iBALANCE

Hardware and Software Design for a Mobile Diagnostic Device

that Assesses Human Balance

Qian Yang, Bradford Diephuis, Virginia Chu and Katharine E. Forth

iShoe Research Team, Cambridge, U.S.A.

Keywords:

Balance, Stabilometry, Diagnostic, Mobile.

Abstract:

Balance deterioration is a major risk factor for falling, particularly among the elderly. Early detection of

emerging balance problems can allow behavioral and medical interventions to reduce the impact and sever-

ity of balance-related incidents. The iBalance technology presents a small, mobile platform that integrates

hardware and software engineering for balance monitoring at a low cost for use in the home, physical therapy

ofﬁce, or other point of care setting. The hardware solution has the form factor of a bathroom scale and takes

the standard approach of a force plate with four load cells arranged in the corners beneath the platform. The

load cells output 12-bit data to a computing device running the accompanying software. There is less scien-

tiﬁc consensus about the most effective software solution for performing analysis on balance data. A survey

of the literature reveals 16 commonly used metrics of balance derived from force plate data. Using principal

component analysis, we identify three underlying clusters of metrics from which a representative metric for

each cluster may be chosen to construct an exogenous balance score. Finally, we have developed a graphical

user interface for the iBalance that allows researchers to collect raw and/or processed data and view analytic

visualizations of the data, with ease of extensibility for further research and analysis.

1 INTRODUCTION

Deterioration of balance is a common and pressing

problem for senior citizens. Injury from falls is one of

the leading causes of accidental death in adults over

85, and among adults 65 and over a hip fracture is

statistically fatal 25% of the time within 6 months of

injury. According to the Centers for Disease Control

and Prevention, the total direct cost of all fall injuries

for people 65 and older in the year 2000 exceeded $19

billion (CDCP, 2009). The high health and ﬁnancial

costs associated with poor balance point to a large un-

fulﬁlled need for diagnostic technology that can help

prevent fall risk and detect deterioration of balance at

an early stage.

Currently, the main method of addressing this

problem has been clinical assessment followed by

physical therapy. Clinical assessments have been

largely limited to qualitative observation over short

time spans by a physician. Some commonly used

techniques include evaluative questionnaires such as

the Berg Balance Scale (Berg et al., 1992), and the

observation of quiescent standing on a foam board

where somatosensory inputs are impaired (Emery

et al., 2005).

Early diagnosis of balance deterioration enables

a host of treatment options including medication, tai

chi, physical therapy, safety equipment such as walk-

ers, and simple adjustments to the home such as rugs

and hand rails. However, a missing link in this pro-

cess is the long-term monitoring and early diagno-

sis of balance deterioration. Because the effects of

balance deterioration are subtle, it is difﬁcult to as-

sess one’s own balance in a timely manner to take

preventative measures before a fall occurs. The in-

convenience of scheduling regular balance monitor-

ing checkups with a physical therapist in the absence

of clear physical symptoms, and the high cost of exist-

ing devices such as NeuroCom units (Chaudhry et al.,

2004) used in state-of-the-art facilities, make long-

term balance monitoring prohibitively inefﬁcient for

most individuals.

107

Yang Q., Diephuis B., Chu V. and E. Forth K..

iBALANCE - Hardware and Software Design for a Mobile Diagnostic Device that Assesses Human Balance.

DOI: 10.5220/0003174001070114

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 107-114

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: iBalance hardware consisting of a force platform

with an USB extension chord for connection with an exter-

nal computing device. The device has dimensions of 17”

x 14” x .25” and weighs less than 10 lbs; substitution of

lighter materials for the metal casing can signiﬁcantly re-

duce the weight and increase the portability of the device.

2 HARDWARE DESIGN

The iBalance is a cost-effective, portable, user-

friendly diagnostic device that can be used from the

comfort of the home or at any point of care setting.

The device consists of a durable balance platform

equipped with pressure sensors and a USB serial port

that transfers data collected from the platform onto

any USB-enabled computing device. Alternatively,

wireless bluetooth may also be used. The data from

the balance platform is collected and displayed in real

time by the computing device, which then computes

a balance diagnostic score based on a ﬁxed interval of

data, usually between 20 and 120 seconds. The ﬁnal

prototype of the device will be able to compute a sim-

ple diagnostic internally so that an external comput-

ing device will not be necessary; this is the adaptation

most suitable for home use, while in clinical applica-

tions the use of a computing device with the ability

to conduct more detailed analysis on the data may be

preferable.

