Wearable Technology: the wave of the futurefiles.meetup.com/1698110/Dehzangi - Presentation.pdf ·...

Omid Dehzangi

Computer and Information Science

University of Michigan - Dearborn

Wearable Sensing and Signal Processing Lab

Wearable Technology: the wave of the future

Outline

Introduction to wearable technology

Vision and mission

Application and high level model design

Wearable platform design and development

My research contributions

Brain-computer interface

Activity of daily living (ADL) monitoring

My current research plans

2

Technology Trends

Transistors

Digital Processing

Brought to homes

3

Analog computer Personal Computers Wearable Computers Today’s Computers

Smaller

Slimmer

Faster

Hand held

Smarter

Hands free

Natural Interface

Wearable Technology

ABI Research has projected that by 2016, wearable wireless device sales will reach more than 100 million devices annually.

The market for wearable sports, fitness, and healthcare monitoring devices cover 80% of it.

The market for wearable technologies in healthcare "is projected to exceed $2.9 billion in 2016 (at least half of all wearable technology)

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Photo courtesy of http://www.phonearena.com/

Vibrotactile Modules

Wearable Computers

5

Phantom Photo Courtesy of

SenseGraphics

Haptics Deep Brain Stimulation Photo Courtesy of

mindmodulation.com

GUI-based feedback

Feedback

Sensors Dry-contact

EEG

Inertial

Sensors

Galvanic Skin

Response

Flex, Pressure and Piezo-electric

Sensors

Wearable Computers

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Sensors

Processing Unit

Communication

Information Fusion

Prediction/Detection

Data Analytics

Big data analysisData miningMachine learningPredictive modelingStatistical analysis

Signal Processing

Feedback

Outline


Vision and mission Application and high level model design


7

Research Vision

Applications

Model design

Algorithms

and analytics

System

integration

Technologies

Demonstrate the linkage between discovery

and societal benefit

Validate real pains and necessities and identify

effective high level solutions

Design and develop in multiple technical levels

Resolve upcoming challenges in practice

Generate transitioning technologies

Wearable platform

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Wireless Health Ubiquitous monitoring and intervention for the applications of health-care and wellness

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Courtesy of Misha Pavel, Program Director, National Science Foundation

Outline


Vision and mission



My current research contributions


Activity monitoring and motion detection

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WEARABLE BRAIN COMPUTER INTERFACE

Application Case Study

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Applications

Brain Computer Interface

• Brain Computer Interface

– Provide a non-muscular avenue for the user to communicate with others and to control external devices

– Infer user’s intentions using brain activities

• Applications

– Assist locked-in individuals to interact with cyber and physical system

– Gaming

– Diagnosis and treatment for neurological disorder

12

Wearable EEG Systems • Smaller form factor (size

of a credit card vs. bulky amps)

• Quicker setup time (seconds vs. 30 mins)

• Faster software training (5 mins vs. 30 mins)

• Quicker EEG signal detection (seconds vs. minutes)

• No need for EEG tech

13

Custom-designed mobile EEG-based BCI Dry-contact electrodes

Low-noise front-end (ADS1299)

Low power processing (MSP430)

Low component count

Bluetooth low energy (TI BLE)

communication module

Wearable EEG-Based BCI

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Wearable BCI Units

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Canonical Correlation Analysis(CCA)

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Picture taken from ref.1

Video

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C:/Users/Omid/Google Drive/Research Pictures/EEG/20141128_143115.mp4

USING GAIT AND SWAY BIOFEEDBACK TO REDUCE FALLS IN THE ELDERLY

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Applications

ACTIVITY OF DAILY LIVING MONITORING

• Dehzangi, Omid, Biggan, John, Birjandtalab Golkhatmi, Javad, Ray, Christopher, Jafari,

Roozbeh, “An Inertial Sensor-Based Method for Early Detection and Prevention of

Excessive Sway in Older Adults via Gait Analysis and Vibrotactile Biofeedback”, Gait &

Posture journal.

• Dehzangi, Omid, Zhao, Zheng, Biggan, John, Ray, Christofer, Jafari, Roozbeh, “The

Impact of Vibrotactile Biofeedback on the Excessive Walking Sway and the Postural

Control in Elderly”, Wireless Health 2013, November 1-3, Baltimore, Maryland, 2013.


Postural control and gait analysis

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Sway Biofeedback for Fall Prevention

Fall is a considerable health concern in the elderly

Wearable kinematic biofeedback system to detect pre-cursors of falls based on the sway of the upper body and other gait parameters, and activate biofeedback

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Hardware Architecture

Laptop UART

BLE transceiver

Microprocessor

Motion Sensor:

Gyro &

Accelerometer BLE

I2C

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Software Architecture

Accelero

meter

X,Y

,Z

Sensor

CalibrationDrift

Detection

PI controller

DCM

Self-ad

aptiv

e

Angle T

hresh

old

Settin

g

Calibrated

Accelerometer

X,Y,Z

Calibrated

Gyroscope

X,Y,Z

Feedback

loop

Euler Angles

(roll, pitch)

X,Y

,Z

X,Y

,ZA

ccelerom

eter

X,Y

,Z

Gyro

scope

The developed system

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(a) (b)

(a) Our designed wearable low-power motion sensor board,

(b) Our biofeedback system, consisting of two motion sensor boards for the chest and the ankle along with the vibratory feedback modules.

Subjects:

24 older adults (age: M = 75.5, SD = 4.32 years; 10 females)

Procedure:

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Experiments

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Mean difference in the sway range.

