Body Sensor Networks: An Application-Centric Approach

Body Sensor Networks:

An Application-Centric Approach

John LachJohn Lach

Charles L. Brown Department of Electrical & Computer Engineering

University of Virginia

[email protected]

Invasive

Under-S

ampled

Imprecise

Inaccurate

Qualita

tive

Expensive

In-Patient

Monitoring

Limitations of Healthcare Data Collection

• Physicians, nurses,

caretakers, and medical

researchers depend on high

quality data to monitor and

assess patients and study,

diagnose, and treat diseases

Project Motivation

Monitoring

Expert

Observation

Patient

Self-Reports

SignificantInsignificant


• But current data collection

mechanisms have limitations

Invasive

Under-S

ampled

Imprecise

Inaccurate

Qualita

tive

Expensive

In-Patient

Monitoring

Limitations of Healthcare Data Collection

• Physicians, nurses,

caretakers, and medical

researchers depend on high

quality data to monitor and

assess patients and study,


Project Motivation

Monitoring

Expert

Observation

Patient

Self-Reports

Body Sensor

Networks

SignificantInsignificant


• But current data collection

mechanisms have limitations

• Wireless body sensor

networks (BSNs) are

emerging as a promising

technological solution

Wireless Health Infrastructure

• HardwareCollecting the data and

getting it where it

needs to go

• SoftwareConverting data into

medically relevant medically relevant

information

• DatabaseEnabling access to

information for diverse

stakeholders

• Interfaces

between them

4

Big Wireless Health Questions

• What value does Wireless Health promise?– Improve patient care

– Enhance wellness

– Enable aging-in-place

– Lower healthcare costs

– …– …

• How can this value be demonstrated?– Need application domain expertise

• Collaborate, collaborate, collaborate

– Deploy platforms in real human subjects studies

– Convert data to medically relevant information

– Identify and use metrics appropriate for target application(s)

– Science-based research5

• “An interdisciplinary team of researchers focusing

on technology to improve healthcare…and

healthcare to improve technology”healthcare to improve technology”

• Co-directors: John Lach (ECE), Steve Patek (SIE),

Jack Stankovic (CS)

• Members from Neurology, Neurosurgery,

Neuropsychology, Nephrology, Biomechanics,

Orthopedic Surgery, Cardiology, Geriatric Nursing,

Psychiatry, etc.6

• Application-driven engineering research

– Embedded systems, circuits, and signal processing expertise

– Strong relationships with medical personnel and patients

– Real systems deployed in real clinical applications, including rapid prototyping

• Application focus is on movement disorders, requiring high precision motion capture and analysis tomotion capture and analysis to

– Study movement disorder etiology and progression

– Increase movement disorder diagnostic sensitivity

– Evaluate efficacy of therapeutic intervention

• Technology goal: maximize wearability and battery life of body sensor

networks (BSNs) while delivering required motion analysis fidelity

• Overall goal: improve healthcare and medical research to ultimately

improve patient outcomes and quality of life while reducing healthcare

costs

7

BSN Research Cycle

8

DBS-based Management of Tremor

• Tremor can impair quality of life for advanced Parkinson’s Disease (PD) and Essential Tremor (ET) patients

• Deep brain stimulation (DBS) offers tremor relief for recalcitrant, advanced tremorrecalcitrant, advanced tremor

• Drs. Jeffrey Elias and Robert Frysinger (UVA Neurosurgery Department) desired to study impact of different DBS settings on PD and ET with quantitative tremor analysis– Occurrence– Frequency– Magnitude– Classification

Image Credit: Wired Magazine 15.03, 20079

BSN Research Cycle

Wearable Inertial

Sensor Nodes

Prototype

10

Joint Time-Frequency

Analysis of Motion

Continuous, Non-Invasive

Tremor Measurement

TEMPO 1

TEMPO1 Data Logger

Mixed-Signal Microcontroller with Custom RTOS

Capture

Compare

Registers

Flash

Memory

User

I/O

General

Purpose

I/O

Bus

Controller

USB

Interface

Serial

Peripheral

Interface

TEMPO1 Sensor TEMPO1 Sensor

MEMS

Sensor

PWM

Generator

Signal

Conditioner Data

11

TEMPO 3

12

TEMPOS, Aggregator, Explorer

13

• Complete power-aware, physically-aware re-

design of BSNs

• Low power CPU/transceiver

• Software stack takes advantage of new HW

Next Gen: Custom ICs for BSNs

• Software stack takes advantage of new HW

• 2+ orders of magnitude less energy

14

from Ben Calhoun

TEMPO 1,

TEMPO 2,

TEMPO 3

BSN Research Cycle

15

• Parkinson’s disease – study efficacy of deep brain stimulation– Jeffrey Elias, Robert Frysinger (UVA Neurosurgery)

• Parkinson’s disease – study impact of cognitive stress– Scott Wylie (UVA Neurology)

• Normal pressure hydrocephalus – improve diagnosis– Jeffrey Barth, Donna Broshek, Jason Freeman (UVA Neuropsychology)