2.1 Mechanical Design

The iBalance hardware consists mainly of a set of

load cells and a stable platform for the subject to

stand on. Since the basis of the iBalance software

relies on changes in the center of pressure (COP) in

the anterior-posterior and medio-lateral directions, at

least three measures of pressure are required to calcu-

late the COP in both directions. To provide the sys-

tem with redundancy and higher resolution, we use

Figure 2: Bottom view of iBalance force platform.

Figure 3: Exploding view of iBalance force platform.

four load cells placed under the corners of the plat-

form. The load cells used in our prototype are similar

to models used in bathroom scale devices.

Three designs were considered for the standing

platform: a single platform (4 load cells per platform),

a separate platform for each foot (2 load cells per plat-

form), and a separate platform for each load cell. The

different designs reﬂect a trade-off between platform

Figure 4: Design of load cells used in force platform.

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stability and measurement independence. The single

platform provides the most stability, but all four pres-

sure measures become correlated by virtue of the rigid

platform, and torsion torque would not be measured.

The two-platform design, consisting of one platform

for each foot, would allow for detection of slight dor-

siﬂexion or plantarﬂexion of the ankle. However, it

would provide less stability as a platform. The single

load cell platforms provide the least platform stabil-

ity. We chose to use the single-platform design for its

stability and sufﬁcient sensitivity to the stabilometric

properties measured by the software algorithm.

The load cell enclosures were designed to be ﬂex-

ible, since too much rigidity would cause a portion of

the pressure to transfer directly from the platform to

the ﬂoor rather than through the load cell. The ﬂex-

ible enclosure was constructed from laser-cut delrin

and acrylic sheets. Aluminum bars were used to pro-

vide structure to the load cell enclosures. The load

cells are rated at 75 kg each with a maximum of 150

2.2 Electronic Design

Load sensing is achieved with four off-the-shelf,

three-wire half-bridge load cells, which is the most

common conﬁguration found in bathroom scales. The

load cells are wired in a standard Wheatstone bridge

conﬁguration. Each load cell through its Wheatstone

bridge sends voltage values to the analog-to-digital

converter (AD7794 from Analog Devices) on differ-

ent ADC input channels. This chip performs both sig-

nal ampliﬁcation and conversion. The data acquired

by the AD7794 is transmitted to an ATMega324 mi-

crocontroller over a serial peripheral interface (SPI).

The microcontroller then sends this over USB to a

computing device.

The AD7794 is a low-power analog front end for

high precision measurement applications. The out-

puts from the four Wheatstone bridges are wired to

differential input pins on the AD7794 development

board. The AD7794 ampliﬁes the difference between

these pins, and then performs an A/D conversion.

The results of the A/D conversion are made available

to the ATMega324 microcontroller via the AD7794s

communication protocol.

An ATMega324 development board is used to in-

terface to the AD7794 development board. We used

an off-the-shelf AVR development board with an AT-

Mega324 microcontroller. This development board

includes an on-board USB chip which allows the AT-

Mega324 to easily stream data over USB. The AT-

Mega324 development board uses a USB chip from

FTDI to establish communications with a computing

device. Drivers from FTDI were installed on the com-

puting device, which makes the USB connection look

like a COM port.

The ﬁrmware for ATMega324 performs the fol-

lowing general functions: using the SPI communi-

cation protocol, the ATMega324 communicates with

the AD7794 to initiate each A/D conversion and read

back the results. It then formats the resulting data into

one channel, and streams the data out over the UART,

which goes through a USB chip and out as a USB

signal. The format of the data streamed is a repeating

cycle through the 16-bit data from each load cell, se-

rialized in clockwise order starting from the front left,

followed by a series of padded zeros.

The AD7794 is programmed to continuously con-

vert data at its maximum speed of 470 Hz. Due to

multiple overheads in transmitting the data to the host

machine, the transmission from one channel clocks in

at 300 Hz, resulting in a frequency of 75 Hz for all 4

channels.

3 SOFTWARE DESIGN

The challenge in designing the software algorithm for

the iBalance is to determine a suitable metric for reli-

ably measuring change in an individual’s balance pro-

ﬁle over time. In order to be of practical use in the

home or at the point of care, the metric used by the

iBalance must also be able to be accurately derived

from a relatively small amount of data.

Due to the complexity of the musculoskeletal and

sensory mechanisms underlying balance, it is difﬁcult

to satisfactorily model the stability of an individual

in terms of a deterministic physical model. A vari-

ety of standard techniques used to study balance in-

stead consider the set of observations given by the

time series of an individual’s center of pressure dur-

ing quiescent stance. The resulting time series gives

rise to a large variety of metrics that may be used

to quantify a balance state (Prieto et al., 1996) (Pe-

terka, 2000). A few of the most widely studied in-

clude the peak-to-peak sway in the anterio-posterior

and medio-lateral directions, the average velocity of

the COP, and the power spectral density of the COP.