The test Control Experimental P-value

Difference in the sway range

0.59±1.77 -0.60±0.63 0.04

The results of the statistical test on the sway range

Results and Analysis

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Identification of the gait phases on the ACC X readings

Gait phase analysis

Initial sway Mid sway Terminal sway Mid stance

The selected phases of a gait cycle.

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DTW extracted strides based on ACC X readings (Experimental)

Gait phase analysis

0 5 10 15 20 25-2

-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Sample

AC

C X

re

ad

ing

0 5 10 15 20 25 30-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

4

AC

C X

re

ad

ing

Sample

DTW extracted strides based on ACC X readings (Control)

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Mean difference in the variance of the gait phases between pre- and post- training.

Gait phase analysis

The gait phases Control Experimental P-value

Initial sway 0.17±0.62 0.40±0.15 0.09

Mid sway -1.54±1.72 -0.03±0.48 0.08

Terminal sway -1.29±1.04 0.038±1.01 0.05

Mid stance -0.18±0.89 -0.07±1.14 0.27

The results of the Chi-square test on the gait phases

Initial sway Mid sway Terminal sway Mid stance

Outline


Vision and mission



My current research contributions


Activity of daily living (ADL) monitoring

My Current Research Plans

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WEARABLE DRIVER MONITORING


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Applications

Goal

• To form relationships between biological state of the driver with his/her driving behavior

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Motivation:

Multi-Modal Driver Monitoring and Modeling via Heterogeneous Wearable Body Sensor Network

System

photograph:

a) b) c)

Integration of heterogeneous wearable monitoring technology, on-board sensing units, and wireless

networking capabilities : a) The full body sensor network, b) the portable EEG system, c) the OBD-II device

Body sensor networks are capable of generating a reliable human state model

Hypotheses:

1. Minimally intrusive: Driver behavior is not affected by

the devices that are used to acquire the necessary

biomedical markers

2. Comprehensive: the system will extract the data

collected from a large number of heterogeneous

sensors and correlate the various readings for earlier

detection

3. Ubiquitous and remotely available: The collected

measurements will be transmitted to a remote location

for longitudinal analysis and discover association in a

long term

4. Real-time responsive: The information will be

accessible in an online fashion to enable real-time

processing and decision making

5. User friendly: Suitable user interface and

visualization tools will be in place for a human user to

be able to interpret the acquired information

Hypothesis 1: Specific driver mental and

physical states can generate abnormal driving

behaviors and a high level of driving impairment.

Hypothesis 2: Driver biological states will have

an impact on his/her biometric measures while

driving. Biometric markers that correspond to

changes in performance of the impaired driver

subjects will aid in explaining the underlying

impact on driving outcome.

Hypothesis 3: There are signature patterns in

the biometric readings from the normal behavior

of the driver that can be non-invasively extracted

and employed for control, identification &

authentication, and interaction with other smart

infrastructures.

The Proposed Platform:


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Real-time Processing

EEG

ECG

OBD-II

DA

Q-F

rontE

nd

Data

Min

ing

Sensors In-Vehicle Mobile Device

Long-term analysis

Data

Base

Big

Da

ta

An

aly

sis

Backend Processing

Bio-feedback

Data

Vis

ua

liza

tion

GSR Data

Ma

trix

User Interface

Driver

DA

Q-B

ack

En

d

GPS

TrafficB

lue

To

oth

Mo

bile

Ne

two

rk

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Platform User Interface

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Characterizing Driver Distraction

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Characterizing Driver Distraction:

Two rounds of driving:

1. Non-peak traffic period,

2. Peak traffic period

Objective:

Investigate the effect of the road condition

on the driver distraction

Hypothesis:

Theta and beta power increase in the EEG

spectrum is related to distraction effects

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30

Comparison of total theta and beta power (dB×103) in

the subjects averaged EEG in frontal component

between round 1 and round 2

Subjects Round 1 Round 2

theta beta theta beta

Sbj 1 1.8 1.2 1.5 1.2

Sbj 2 2.8 1.6 1.7 1.3

Sbj 3 2.5 1.5 1.3 1.1

Avg 2.33 1.5 1.48 1.27

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-0.1 -0.05 0 0.05 0.1 0.15-0.1

-0.05

0

0.05

0.1

0.15

x

y

pdf(obj,[x,y])

-0.1 -0.05 0 0.05 0.1 0.15-0.1

-0.05

0

0.05

0.1

0.15

x

y

pdf(obj,[x,y])

the effect of the driving condition on the driver distraction

-0.1 -0.05 0 0.05 0.1 0.15-0.1

-0.05

0

0.05

0.1

0.15

x

y

pdf(obj,[x,y])

-0.1 -0.05 0 0.05 0.1 0.15-0.1

-0.05

0

0.05

0.1

0.15

x

y

pdf(obj,[x,y])

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Ideas to Pursue

Towards proactive driver monitoring and safety platform as

advancement in automated passenger vehicle infrastructure.

Connect the vehicle occupants to the loop via development of D2V & D2I

in automated vehicles to improve occupant safety, performance, health &

wellness.

The connected vehicle infrastructure will associate the driver with the

smart city and/or smart home infrastructure to optimize his/her daily

operations.

Driver identification and authentication is an important outcome which

will be performed non-invasively via extracting the signature biometrics

from the normal behavior of the driver.

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Thanks for your attention

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Wearable Technology: the wave of the futurefiles.meetup.com/1698110/Dehzangi - Presentation.pdf ·...

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