Understanding and Modeling of WiFi Signal Based Human Activity - - PowerPoint PPT Presentation

Understanding and Modeling of WiFi Signal Based Human Activity Recognition

Wei Wang†, Alex X. Liu†‡, Muhammad Shahzad‡,Kang Ling†, Sanglu Lu†

†Nanjing University, ‡Michigan State University

September 8, 2015

1/24

Motivation Modeling Design Experiments Conclusions

Motivation

• WiFi signals are available almost everywhere and they are

able to monitor surrounding activities.

2/24

Motivation Modeling Design Experiments Conclusions

Motivation

• WiFi signals are available almost everywhere and they are

able to monitor surrounding activities.

2/24

Motivation Modeling Design Experiments Conclusions

Problem Statment

WiFi based Activity Recognition

• Using commercial WiFi devices to recognize human activities.

Wireless router Laptop

Wireless signal reflection

Advantages Work in dark Better coverage Less intrusive to user privacy No need to wear sensors

3/24

Motivation Modeling Design Experiments Conclusions

Problem Statment

WiFi based Activity Recognition

• Using commercial WiFi devices to recognize human activities.

Wireless router Laptop

Wireless signal reflection

Advantages Work in dark Better coverage Less intrusive to user privacy No need to wear sensors

3/24

Motivation Modeling Design Experiments Conclusions

Problem Statment

WiFi based Activity Recognition

• Using commercial WiFi devices to recognize human activities.

Wireless router Laptop

Wireless signal reflection

Advantages Work in dark Better coverage Less intrusive to user privacy No need to wear sensors

3/24

Motivation Modeling Design Experiments Conclusions

Problem Statment

WiFi based Activity Recognition

• Using commercial WiFi devices to recognize human activities.

Wireless router Laptop

Wireless signal reflection

Advantages Work in dark Better coverage Less intrusive to user privacy No need to wear sensors

3/24

Motivation Modeling Design Experiments Conclusions

Problem Statment

WiFi based Activity Recognition

• Using commercial WiFi devices to recognize human activities.

Wireless router Laptop

Wireless signal reflection

Advantages Work in dark Better coverage Less intrusive to user privacy No need to wear sensors

3/24

Motivation Modeling Design Experiments Conclusions

Challenges

• Measurement from commercial devices are noisy and have

unpredictable carrier frequency offsets

• Needs robust and accurate models to extract useful infor-

mation from measurements

11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 4/24

Motivation Modeling Design Experiments Conclusions

Understanding Multipath Key observations

• Multipaths contain both static

component and dynamic com- ponent

• Each path has different phase
• Phases determine the ampli-

tude of the combined signal

Sender Receiver Wall Reflected by body Reflected by wall LoS path dk(0) dk(0)

5/24

Motivation Modeling Design Experiments Conclusions

Understanding Multipath

Sender Receiver Wall Reflected by body Reflected by wall LoS path dk(0) dk(0) I Q Combined Static component Dynamic Component

6/24

Motivation Modeling Design Experiments Conclusions

Understanding Multipath

Sender Receiver dk(t) Wall Reflected by body Reflected by wall LoS path LoS path I Q Combined Static component Dynamic Component

6/24

Motivation Modeling Design Experiments Conclusions

Understanding Multipath

Sender Receiver dk(t) Wall Reflected by body Reflected by wall LoS path I Q Combined Static component Dynamic Component

6/24

Motivation Modeling Design Experiments Conclusions

Understanding Multipath

Interpreting CSI amplitude

• Phases of paths are deter-

mined by path length

• Path length change of one

wavelength gives phase change of 2π

• Frequency
• f

amplitude change can be converted to movement speed

I Q Combined Static component Dynamic Component

7/24

Motivation Modeling Design Experiments Conclusions

CSI-Speed Model How accurate is it?

• Wave length → 5 ∼ 6cm in 5 GHz band

2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 50 100 150 200

Time (seconds) CSI power

Waveform with regular moving speed

CSI amplitude changes are close to sinusoids

8/24

Motivation Modeling Design Experiments Conclusions

CSI-Speed Model How accurate is it?

• Wave length → 5 ∼ 6cm in 5 GHz band

2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 50 100 150 200

Time (seconds) CSI power

Waveform with regular moving speed

CSI amplitude changes are close to sinusoids

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.2 0.4 0.6 0.8 1

Measurement error (meters) CDF

Moving distance measurement error

Average distance measurement error of 2.86 cm

8/24

Motivation Modeling Design Experiments Conclusions

CSI-Speed Model How robust is it?