Studies have shown that there are many redundan-

cies in the full set of such metrics, from which a few

principle parameters may be extracted (Rocchi et al.,

2004). In this section we demonstrate that there are

three principle groups of metrics, from which repre-

sentative metrics may be extracted to form the basis

of a singular parameter which can be used to track the

balance proﬁle of an individual over time.

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109

Figure 5: Sample stabilogram plot annotated with the

medio-lateral peaksway (XSWAY) and anterior-posterior

peaksway (YSWAY) metrics. The COP data time series is

plotted relative to the coordinate axis of the force platform,

with (0.5, 0.5) representing the center. The series is colored

in progression from green to red over time.

3.1 Stabilometric Properties of COP

Time Series

A large set of metrics have been reported in the liter-

ature that derive summary statistics from COP data

(Prieto et al., 1996). Researchers have conducted

Principle Component Analysis of different subsets of

these metrics in an attempt to distill a standard combi-

nation with which stabilometric data may be analyzed

(Prieto et al., 1996) (Rocchi et al., 2004).

3.2 Principal Component Analysis of

Astronaut Data

In constructing the iBalance algorithm, we are in-

terested in how to derive a signal from the various

stabilometric properties of the COP that maximally

captures variations in the observed data. We con-

ducted a principal component analysis on data from a

group of individuals in both their normal and balance-

compromised state. The particular dataset we chose

to study was astronaut data generated by NASA. The

effects of long-term exposure to zero-gravity condi-

tions on the vestibular and musculoskeletal systems

of astronauts is a heavily researched problem, and it

is widely known that astronauts experience compro-

mised balance during the ﬁrst week upon their return

from space, with variations in the recovery period dif-

fering among individuals.

For our analysis, we were able to use data col-

lected from 18 NASA astronauts before launch and

after return from space using NeuroCom Interna-

tional’s dynamic posturography system. Center of

pressure data was collected from each astronaut on

Figure 6: Normalized Cumulative Eigenvectors from Prin-

ciple Component Analysis. The ﬁrst three principle compo-

nents account for approximately 58%, 78%, and 91% of the

observed variation in the data.

Figure 7: Correlation coefﬁcients of each metric with ﬁrst

two principle components. The red, green, and blue data-

points correspond to Clusters 1, 2, and 3 listed in Table 1

respectively.

8 different occasions: 60, 30, and 10 days before

launch, twice on return from launch, and 2, 4, and

8 days after launch. On each day of testing, three tri-

als were performed under each of two standard Neu-

roCom protocols: SOT1 (quiescent standing, eyes

open), and SOT2 (quiescent standing, eyes closed).

The time series data collected from these trials were

20 seconds each in length, collected with a sampling

rate of 100 Hz. The COP is represented as the relative

normalized pressure in the medio-lateral and anterior-

posterior directions, with (0.5,0.5) representing per-

fectly balanced pressure in each direction.

We follow the methodology of Rocchi et al in us-

ing the correlation matrix rather than the covariance

matrix in the PCA due to the differences in parame-

ter units and variance (Jolliffe, 1986). This prevents

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Table 1: PCA shows that stabilometric properties derived from COP time series can be clustered into several distinct groups

that correlate similarly with the observed principle components.

Cluster 1 MD average Euclidean distance of the COP from mean normalized coordinates

RMSD root mean square value of the distance of COP from mean normalized coordinates

YSWAY maximum differential of coordinates along the anterior-posterior axis

XSWAY maximum differential of coordinates along the medio-lateral axis

MAXD maximum Euclidean distance of COP from mean normalized coordinates

MV mean velocity of COP with instantaneous velocity measured at 5 Hz

RMSV root mean square value of the velocity time series

AREA-CC area of circle centered at mean COP containing 95% of the observed COP time

series, assuming Gaussian distr

AREA-CE area of ellipse centered at mean COP containing 95% of the observed COP time

series, assuming Gaussian distr

Cluster 2 MFREQ approximate rotational frequency in Hz of COP trajectory along circular path

centered at the mean with radius equal to average distance from mean

FDCC fractal dimension of COP within 95% conﬁdence circle

FDCE fractal dimension of COP within 95% conﬁdence ellipse

Cluster 3 P50 median frequency, in Hz given by discrete

fourier transform of COP time series

P95 frequency below which 95% of total power is found, in Hz given by discrete fourier

transform of COP time series

FREQD measure of variation in frequency content given by discrete fourier transform

of COP time series

CFREQ measure of frequency at which power spectral density is most concentrated

the resulting principal components from being domi-

nated by inherent differences in the variance of each

parameter caused by differences in units. The results

show that two principal components account for al-

most 80% of the variation in the data and three prin-

cipal components account for more than 90% of the

variation in the data. When we plot the correlation

coefﬁcients of each metric against the ﬁrst two prin-

cipal components, we see that the metrics naturally

form three distinct clusters.

