WH2014 Session: Postural stability regularity measures with wireless sensors can

WLSACONVERGENCE SUMMIT

POSTURAL STABILITY REGULARITY MEASURES WITH WIRELESS SENSORS CAN IDENTIFY FALL RISK IN OBESE ELDERLY

THURMON E. LOCKHART ARIZONA STATE UNIVERSITY

Postural stability regularity measures with wireless sensors can identify fall risk in

obese elderly

Wireless Health 2014, Bethesda, MD0-30-2014,

Thurmon E. Lockhart (Arizona State University)Chris Frames and Rahul Soangra (Virginia Tech)

John Lach (University of Virginia)

Wireless Health 2014, NIH-Bethesda, MD, 10-30-2014

Research to Outreach

Introduction» Problems Associated with Fall in the Obese Elderly

Research» Mechanisms Related to Fall Accidents in the

Elderly» Wearable Fall Risk Assessment Tool development

using nonlinear dynamics

Outreach» Community-Dwelling Elderly

DiscussionsFuture Research


Falls in the Elderly

•Every 17 seconds, an Elderly is admitted to an emergency department due to falling

•Every 30 minutes, an Elderly dies due to falls


Quality of Life

20% - 36% fear falling1

20% die within a year after hip fracture2

25% in a nursing home one year later3

1. Vellas BJ, Age & Aging, 1997; Friedman SM, JAGS, 2002 2. Lu-Yao GL, AJPH, 19943. Magaziner, J Gerontology: Medical Sciences, 2000


Obesity & Falls in the Elderly

In the US, 35.7% of the adults, or over 72 million people are obese

Obesity is cause of many physical health conditions

Associated with structural and functional limitations

Falls being the most common cause of injuries in obese (36%)

Middle-aged obese fell 27% more than lean counterparts

Older obese fell 15% more than lean counterparts (Fjeldstad et al., 2008)

Although implicated, the mechanisms leading to increased fall risk in this population is unclear


Why do obese older adults fall more than their lean counterparts?

What is the relationship between these risk factors and fall accidents in the obese elderly? And, how can we use this info to assess fall risk……


Slip and Fall Experiments


Trip and Fall Experiments


2014

Bunterngchit Y. et. al., IJIE, 1999Gronqvist R. et.al.,Ergonomics, 2001

Lockhart et.al., ASTM STP,2002Lockhart et.al., Safety Science, 2002

Lockhart et.al., Ergonomics, 2003James, C.R. et.al., RQES, 2004

Edmison, J. et.al., SHTI, 2004

Lockhart, T.E. et.al., Human Factors, 2005Yoon, H.Y. and Lockhart, T.E., IJIE, 2005Kim, S. W. and Lockhart, T.E., Safety Science, 2005

Liu and Lockhart, Gait & Posture, 2006Lockhart & Kim, Gait & Posture, 2006Kim, S., et.al., Gait & Posture, 2006Lockhart T.E., et. al., Safety Science, 2006

Lockhart, T.E. et. al., Gait & Posture, 2007

Parijat, P., and Lockhart, T.E., Ergonomics, 2008Lockhart, T.E. and Liu, J., Ergonomics, 2008Parijat, P., and Lockhart, T.E., Gait & Posture, 2008Liu, J., Lockhart, T.E., et. al., IEEE, 2008Granata K.P., and Lockhart T.E., J. Elect. & Kine., 2008Lockhart, T.E., J. Elect. & Kine., 2008Kim, S., and Lockhart, T.E., Indus. Heal., 2008Shi, W., Lockhart, T.E. et. al., Safety Science, 2008

Liu, J. & Lockhart, T.E., Gait & Posture, 2009Lockhart, T. E., et.al., Assis. Tech., 2009Kim S., Lockhart, T.E. et.al., Qual. in Age., 2009Lee, M., et. al., Hum. Mov. Sci., 2009

Lockhart, T. E. and Shi, W., Ergonomics, 2010Kim, S., and Lockhart, T.E., Int. J. Rehab. Res., 2010Kim, S., Lockhart, T.E. et. al., IJIE, 2010

Lockhart, T.E. et. al., Safety Science, 2011Liu, J., & Lockhart, T.E., CMBBE, 2011