• Geriatrics – enable fall risk assessment in naturalistic settings

TEMPO in Human Subject Studies

– Thurmon Lockhart (Virginia Tech Locomotion Laboratory)

– Mark Williams (UVA Geriatric Medicine)

– Amy Papadopoulos, Cindy Crump (AFrame Digital)

• Dialysis – study cause of higher fall rates– Emaad Abdel-Rahman (UVA Nephrology)

• Cerebral palsy – study efficacy of orthopedics for children– Brad Bennett (Kluge Children’s Rehabilitation Center)

• Dementia – assess physical agitation– Azziza Bankole, Martha Anderson, Aubrey Knight (Carilion Center for Healthy Aging)

• Multiple sclerosis – gait analysis for early diagnosis– Myla Goldman (UVA Neurology)

16

Tremor AssessmentRaw 3-axis Tremor Acceleration Waveforms

Teager Tremor Waveform

17

Tremor Assessment

18

Head

Right Hand

Left Hand

DBS Best Setting DBS Off

TEMPO 1,

TEMPO 2,

TEMPO 3

BSN Research Cycle

Teager & STFT Energy,

ANN, SVM, KNN,

Real-time variance,

Haar DWT

Tremor, Gait Disorders, Fall

Risk, Agitation, …

19

• To make health assessment of human

movement a reality, platforms will need:

– Smaller form factor (invisible to the user) for

unobtrusive, naturalistic use

BSN Requirements

unobtrusive, naturalistic use

– Longer battery life (from hours now to days and

weeks in the near future) for continuous,

longitudinal monitoring

– Higher application fidelity (data that reliably yields

clinically-meaningful, non-obvious information) for

delivering value

20

TEMPO Empirical Observations

~5 Hr

• Transmit compressed data

• Transmit only when necessary

~5 Hr

Battery Life

for TEMPO

3

necessary

• Transmit information instead of data

• Power off gyros when possible

On-node signal processing is key to energy efficiency

21

What if this is

insufficient?

• Reducing energy use (and increasing battery life) is

easy. We simply need to reduce the amount of

transmitted data for a given BSN application.

– BUT…reduction of data may decrease application fidelity

• So move processing to the periphery of the network

Research Direction

• So move processing to the periphery of the network

and reduce transmission energy

– BUT…processing consumes power and resources are

limited (lack of FPU, memory, clock speed, etc.)

A framework is needed to both understand and

manipulate the energy-fidelity tradeoff for healthcare

BSN applications

22

Effects of Lossy Compression• Assume an application accepts

a maximum MSE distortion of

100 (from raw 12-bit ADC

acceleration)

• For fixed compression ratio:

– Distortion above this

threshold violates the

23

For a given data rate, distortion will change over time,

so dynamic management of data rate is desirable

threshold violates the

requirement, so data rate

must increase

– Operating well below the

threshold is energy inefficient

since data-rate can be further

reduced

Energy-Fidelity FrameworkBSN Hardware

Measured

Time

Energy-Fidelity

Tradeoff Space

Epb

Energy Model

LabVIEW

Source

C Source

Data Rate

Reduction

Techniques

BSN Tremor DataFidelity Measurer

Raw Data

CR MSE

24

Dynamic Energy-Fidelity Management

25

What is the right measure of “fidelity”?

Almost certainly NOT MSE!!!

• A data controller and a destination controller

• Runtime optimization of tradeoffs between power consumption, computational complexity, and signal fidelity

• Adjust to system dynamics– Data characteristics

– Wireless channel characteristics

– Application priorities

BSN Control Architecture

26

• DVFS promotes power savings when

timing and/or processing

requirements are relaxed

– Takes advantage of the relationship

of supply voltage and operating

frequency in CMOS circuits

• DVFS is found in higher powered

Dynamic Voltage-Frequency Scaling for BSNs

using COTS Components

Tremor Inertial Accelerometer Data

No activity -

”lightweight

calculations”

Active Tremor

α⋅⋅⋅ FVC=P DDp

2

• DVFS is found in higher powered

consumer mobile electronics and

cutting edge, low-power circuit

research

• Common BSN microcontrollers can

operate over a range of voltages and

frequencies

– On-chip oscillators for clock

generation

• MSP430 maximum operating

frequency equation:

Tremor Inertial Accelerometer Data

Teager “Energy” Calculation

No Teager

computation

requiredGreater

Processing

Load

( ) 6100.667.6)( ×−⋅ DDDCO V=HzF

27

TEMPO 1,

TEMPO 2,

TEMPO 3

Energy-Fidelity Scalability,

Application Profiling, DVFS,

BCC

BSN Research Cycle

Teager & STFT Energy,

ANN, SVM, KNN,

Real-time variance,

Haar DWT

Tremor, Gait Disorders, Fall

Risk, Agitation, …

28













29

Portable, Non-Invasive Fall Risk Assessment

in End Stage Renal Disease Patients on HemodialysisThurmon E. Lockhart, Emaad Abdel-Rahman, John Lach

30

Major Findings:•Get-up & Go Time:

