7/27/2019 Wearable Sensor System

1/1

The four

different move-

ment disorders

t h a t

Epochlevel

Mobilitylevel

Abnormalitylevel

Assessment level

A

B

C D

F

Planner

Abnorm. MobilityE

Assess. Abnorm.D

Abnorm.Assess.C

MobilityAbnorm.B

EpochMobilityA

Mobility Epoch.F

E

Hardware developed by research team members to acquire

and store multi-channel signals from hybrid sensors usingwireless technology. Hybridsensors under development in

this project will detect EMG and accelerometer (ACC) sig-nals.

b

x1

xN

b

Hidden Layer Output Layer

q11

-1

1

-1

1

-1y

q2

f(y)

...

Artificial Neural Network

f(q1)

f(q2)

Filter

Filter

Filt

er

Filte

r

Filte

r

Filte

r

Feature

Extraction

Raw

data

b

x1

xN

b

Hidden Layer Output Layer

q11

-1

1

-1

1

-1y

q2

f(y)

...

Artificial Neural Network

f(q1)

f(q2)

Filter

Filter

Filt

er

Filte

r

Filte

r

Filte

r

Feature

Extraction

Raw

data

Blackboard architecture for the PSM application. The blackboard

database shown here contains four data representation levels

(Signal Epoch, Mobility, Movement Abnormality, and Medication

State). Transformations between levels are carried out by a collec-

tion of independent programs that are typically called Knowledge

Sources and are invoked according to algorithmically defined con-

trol plans.

Features from the raw EMG and ACC signals are analyzed using

Artificial Neural Networks (ANNs). More specifically, dynamically

evolving features for each activity are characterized through Time-

Dependent Neural Networks. ANN technology is being used in con-

junction with Rule-Based Systems technology. Iterative Correlation

Analysis is used for correcting initial identifications . These diverse

techniques operate collectively through the Blackboard architecture

in the previous figure.

PI: Carlo J. De Luca, PhD1

Co-PIs: Serge H. Roy, ScD1, S. Hamid Nawab, PhD1,2, Joe Jabre, MD3

Other Key Persons: L. Gilmore, ABEE1, Samuel Chang, MS1,2, Peter Novak, MD3, Cathi Thomas, RN, MS3

1 NeuroMuscular Research Center, Boston University2 Electrical and Computer Engineering, College of Engineering, Boston University

3 Department of Neurology, Boston Medical Center

Wearable-Sensor System for Monitoring Motor FunctionNIH/NIBIB EB007163

BOSTON

UNIVERSITY

SCHOOL of

Medicine

Special thanks to the patients and staff of the Boston University Parkinsons Disease Center

A schematic of the proposed Personal Status Monitor (PSM) for identifying movement disorders, medication states, and mobility in pa-

tients with Parkinsons disease by the automatic analysis and interpretation of electromyographic (EMG) and accelerometer (ACC)

signals recorded from the surface of the body. In this example, the PSM devi ce is monitoring the subject while walking. The proposed

system will provide a continuous history and statistical summarization that can be made available to the clinician to help manage

Wearable Wireless EMG System

Wearable Wireless EMG System

Wearable Wire less EMG System

Problem: Uncontrollable movement activity is a major problem in long-term management of approximately 50% of patientswith Parkinsons disease. Physicians rely on patient diaries to monitor these complications. Diaries need to be recorded

every 15 min, are often inaccurate, have poor time resolution, and are difficult for patients to manage.

Algorithm Development

Data Collection Protocol: patients are monitored using the Personal Status Monitor (PSM) and are videotaped duringthis approximately 4-hour period that is timed to coincide with a complete medication cycle (from On to wearing Off). Ac-

tivities are conducted in a laboratory configured like an apartment. The activities include standardized motor tests used

for clinical assessment (e.g. motor scales from UPDRS), scripted functionl tasks (e.g. sit-stand-and-walk) and free-roaming activity.

The PSM will automatically identify the

onset and duration of the patients On,

Off, and On-with-Dyskinesia medica-

tion phases.

Medication States

The PSM will automatically identify these

four different movement disorders associ-

ated with Parkinsons disease and their se-

verity.

Tremor: Oscillatory,rhythmic movement

Bradykinesia: Slow movement

Akinesia: Inability to initiate a movement

Dyskinesia: Spasmodic movement

Movement Disorders

The PSM will automatically iden-

tify transitions between these

four mobility states.