Park, S.H., et. al., J. Neuro. Rehab., 2011

Parijat, P. & Lockhart, T.E., ABME, 2012Wu. X., Lockhart, T.E., et.al., J. Biom., 2012Kim, S., and Lockhart, T.E., J. Neuro. Rehab., 2012Haynes, C., & Lockhart, T.E., J. Biom., 2012Suwittayaruk, P. et. al., Hum. Fac. Erg. Manu. Ser. Indus., 2012Liu, J., et. al., Saf. Heal. Work, 2012

Soangra, R., Lockhart, T.E., et.al., ABME, 2013Yeoh, H. Lockhart, T.E. et.al., Ergonomics, 2012

Yeoh, H., Lockhart, T.E., et.al., Work. Heal. & Saf., 2013Liu, J., and Lockhart, T.E., Saf. And heal. Work, 2013

Obesity

Inertial Sensors

Hemodialysis

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

1999

Falls

Slip s

RCO

F

E-Te

xtile

Agin

g Falls

Occupational

3D-Joint Moments

HCVGlare Responses

Slips

Fatigue Dynamic

Stability

E-TextileDynamic Stability

Slip Mechanism

Load Carriage Glare Responses

Reactive Recovery

Cognition

Balance Training

Linear Discriminant Analysis

Dynamic Accomodation

Weight Training

Leg Strength

Training

Limb ControlIntellectual Disability

Falls

Falls

Prior Work on Falls

Dynamic falling behavior was closely related to postural stability during stance


Mechanisms

Similarly, the major reasons for falls in this population may be attributed to postural instability.

Obesity results in an increase of segmental body mass

sensory impairments,

diminished joint range of motion, and

alter neural processing and influenced ability to control the orientation of the body in space.


Mechanisms

Although implicated, considerable controversy exists.

One of the major limitations of these studies is that the conclusions are derived on the basis of sway area, path length and COP velocities.

Non-linear aspect of balance derived from temporal structure of COP signals have not been considered in any of these studies to detect subtle impairments in balance mechanisms.


Specific Aims

Study Objective: To characterize fall risk of obese older adultsusing the Portable Wireless System by monitoringfunctional and mobility characteristics

Central Tenet: Fall risk will be significantly higher for obese elderly than their lean counterparts

Relevance: Accurate fall risk assessment will allow us to pinpoint the most effective intervention strategies to reduce falls in this population

Portability and usability of fall risk assessment Technology


Real- ti m

eN

on-r eal- ti me

Inte grat iv

e am

bulat o

ry meas u

rem

e nt fra

mew

ork

TEMPO

NetworkingBluetoothZigbeeWiFiCellular

Fall Event Detection

2-dimension motion feature(angular rate & body orientation)Fast threshold techniquePrior to the impact detection

Portable Sensors

AccelerometerGyroscopeTemperature sensorPulsoximeterMagnetometer

Fall Risk Prediction

Local dynamic stability(max Lyapunov exponent)

Floquet dynamic stabilityLeg

Vel

ocity

Swing

Stance

Leg Angle

Leg

Vel

ocity

Swing

Stance

Leg Angle

Gait AnalysisSpatial (Step length)Temporal (stance time, swing time)Walking velocityGait symmetry

ADL Classification

Sit-to-stand / stand-to-sitLying downStoopingWalking / Stairs climbing

Gait stability Symmetry Index (GSI)


Methods

Approved by Virginia Tech – Institutional Review Board

Inclusion Criteria

– Age 65 years

– Ability to understand and provide informed consent

– Ability to walk 10 m

300+ elderly individuals participated in this study so far from the elderly care centers in northern Virginia

Here we present preliminary results of our study


Participants:

Instrumentation: TEMPO (Technology-Enabled Medical Precision Observation) 3.1 which is manufactured in collaborative research with inertia team in UVA [11]. •MMA7261QT tri-axial accelerometers and •IDG-300 (x and y plane gyroscope) and ADXRS300 as z-plane uniaxial gyroscope. •One instrumented force platform (AMTI BP400600 SN 6780/6782) was used to measure ground reaction forces (GRF) under subject’s feet and the chair during STS. •Signals were denoised using Ensembel Empirical Mode Decomposition Golay (EEMD-Golay) (Soangra & Lockhart, 2013)

Methods Fall Status

Health Status Faller (F)

Non-Faller (NF)

Non-Obese (19≤BMI<30 kg/m2)

14 20

Obese (30≤BMI<40 kg/m2)

20 46


Methods

Participants performed

» Sit-to-Stand (STS) using standard chair (43 cm popliteal height) and arm rest

(65 cm) (Soangra & Lockhart, 2012)

» Timed 10-m Walk (Scivoletto et. al. , 2011)

– Gait characteristics (Lockhart et al., 2012)

» ABC score rating (Powell & Myers, 1995)

» Timed Up & Go (TUG) (Podsiadlo & Richardson, 1991)

– Sit-to-walk (STW) (Soangra & Lockhart, 2013)

» Postural Stability – 60 seconds FP and IMU – EO, EC

Movement variability can be used to assess the limitations of movement control system.