•Post-HD > pre-HD

� after HD session: less mobility

•PLM Simultaneous Index:

Pilot Results

•PLM Simultaneous Index:

•Post-HD < pre-HD

� after HD session: less mobility

•Strength:

•Post-HD < pre-HD

� after HD session: less strength

•Gait parameters and stability:

•No significant difference

�ESRD patients’ fall problem is unique

Pending R21 proposal (NIA) for further exploration and out of clinic monitoring

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32

Enabling Longitudinal Assessment of

Ankle-Foot Orthosis Efficacy for Children with Cerebral Palsy

Bradford C. Bennett, John Lach

• AFO goals

– Facilitate walking by controlling the position of the ankle and providing

a base of support

– Prevent contractures by putting muscles in a lengthened position and

providing variable ranges of motion

– Prevent deformity by controlling the position of the foot/ankle

Ankle-foot orthosis (AFO) Children with equinus pattern gait (A)

and crouch pattern gait (B) deformity

– Prevent deformity by controlling the position of the foot/ankle

• Joint angles, gait moments, and gait velocity assessments in

VICON laboratories

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AFO-Embedded Inertial Sensing• Validation against VICON

• 4 CP children to date

• Preparation for in-field data collection

• Working with prosthetics manufacturers

Ankle Joint Angle Shank Angular Velocity34













35

Continuous, Non-Invasive Assessment of Agitation in

Dementia Using Inertial Body SensorsAzziza Bankole, Martha Anderson, Aubrey Knight, Tonya Smith-Jackson, John Lach

• Agitation is common in dementia patients

– Major caregiver burden

– Changes over time and in response to stimuli

• Approaches to mitigate agitation require evaluation• Approaches to mitigate agitation require evaluation

• Cohen-Mansfield Agitation Inventory (CMAI) requires

sampled expert observation

• Need for continuous, longitudinal monitoring

36

• Explored use of inertial BSNs for

physical agitation assessment

• Jerky, repetitive movements

correlate with physical agitation– Requires joint time-frequency analysis

• Pilot study: six dementia subjects

Inertial BSNs for Agitation Assessment

• Pilot study: six dementia subjects

in long term care facility– Three 3-hour sessions over six weeks

(different times of day)

– Three TEMPO nods (dominant wrist,

waist, and opposite leg) with triaxial

accelerometers

– Suggested Teager energy a good measure

of physical agitation

37

• Epilepsy – detect seizures

• Parkinson’s disease – study fall risk

• Traumatic brain injury – measure head forces

• Dementia – study relationship between agitation & incontinence

• Posture – provide real-time feedback to users

Planned Human Subject Studies

• Posture – provide real-time feedback to users

• Autism – improve diagnosis using motor skill assessment

• Prosthetics – develop smarter controls

• Stroke – track stroke rehabilitation progress via telemedicine

38

Acknowledgments

• This work is funded in part by

– National Science Foundation and National Institutes

of Health

– The UVA Biomedical Innovation Fund and the UVA – The UVA Biomedical Innovation Fund and the UVA

Institute on Aging

– Philips, Carilion Health Systems, and the MITRE

Corporation

39

40

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ASSIST Platform Evolution

Gen 2: Add

Gen 1:� EKG� NOx

� pH� Skin Resistance� Temperature� Battery Assist

Gen 2: Add � Pulse Oxymetry

� CO, Ozone, H2S

� Skin Resistance

� Stress Response

� Lung Sounds

Gen 3: Add� Ultra-miniature� Particulate Matter� Breath NOx, pH� Self-powered� ~0.1 mJ/sample with

Advanced Computation

42

ASSIST’s Strategic Leadership

43

• Ongoing human subjects studies with clinician and medical researcher collaborators

– Tremor assessment for Parkinson’s Disease study, diagnosis, treatment

– Gait analysis for movement disorder diagnosis and fall risk assessment

– Agitation in dementia patients– Many others… TEMPO sensor node provides 6 degrees of freedom motion

We are using a vertically integrated approach to develop miniature on-body wireless sensor

nodes for continuous, long-term, non-invasive collection of accurate, precise, quantitative data

for a variety of healthcare and medical research applications.

Summary

– Many others…

• Key system metrics– Wearable (small, light, easy to use)– Low power (long lifetime with small battery)– Configurable (adaptable for specific applications)– High fidelity (achieve application requirements)

• Key engineering research thrusts– Embedded, low-power system design– Adaptive wireless communication systems– Resource-constrained DSP algorithms– Sensor fusion– Biomedical analysis and feature detection

• Project goal: Improve quality of medical data and ultimately patients’ health and quality of life while reducing healthcare costs

TEMPO sensor node provides 6 degrees of freedom motion

capture (3 axes of both linear acceleration and rotational rate)

in the form factor of a wristwatch

Gait cycle data collected from an ankle-worn TEMPO node

44

Body Sensor Networks:

An Application-Centric Approach

John LachJohn Lach

Charles L. Brown Department of Electrical & Computer Engineering

University of Virginia

[email protected]

Documents

Body Sensor Networks: An Application-Centric Approach