Doorjamb: Unobtrusive Room-level Tracking of People in Homes

using Doorway Sensors

Timothy W. Hnat, Erin Griffiths, Ray Dawson, Kamin WhitehouseU of Virginia

Presenter:SY

About This Paper

• Unobtrusive room-level tracking – People in homes

• Doorway sensors– Ultrasound sensor

• Method– Estimates the height and direction

Technical Problems

• Multi-target tracking – Data association

• Noise– Person’s posture, multipath reflections, and the

natural undulation of gait• Algorithms– Crossing event detection– Tracking

Contributions

• Hardware– Design and prototyping– Lesson learned

• In-depth analysis of the sources errors– Present signal processing algorithm

• Data association challenges– Tracking algorithm

• Proof-of-concept implementation, deployment, and empirical evaluation

Outline

• Hardware design• Signal processing algorithm• Tracking algorithm• Evaluation• Conclusion

Hardware

• Features– Cost effective– Battery powered– Wireless

• Design– Detect height • Measure the distance to the top of the head

– Detect walking direction• Angled into one room more than the other

Doorway Sensor

• Parallax PING ultrasonic range finders• Passive infrared sensors • Magnetic reed sensors• Custom-designed power module • Synapse Wireless SnapPY RF100 module

Achieving Doorway Coverage

• Requirements– 1 cm resolution– Heights ranging from 151 cm to 189 cm– Walking speeds up to 3 m/s^2– Doorways range: 90-300 cm wide, 213-275 cm tall

• Parallax PING ultrasonic– 40 degree beam angle– Min: 2 cm; Max: 300 cm

Achieving Doorway Coverage

• Tallest person– Gap between the head and doorway 24cm– 40 degree beam Sensing diameter of 17 cm– Speed of 3 m/s, a head that is 15 cm diameter• Pass sensing region in about 100 ms

– 50 Hz sample rate – one module at a time

Doorway size

• Typical doorway width of 90 cm– Sensing diameter – 17 cm– Head radius – 7 cm– Two sensors should be enough

• Higher door frames require fewer sensor• 300 x 275 cm– 4 range finders– Sampling rate 12.5 Hz– Cannot support wide and short

Early Prototypes and Lessons Learned

Audible click

Outline

• Hardware design• Signal processing algorithm• Tracking algorithm• Evaluation• Conclusion

Signal Processing

• Input: stream of height value• Output: doorway events D (tj,hj, vj)• Four algorithms– Doorway crossing detection– Noise filtering– Height estimation– Direction estimation

Signal Captured

Doorway Crossing Event

• Find timeout, multi-path, measurement events • Within 400 msecs of each other

Noise Filtering

Extend 200ms

Define clusters

Noise Filtering -- Obstacle

• Extends 30 seconds on either side– Remove any height measurement that is positive and identical

Height Estimation

• Multi-path reflections– Maximum measurement may fail– Typically only occur once

• Height estimation– If maximum height cluster exist• Max of the cluster

– Else • Maximum height

Direction Estimation

• Sensor tilts into the doorway• Three algorithms– Line slope– Compare max height timestamp to median– Compare min height timestamp to median

• Vote– Each algorithm estimate: +1, -1, 0– Sum all: [-3,3]

Outline

• Hardware design• Signal processing algorithm• Tracking algorithm• Evaluation• Conclusion

Tracking

• Input: sequences of detection events D• Output: Corresponding room states S, (r1i, r2i)• Ambiguity– False detections, miss detections

• Key insight– Ambiguities can often be resolved by future

observations

MHT Algorithm

• Multiple hypothesis tracking approach– Multiple alternative tracks are considered

simultaneously

• As new events are processed– Tracks that are not consistent with the new

information are evicted

Overview

• Initial– All tracks created with identical weight– For 2 persons + K rooms, K2 tracks are created

• Update– For each doorway event

• Update track• Update weight (based on prior training study)

• Merging and Evicting– Evicting low weight tracks– Merging duplicate tracks

Prior Training Study

• Find conditional probabilities– p(H|O) – a height measurement given the origin– p(V|O) – a direction measurement given the origin– p(H = ) – probability of missed detection

• Origin -- Person A, or B, or false detection• Training period– Each individual walks under each doorway

multiple times

Creating Tracks

• Initial tracks – every possible combination

• For each new doorway event– Between rooms i and j– Five new states are possible• a/b move to room i/j + false detection

– Duplicate every track 5 times

Weighting Tracks

• New weight is– Old weight multiply by– Probability of the origin moved through doorway

m given height measurement– Probability of moving from room p to m given the

direction measurement– Probability of moving from the last observed room

m-1 to p without having detected

Merging and Evicting Hypotheses

• “N-best” eviction policy– Keep the n best

tracks• Problem –

duplicate tracks• Track merging

algorithm

1

2

3

4

Outline

• Hardware design• Signal processing algorithm• Tracking algorithm• Evaluation• Conclusion

Experimental Setup

• Built 43 ultrasonic doorway sensors– Deployed across 4 different homes – Periods of 6-18 months– Used for development, testing, and iterative design

• For this evaluation– Performed 3 controlled experiments– 3 different pairs of testers– Randomly walk around– Collect ground truth with handheld device– 3000 unique doorway events

Evaluation Metric

• Type 1: correct state• Type 2: wrong person• Type 3: false room transition• Type 4: missed room transition

Tracking Accuracy

False Detections and Missed Detections

• Precision:– The number of false detections divided by the number of total detections

• Recall – Number of missed detections divided by the number of true doorway

crossing events

Height Measurement Accuracy

Direction Measurement Accuracy

Systems Performance

• Average 24 states, max 55 states per track• Real time, online

– With 500 ms look-ahead window

Limitations

• Fall short of true in-situ experiments– Controlled experiments

• Do not capture long-term effects• A proof-of-concept for Doorjamb tracking• Scalability– Typical homes with 3-4 people

• Requires calibration and training• Does not detect children

Conclusion

• Track people in homes with room-level accuracy

• Unobtrusive• Achieve 90% tracking accuracy• My opinions– Well written complete work– Not so sexy– Has it’s own selling points