Modeling RFID Signal Reflection for Contact-free Activity ...csywwang/papers/... · Signal...
Transcript of Modeling RFID Signal Reflection for Contact-free Activity ...csywwang/papers/... · Signal...
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Yanwen Wang Yuanqing Zheng
Modeling RFID Signal Reflection for Contact-free Activity Recognition
UbiComp 2019, LondonSeptember 11th, 2019
The Hong Kong Polytechnic University, Hong Kong, China
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CONTENTS
▪ Introduction
▪ Methodology
▪ Evaluation
▪ Conclusion
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Introduction
Human Activity Recognition necessity01
◆ Falls Are Serious and Costly
•Each year, 3 million older people are
treated in emergency departments for
fall injuries.
•Over 800,000 patients a year are
hospitalized because of a fall injury.
•Falls are the most common cause of
traumatic brain injuries (TBI).
Bergen G, Stevens MR, Burns ER. Falls and Fall Injuries Among Adults Aged ≥65 Years — United States, 2018. MMWR Morb Mortal Wkly Rep 2016;
65:993–998. DOI: http://dx.doi.org/10.15585/mmwr.mm6537a2
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Introduction
Human Activity Recognition necessity01
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❖ 17.8% had one-time in-home falls
❖ Post fall medical outpatient was 41.5%
❖ 50% of the citizens do not deploy any preventive equipment at home
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Privacy concern, LoS dependent
Introduction
Camera-based Activity Recognition
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[1] Fang, Biyi, Xiao Zeng, and Mi Zhang. "Nestdnn: Resource-aware multi-tenant on-device deep learning for continuous mobile vision." In ACM MobiCom, 2018.[2] Xu, Mengwei, et al. "DeepCache: principled cache for mobile deep vision." Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. ACM, 2018.
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▪ Direct body contact
Wearable sensor-based Activity Recognition
Introduction01
▪ Inconvenience▪ High cognitive load 6
[1] Han Ding, Longfei Shangguan, Zheng Yang, Jinsong Han, Zimu Zhou, Panlong Yang, Wei Xi, and Jizhong Zhao. Femo: A platform for free-weight exercise monitoring with rfids. In ACM SenSys, 2015.[2] Yuxiao Hou, Yanwen Wang, and Yuanqing Zheng. TagBreathe: Monitor Breathing with Commodity RFID Systems. In IEEE ICDCS, 2017.
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Wireless Signal
RF Signal
Transceiver
Phase
Introduction01
Wireless signal-based Activity Recognition
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Wi-Fi
Low resolution
FMCW
Specialized devices
High cost
USRP
Specialized devices
High cost
Introduction01
Wireless signal-based Activity Recognition
▪ Low resolution▪ High deployment cost
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Camera Sensor Wireless RFID
X LoSX Privacy
X IntrusiveX Direct contact
X High costX Low resolutionX Specialized devices
√ Safe√ NLoS√ Convenient√ Contact free√ Non-intrusive
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Contact-free
Signal reflection model
RFID
Methodology02
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ReaderTag
Methodology02RFID System
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➢ If the signal propagation distance changes continuously, the signal phase will change from [0,2π]
➢ If the signal propagation distance
changes one wavelength, the
signal phase will change 2π
Reader
Phase change of the signal
𝜆/2 𝜆0 3𝜆/2
Methodology02
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If RFID tags are attached on human body (clothes), one may infer the type of human activity
???
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Contact-free Activity Recognition -----The TACT System
Reflection model for contact-free activity recognition13
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• Preliminary experiments
Understanding the Reflection of RFID Signal
➢The phase values continuously changed and exhibited a periodic pattern.