The ﬁrst cluster corresponds to the metrics that are

based on the length extent of the phase plot. This

refers to measures of the overall size of the space tra-

versed by the COP time series over a ﬁxed interval of

time. From a physical standpoint, this is analagous to

measures of the maximum tilt from upright position

an individual experiences over the course of the time

series.

The second cluster corresponds to the metrics that

are based on the area of the phase plot. This refers to

measures of the total distance traversed by the COP

time series over a ﬁxed interval of time. Whereas the

length extent of the phase plot only looks at maxi-

mum differentials between COP coordinates, the area

measures look at how the space in between was ﬁlled.

From a physical standpoint, this is analagous to un-

derstanding whether the subject was moving quickly

or slowly within the ﬁxed interval of the observed

time series. In combination with the ﬁrst cluster of

metrics, we can gain an understanding of whether

the subject was moving quickly over a small area, or

moving slowly over a large area, or some other com-

bination thereof.

The ﬁnal cluster corresponds to metrics based on

the power spectrum of the phase plot. This describes

the frequency of oscillations observed in the COP

time series. From a physical standpoint, analysis of

the frequency domain may reveal underlying patterns

in the feedback-control mechanism of the body as it

attempts to maintain balance during quiescent stand-

ing, as well as any noise ﬂuctuations caused by envi-

ronmental factors that have an effect on balance.

See Table 1 for a list of metrics that belong to each

cluster. These results show that we can use a represen-

tative metric from each of these clusters to determine

a three-dimensional descriptive balance vector. The

distance of this vector from the space of normal bal-

ance may be used as a singular metric describing the

balance proﬁle of an individual.

3.3 Punctuated Equilibrium Model of

Human Balance

In addition to the metrics above, iShoe Research has

developed a new quantitative and descriptive model

for analyzing human balance which provides addi-

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111

Figure 8: Stabilogram annotated with punctuated equilib-

ria. The colored regions represent different clusters of static

equilibrium, with overlayed pentagons indicating the rela-

tive size of the equilibria. The datapoints outlined in blue

and black represent dynamic trajectories between different

equilibria or returning to the same equilibria, respectively.

tional measures of an individual’s stability. The Punc-

tuated Equilibrium model captures the hypothesis that

human balance can be characterized by two states:

one of static equilibria, during which the center of

mass remains stable within a bounded region, and one

of dynamic trajectories, during which equilibrium is

lost and the center of mass attempts to readjust to a

new equilibrium. It is possible using Hidden Markov

Model analysis to capture from the observed stabilo-

gram data this underlying series of static equilibria

punctuated by dynamic trajectories.

The Punctuated Equilibrium Model provides sev-

eral quantitative measures for balance, including the

number of equilibria, the length of time spent in each

equilibrium, and the size of the bounded region for

each equilibrium. For example, analysis of the NASA

data described above shows a negative correlation be-

tween the number of equilibria and the quality of an

astronaut’s balance as represented by the number of

days since return from space. In addition to these

quantitative measures, the algorithm for Puncutated

Equilibrium is also able to provide a qualitative vi-

sual model of an individual’s balance proﬁle. While

stabilograms are typically difﬁcult to analyze due to

the density of datapoints collected in a bounded re-

gion over time, applying the algorithm for Punctuated

Equilibria transforms the data into regions of stability

and instability. The location and pattern of these equi-

libria and dynamic trajectories can help determine in-

formation such as whether an individual is weaker on

one leg than the other or has a tendency to lean or fall

in a particular direction.

4 GRAPHICAL USER

INTERFACE FOR BALANCE

RESEARCH

In order to facilitate the use of the iBalance as a re-

search device, we developed a basic graphical user

interface. The GUI is designed as a platform for data

collection, as well as for providing analytic visualiza-

tions of the balance data.

