8 samples are good enough

Yanyong Zhang yyzhang@winlab.rutgers.edu

Having fewer devices is OK

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5 transmitters + 3 receivers = 90% cell estimation accuracy

Yanyong Zhang yyzhang@winlab.rutgers.edu

Can we use the same training after 3 months?

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Yanyong Zhang yyzhang@winlab.rutgers.edu

Next, let us localize multiple people

 Challenge: we do NOT want to train all N people

with all the combinations at different cells

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Yanyong Zhang yyzhang@winlab.rutgers.edu

Fingerprinting 1 person

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…

9 trials in total for 1 person

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Fingerprinting 2 people

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… …

36 trials in total for 2 people!

Yanyong Zhang yyzhang@winlab.rutgers.edu

Fingerprinting N people

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1 person 2 people 3 people 9 cells 9 36 84 36 cells 36 630 7140 100 cells 100 4950 161700 161700 × 1 min = 112 days The calibration effort is prohibitive!

Yanyong Zhang yyzhang@winlab.rutgers.edu

Instead,

 Can we use 1 person’s training data to localize N

people?

 Yes. SCPL has two phases: (i) counting and (2)

tracking

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Yanyong Zhang yyzhang@winlab.rutgers.edu

RSS change with people

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Link 3 Link 1 Link 2

Link 1 change: 0 dB Link 2 change: 0 dB Link 3 change: 0 dB

Link 3 Link 1 Link 2

Link 1 change: 4 dB Link 2 change: 5 dB Link 3 change: 0 dB

Link 3 Link 1 Link 2

Link 1 change: 0 dB Link 2 change: 6 dB Link 3 change: 5 dB

Link 3 Link 1 Link 2

Link 1 change: 4 dB Link 2 change: 7 dB Link 3 change: 5 dB

Additive effect on the radio links!

Yanyong Zhang yyzhang@winlab.rutgers.edu

So,

 Can we directly infer n from the observed total

RSSI change?

 Is it linear?

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Yanyong Zhang yyzhang@winlab.rutgers.edu

Nonlinear fading effect!

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Calibration data

5 dB 6 dB

Calibration data

5 dB 4 dB 5 dB 7 dB 4 dB

4 dB + 0 dB = 4 dB √ 5 dB + 6 dB = 11 dB ≠ 7 dB X 0 dB + 5 dB = 5 dB √ Measurement

Shared links observe nonlinear fading effect from multiple people.

Yanyong Zhang yyzhang@winlab.rutgers.edu

Location-Link Correlation

 To mitigate the error caused by this over-

subtraction problem, we propose to multiply a location-link correlation coefficient before successive subtracting:

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Sequential Counting (SC) Algorithm

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∆ = Sum of RSS change of links

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Parallel Localization (PL)

 Cell-based localization  Trajectory-assisted localization

 Improve accuracy by using human mobility constraints

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Mobility makes localization easier

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In a building, your next step is constrained by walking speed, cubicles, walls, etc.

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Trajectory-based Localization

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Trajectory ring filter

Indoor mobility constraints can help improve localization accuracy.

Data likelihood map Refined likelihood map

=

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Parallel Localization (SL) Algorithm

 Single subject localization  Multiple subjects localization

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ViterbiScore =

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Testing Environment

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Total size: 10 × 15 m 37 cells of cubicles and aisle segments 13 transmitters and 9 receivers Test paths with partial overlap

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Counting Results

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We achieve above 85% counting accuracy when no trajectories are overlapped.

Yanyong Zhang yyzhang@winlab.rutgers.edu

Localization Results

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Trajectory ring filter achieve 1-meter localization accuracy and improve 30% from the baseline.

Yanyong Zhang yyzhang@winlab.rutgers.edu

Lessons learned

 Calibration data collected from one subject can

be used to count and localize multiple subjects.

 Though indoor spaces have complex radio

propagation characteristics, the increased mobility constraints can be leveraged to improve tracking accuracy.

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Yanyong Zhang yyzhang@winlab.rutgers.edu

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Questions & Answers