➢The range of phase values was only around 0.2, which was much smaller than 2π 14
motionless tag
motionless reader
Signal propagation distance change
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Static Virtual Point: All the signal reflected from static objects & LoS
Object moving:Moving from A to B
Signal Reflection Model
▪ When the object moves, the dynamic component rotates▪ The combined phase reaches maximum and minimum at
two tangent points▪ The combined phase periodically changes▪ The combined phase range is 𝚫𝜽
In-phase and Quadrature components
Q
I
𝚫𝜽
Phase Change of SignalSignal Reflection model Static component
dynamic component
combined component
Methodology02
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Signal Reflection modelMethodology02
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0 1Signal phase
∈[𝜽,𝜽+𝚫𝜽],𝚫𝛉<𝟐𝛑;
0 2
The phase waveform may not be a standard
sinusoid form
0 3
Dynamic & Combined share
same period
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Phase waveform
DTW
Time Duration
Variance
Speed
STFT
Distance
Phase difference
Feature ExtractionMethodology02
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Misalignment Elastic
Property
Different activities have different phase waveforms, while same activities share similar phase waveforms
Dynamic Time Warping (DTW)
Unknown waveform
Methodology02
❖Phase waveform
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❖Time Duration
Objective: To segment the data corresponding to human activities
The variance of phase readings can serve as a good indicator for activity segmentation.
Methodology02
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Objective: To measure the moving speed of different activities.
𝑣 = 𝑓 × 𝜆
A faster movement results in more rapid fluctuation of phase waveform --- the frequency of phase waveform.
Method: Short-Time Fourier Transform (STFT)⚫ Instantaneously frequency
Spectrogram of walking and falling
walking falling
Methodology02
❖Moving speed
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Objective: To measure the moving distance of different activities.
𝐷 =
𝑖=1
𝑁−1𝜆
2𝛥𝜃× (|𝜃𝑖+1 − 𝜃𝑖|)
Extract distance from phase difference of 2 consecutive phases.
𝐷 =
𝑖=1
𝑁−1𝜆
2π× (|𝜃𝑖+1 − 𝜃𝑖|)
Methodology02
❖Moving distance
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Coarse-grained features: duration of activity, speed, distance
Fine-grained features: phase waveform
T V D 𝑑1 𝑑2 𝑑3 𝑑4 𝑑5 𝑑6
𝑑1 𝑑2 𝑑3 𝑑4 𝑑5 𝑑6T V D
Random Forestunknown
Tag1: Activity ranking
Tag2: Activity ranking
Tag3: Activity ranking
average Activity ranking
𝑑7 𝑑8
𝑑7 𝑑8
Methodology02
❖Classification
Fine-grainedCoarse-grained
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System Performance
Robustness
Extracted Features
Evaluation03
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Experiment settings
COTS RFID System
◆ Impinj R420 Reader
◆Commodity passive tags
◆Directional Antenna
Room C
X 12
640 traces for training 1280 traces for evaluation10-fold cross-validation
Evaluation03
Room A
◆Small size with 3m*2m
◆ Large size with 10m*8m
Room B
◆Median size with 8m*6m
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▪ The speeds of falling and walking significantly differ with other activities
▪ Walking has longer moving distances
▪ Other activities are difficult to distinguish
▪ Only using coarse-grained feature may not work
Extracted FeaturesEvaluation03
Coarse-grained features
58%
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Fall
Walk
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81%
System PerformanceEvaluation03
Fine-grained features
8 DTW distances
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Combined features
93%
System PerformanceEvaluation03
Fine-grained + Coarse-grained features
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Compare to existing work
RF-Care: Lina Yao, Quan Z. Sheng, Wenjie Ruan, Tao Gu, Xue Li, Nick Falkner, and Zhi Yang. 2015. RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive
RFID Tag Array. In proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS’15).
Six classifiers
System Performance
Evaluation03
Our TACT system significantly outperforms RF-Care
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RobustnessEvaluation03
Deploying multiple tags indeed can improve the system performance
Environment has relatively small impact on the performance
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Conclusion
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Conclusion04
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Thanks!
Yanwen Wang Yuanqing Zheng
UbiComp 2019, LondonSeptember 11th, 2019
The Hong Kong Polytechnic University, China
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ExtractionPre-process IdentificationClassification
Data
Remove
Frequency
Hopping
Features
Coarse-grained
Fine-grained
Features
Classifier
Random Forest,
Neural Network,
Decision Tree,
SVM, Naïve
Bayesian, QDA
Activities
Walking, Falling,
Sitting, Standing,
Raisehand, Drophand,
Rotation, Get-up
Methodology02
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