4.1 Real Time Data Visualization

The GUI provides two methods for visualizing data

in real-time as it is being collected from the iBalance

platform. In the Stabilogram method, we display the

real-time position of the subjects center of mass on

a two-dimensional coordinate system. The display

shows a trail of the previous second of movement,

so any shift in position from the subject is immedi-

ately visually noticeable. The Oscilloscope method

presents the same data in two simultaneous plots, dis-

playing the time series of the center of mass in the

mediolateral (ML) direction and in the anteroposte-

rior (AP) direction. This second display highlights

one-dimensional movements of the center of mass,

and permits observation of the entire data collection

sequence at any time. These real-time displays are

updated at the same refresh rate as the hardware out-

put.

Figure 9: Oscilloscope view of real-time data collection

from iBalance.

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Figure 10: Stabilogram view of real-time data collection

from iBalance.

4.2 Static Data Visualization

After data collection is complete, we provide ad-

ditional methods for visualizing the resulting data.

These methods are intended to provide summary

statistics of the data, as well as to display the re-

sults of analysis using a variety of common as well

as novel models for human balance. The two main

models currently implemented for the GUI include

the Stabilogram-Diffusion plot described by Collins

and De Luca (Collins and De Luca, 1993), and the

Punctuated Equilibrium analysis developed by iShoe

Research. The GUI is designed to be easily extensible

to use for visualization of COP data in the context of

new models.

The existing options provide six display modes for

the collected data. The Time Series option is similar

to the real-time Stabilogram display, showing the sub-

jects center of mass on a two-dimensional coordinate

system. However, in the static display, the entire time

series of data is displayed, and color transitions are

used to denote the passage of time. The Velocity Time

Series displays the magnitude and direction of the in-

stantaneous velocity vector over the entire time series,

using the same color transitions to show evolution of

time. The Classiﬁer Plot depicts the steps in the anal-

ysis of punctuated equilibria, which derives regions

of stability and dynamic trajectories from the instan-

taneous velocity time series of the COP data using a

Hidden Markov Model. The Punctuated Equilibrium

plot visually displays the regions of equilibria com-

puted by the HMM and the interspersed dynamic tra-

jectories. The Fast Fourier Transform plot depicts the

results of a Discrete Fourier Transform on the time se-

ries data. Finally, the Stabilogram Diffusion plot de-

picts the Stabilogram-Diffusion plot showing closed-

loop and open-loop control described by Collins and

De Luca (Collins and De Luca, 1993).

Figure 11: Stabilogram-Diffusion analysis (Collins and De

Luca, 1993) of data collected from iBalance.

4.3 Data Collection

While we provide various forms of data visualization

and provide access to our own Punctuated Equilib-

rium analysis, the primary purpose of the GUI is to

be used as a platform for data collection. The GUI

is designed to be easily extensible so that new ap-

proaches to analyzing the data can be incorporated

into the toolkit by users. To this end, we provide func-

tionality to output the raw data collected from the four

load cells into comma-separated values (CSV) ﬁles.

These are saved as raw 12-bit values. For the con-

venience of the researcher, we also write to the CSV

ﬁle the derived center of mass coordinates in the AP

and ML directions. Finally, the data ﬁles are automat-

ically annotated upon creation to allow easy indexing

of collected data.

5 CONCLUSIONS

With the high risk of falling in the senior citizen popu-

lation and the signiﬁcant health and ﬁnancial costs of

those falls, the beneﬁts of preventative medicine for

balance deterioration are clear. An effective solution

iBALANCE - Hardware and Software Design for a Mobile Diagnostic Device that Assesses Human Balance

113

for long-term monitoring and early diagnosis of bal-

ance deterioration has the potential to be transforma-

tive for healthcare for senior citizens. The iBalance

technology is a cost-effective platform for which both

simple self-diagnostic algorithms as well as advanced

clinical tools have been developed, with the hope that

balance diagnostics will become as widely adopted as

blood pressure monitoring to help prevent thousands

of injuries each year. In fact, widespread use of the

iBalance device and integrated GUI also has the po-

tential to generate a wealth of data from which re-

searchers may seek to gain a greater understanding of

the variation in balance proﬁles between individuals

and of the long-term progression of balance proﬁles

in individuals.

Ongoing research for the iBalance aims to rig-

orously evaluate the accuracy and precision of the

data collected by the hardware device in various en-

vironments, as well as validate the iBalance metrics

against standard physical therapy balance measures

through a prospective study with a blinded clinical

trial. Areas for future work include reﬁning the ana-

lytical algorithm to achieve the most valid and reliable

results with the shortest data sample to increase its

ease-of-use, as well as adapting analytical algorithms

for use with pressure-sensing insoles worn throughout

the day for continuous balance monitoring and mea-

surement of dynamic gait.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. William H.

Paloski of the Neurosciences Laboratory at John-

son Space Center for generously providing data from

NASA for the study of balance metrics.

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