For example: •low stride-to-stride variations reflect a rhythmic and stable gait, •whereas high gait variability reflects an unstable walking pattern (Beauchet et.a.l, 2009; Hausdorff et. al., 1997; Hausdorff et. al., 1999)


Approximate Entropy: Complexity

ApEn quantifies regularity and complexity of a system (Pincus, 1994)

- High ApEn values indicate unpredictability and random variation

- Low ApEn indicates high predictability and regularity of time series data

Approximate Entropy: It is the logarithmic likelihood that the patterns of the data are close to each other and will not remain close for the next comparison within a longer pattern.

If SN is a time series of length N

Where m is the pattern length ( usually chosen as 2) and d is similarity coefficient (chosen as 0.2 % of SD of time series )

Heart rate signals of old and young participant. Adapted from

Lipsitz and Goldberger, 1992

17


Data Analysis

Two-way between subject ANOVA was performed to determine

» Main effects of fallers/non-fallers» Main effects of obesity» Interaction effects Obesity X Fall

Alpha level = 0.05


Results & DiscussionsNon Obese Obese

Parameters Non-Fallers Fallers Non-Fallers Fallers

ABC Score 87.68±16.72 64.93±10.89 73.25 ± 19.25 67.4 ± 13.60

Walking Velocity (m/s) 1.02 ± 0.17 0.92 ± 0.10 1.12 ± 0.25 0.81 ± 0.17

Sit to Stand Time (s)* 2.80 ± 1.29 3.70 ± 1.71 4.96 ± 2.05 5.84 ± 1.85

TUG Time (s)* 7.68 ± 1.16 9.32 ± 1.57 7.78 ± 0.77 11.30 ± 1.27

Gait Cycle Time (s) 0.92 ± 0.27 1.16 ± 0.14 0.91 ± 0.23 0.95 ± 0.24

Double Support Time (s)

0.23 ± 0.11 0.28 ± 0.12 0.18 ± 0.12 0.28 ± 0.18

Stance Time (s) 0.54 ± 0.18 0.70 ± 0.13 0.50 ± 0.11 0.54 ± 0.21

Swing Time (s) 0.41±0.07 0.43 ± 0.09 0.43 ± 0.06 0.41 ± 0.10

Swing Angle (Degrees) 32.64 ± 5.33 33.94 ± 5.02 31.29 ± 6.63 28.85 ± 6.60

Step Length (m) 0.41 ± 0.07 0.44 ± 0.07 0.39 ± 0.09 0.38 ± 0.08

Cadence (step/min) 118.26 ±17.46 111.62 ± 12.26

129.14 ± 22.55 119.51 ± 19.64


Results & Discussions

Approximate entropy in AP direction was significantly lower in obese when compared to non-obese and overweight elderly (p=0.04).


Discussions

The aim of this study was to determine if obesity affected postural stability in community dwelling elderly

Our results suggest that body weight/BMI is an additional risk factor for falling in community dwelling elderly

This decrease in complexity may be due to •impaired feedback control or•impaired proprioception •leading to reduced adaptive capacity of postural system

Postural imbalance in obese elderly community dwelling elderly may be an important marker for fall risk


Comprehensive Fall Risk Prediction Tool

Gait Velocity

Stability [λ]

DFA

Medication

Fall Risk Score

Functional Score

Day 1Medication, Fall Risk Score, Stability

Stability [λ]

Fall Risk Score

Medication

Gait Velocity

Day 60

DFA

Functional Score

Stability [λ]

Fall Risk Score

Medication

Gait Velocity

DFA

Functional Score

Medication, Stability, Gait Velocity

Fall Risk Score, DFA

Healthy

Low Risk

High Risk

Discussion and Future Work


This research was supported by the NSF (grant #CBET-0756058) and NSF-Information and Intelligent Systems (IIS) and Smart Health and Wellbeing -1065442 and 1065262. NIOSH (grant #CDC/NIOSHR01-OH009222), and NIH (AG022963-04)

Thank You!

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WH2014 Session: Postural stability regularity measures with wireless sensors can

Healthcare

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