• Robust over different multipath conditions and movement

directions

• Linear combination of multipath do not change frequency

0.2 0.4 0.6 0.8 1 1.2 1.4 0.05 0.1 0.15 0.2 0.25

Estimated speed (m/s) Probability

running walking sitting down

Speed distribution of different activities in different environments

9/24

Motivation Modeling Design Experiments Conclusions

CSI-Activity Model Activities are characterized by

• Movement speeds
• Change in movement speeds
• Speeds of different body components

2 2.5 3 3.5 4 −15 −10 −5 5 10 15

Time (seconds) CSI

Walking

0.5 1 1.5 2 −40 −20 20 40

Time (seconds) CSI

Falling

0.5 1 1.5 2 −40 −20 20

Time (seconds) CSI

Sitting down

10/24

Motivation Modeling Design Experiments Conclusions

CSI-Activity Model

• Use time-frequency analysis to extract features
• Use HMM to characterize the state transitions of movements

Walking Falling Sitting down

11/24

Motivation Modeling Design Experiments Conclusions

CSI-Activity Model

• Build one HMM model for each activity
• Determine states based on observations in waveform pat-

terns

• State durations and relationships are captured by transition

probabilities

State 3 State 2 State 1 State 4

12/24

Motivation Modeling Design Experiments Conclusions

System Architecture

CSI measurement collection Noise reduction HMM training

HMM Model

Feature extraction HMM based activity recognition

Activity data collection Model generation

Activity detection and segmenting

Online monitoring

Monitoring records

13/24

Motivation Modeling Design Experiments Conclusions

Data Collection

N M 30 subcarriers

N × M × 30 CSI streams

11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds)

14/24

Motivation Modeling Design Experiments Conclusions

Noise Reduction

Correlation of CSI on different subcarriers

• Subcarriers only differ slightly in wavelength
• Subcarriers have the same set of paths, with different phases

Frequency 312.5kHz Wave length = 5.150214 cm Wave length = 5.149662 cm

15/24

Motivation Modeling Design Experiments Conclusions

Correlation in CSI Streams

16/24

Motivation Modeling Design Experiments Conclusions

Noise Reduction

Combines N × M × 30 subcarriers using PCA to detect time- varying correlations in signal

11 11.5 12 12.5 60 65 70 75 CSI Time (seconds)

Original

11 11.5 12 12.5 65 70 75 CSI Time (seconds)

Low-pass filter

11 11.5 12 12.5 −10 −5 5 10 Time (seconds) CSI

PCA

17/24

Motivation Modeling Design Experiments Conclusions

Real-time Recognition

• Activity detection
• Use both the signal variance and correlation to detect pres-

ence of activities

• Feature extraction
• Time-frequency analysis (DWT)
• HMM model building
• Eight activities

Walking, running, falling, brushing teeth, sitting down, opening refrigerator, pushing, boxing

• More than 1,400 samples from 25 persons as the training

set

18/24

Motivation Modeling Design Experiments Conclusions

Evaluation Setup

• Commercial hardware with no modification
• Transmitter: NetGEAR JR6100 Wireless Router
• Receiver: Thinkpad X200 with Intel 5300 NIC
• A single communicating pair is enough to monitor 450 m2
• pen area
• Measurement on UDP packets sent between the pair
• Sampling rate 2,500 samples per second

19/24

Motivation Modeling Design Experiments Conclusions

Evaluation Results

Activity recognized True activity R W S O F B P T E Running 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Walking 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sitting 0.000 0.000 0.947 0.030 0.011 0.000 0.012 0.000 0.000 Opening 0.000 0.005 0.150 0.803 0.042 0.000 0.000 0.000 0.000 Falling 0.000 0.010 0.041 0.010 0.939 0.000 0.000 0.000 0.000 Boxing 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 Pushing 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 Brushing 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 Empty 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

• Ten-fold validation accuracy: 96.5%
• Detects human movements at 14 meters
• Real-time recognition on laptops
• Packet sending rate can be as low as 800 frames per sec-
• nd

20/24

Motivation Modeling Design Experiments Conclusions

Evaluation on Robustness

• Models are robust to environment

changes

• Train once, apply to different sce-

narios

• Training use database collected in

lab with different users

• Test in with users not in the train-

ing set

• Open lobby
• Apartment (NLOS)
• Small office

Table

Table

Walking/running route

7.7 m 6.5 m 1.6 m

Fridge Training location Tx Rx Lab Table Fridge Kitchen Tx Bath room Rx

Testing location

Appartment 21/24

Motivation Modeling Design Experiments Conclusions

Evaluation on Robustness

• Consistent performance in unknown environments, with more

than 80% average accuracy

lab lobby apartment

• ffice

0.5 1

Environments Accuracy

R W S O F B P T

22/24

Motivation Modeling Design Experiments Conclusions

Conclusions

• CSI measurements contains fine-grained movement infor-

mations

• CSI-Speed model

quantifies the correlation between CSI value dynamics and human movement speeds

• CSI-Activity model

quantifies the correlation between the movement speeds of different human body parts and a specific human activity

• Our models are robust to environment changes

23/24

Motivation Modeling Design Experiments Conclusions

Q & A

Thank you! Questions?

24/24