Mobility StatesProposed Solution

Sitting

Standing

Walking

Lying Down

OFF OFF

ON

Levodopa

IntakeOnset

Dyskinesia

End-of-Dose

Dyskinesia

Peak-Dose

Dyskinesia

Off-Period

Bradykinesia

Off-Period

WEARING-OFF

Levodopa

IntakeOnset

Dyskinesia

End-of-Dose

Dyskinesia

Peak-Dose

Dyskinesia

Off-PeriodFreezing

TremorBradykinesiaFreezing

Tremor

-1

0

1EM G (m V )

Quad

-30

AC C - X (G)

-303

AC C - Y (G)

-303

AC C - Z (G)

-1

0

1EM G (m V )

TA

-303

AC C - X (G)

-303

AC C - Y (G)

4 6 8 10 12 14 16 18 20 22

-303

AC C - Z (G)

Tim e ( s)

-1

0

1EMG (mV)

Quad

-30

ACC-X (G)

-303

ACC-Y (G)

-303

ACC-Z (G)

-1

0

1EMG (mV)

TA

-303

ACC-X (G)

-303

ACC-Y (G)

8 10 12 14 16 18 20 22 24 26

-303

ACC-Z (G)

Time ( s)

Walking While OnDyskinesia While SittingTremor While Sitting Walking While Off

-5000

500EMG

(V)

Deltoid

-3

-2-1

ACC-X (G)

-2-1

0

ACC-Y (G)

-2-1

0ACC-Z (G )

-5000

500EMG

(V)

Biceps

-1

01

ACC-X (G)

-2

-10

ACC-Y (G)

0 2 4 6 8 10 12 14-2

-1

0ACC-Z (G )

Time (s)

-5000

500EMG

(V)

Deltoid

-3-2

-1A CC- X( G )

-1

01

A CC- Y ( G )

-1

01

A CC- Z ( G )

-5000

500EMG

(V)

Biceps

-1

0

1

A CC- X( G )

-1

0

1

A CC- Y ( G )

0 2 4 6 8 10 12 14

-2

-10

A CC- Z ( G )

Time (s)

Sample data from a patient asked to sit quietly and not move. The

figure on the left was taken during his Off period when he was experi-

encing Tremor and on the right during his On w/ Dyskinesia period.

Data were recorded from the Anterior Deltoid (Ch1-4) and Biceps

brachii (Ch 5-8). EMG signals are in black; ACC signals are in blue.

Sample data from the same patient while walking. The figure on the

left was taken during his On period when he walked normally with-

out movement disorders and on the right during his Off period

when he had Akinesia or Freezing. Data were recorded from the

Quadraceps (Ch1-4) and Tibialis Anterior muscles (Ch 5-8).

Sensor Data from PD Patient

Preliminary Results

Bradykinesia

Dyskinesia

Tremor

Sitting

Standing

Walking

44 45 46 47 48A

P

44 45 46 47 48 49 minsA

P

44 45 46 47 48A

P

44 45 46 47 48A

P

44 45 46 47 48A

P

44 45 46 47 48A

P

P = Present

A = Absent

49 mins

49 mins

49 mins

49 mins

49 mins

Results from the algorithms provide automatic classification of mobility

states and movement disorders in a patient with PD transitioning fromOff to On-with-Dyskinesia. Classification is made solely on thebasis of EMG and ACC data. Only 4 of the 8 sensors were used for

the mobility analysis. No attempts were made to reduce the number

of requisite sensors (to less than 8) for identifying movement disor-

ders at this stage of algorithm development. Classification resolu-

tion is 1s and the sensitivity is 95% based on the interpretation of

videotaped data by movement disorder specialists.

The patient is a 73 y.o. male with advanced PD characterized by

severe motor fluctuations.

Goal: To developa Personal Status Monitor (PSM) that automatically identifies and tracksmovement disorders, medication states, and mobility in patients with Parkinsons disease.

Project 1: Technical Infrastructure Project 2: Clinical Application to PD

Summary Results - Year 2

We have built a custom hybrid surface sensor acquisition system for acquiring EMG and ACC signals from patients and control subjects. Algorithm development has been enhanced for standardized and singular activities using a blackboard-based signal processing system and time-dependent neural networks.

Preliminary classification results for patients with Parkinson's disease resulted in 95% sensitivity for mobility and movement disorders during standardized and singular activities.

We are currently preparing to acquire sufficient data to enhance the algorithms for monitoring patients during unconstrained free-form activities.

Blackboard Database

Knowledge Sources

DATA

Acquisition