91643 v00 Digest Mar09 EquuSys FINAL

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1 MATLAB Digest www.mathworks.com

At EquuSys, we used MALAB to develop

an easy-to-use, noninvasive system that en-ables equine proessionals to identiy and

diagnose lameness that would otherwise be

undetectable, even or the well-trained eye.

EquuSense technology sits at the conjunc-

tion o several dierent disciplines, includ-

ing telemetry, biomechanics, and veterinary

medicine. Despite the complex, multidis-

ciplinary challenge, a small team o devel-

opers with little MALAB experience was

able to move rapidly rom the initial idea to

a production system. Te EquuSense system

(Figure 1) provides real-time quantitative

data on horses trotting in-hand, being rid-

den on the at, or jumping. Its 18 wireless

sensor nodes measure position, velocity, ac-

celeration, orientation and rotation, relative

to the horse or a global rame o reerence,

to an accuracy o a ew millimeters or a

ew degrees. EquuSense soware processes

this telemetry data to provide objectivedata on equine biomechanics in the orm o

charts, 3-D plots, and animation.

Developing Algorithms

o provide meaningul inormation on

equine biomechanics, we must translate and

aggregate raw data obtained rom localized

sensors on the horses body to produce a six-

degrees-o-reedom representation o the

horses motion. Te EquuSense nodes use

accelerometers and gyroscopes to measure

acceleration and angular rotation. Magne-

tometers measure orientation with respect

to the earth s magnetic eld. Our rst objec-

tive was to convert the acceleration and an-

gular rotation data rom a local to a global

rame o reerence.

By Michael Davies, EquuSys

Horses are very valuable, but also very fragile; at any given time, one in six

top equine athletes are lame in some way. Because horses are fight animals,

a lame horse will go to extraordinary lengths to hide its injury. Wild horsesevolved this trait to help prevent an injured animal rom being singled out rom

the herd as easy prey. This behavior can have disastrous consequences or do-

mesticated horses and their riders: Because horses are so adept at masking inju-

ries, it is challenging even or experts with decades o experience to recognize

subtle lameness, while diagnosing the precise cause o the injury is harder still.

Using MATLAB to Process Large Telemetry DataSets for Biomechanical Performance Analysis

MATLAB Digest

With multiple sensor nodes measur-

ing at up to 1000 times per second, it was

immediately clear that each session would

generate gigabytes o data. With MALAB,

we transormed the millions o rows o

data rom comma-separated value les

into matrices that we could then lter,process, and convert into meaningul

inormation.

Products Used

MATLAB

Aerospace Toolbox

Instrument Control Toolbox

Figure 1. A horse with EquuSense sensors fttedto the ront hooves.


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We used Aerospace oolbox to trans-

orm rames o reerence by converting

direction cosine matrices to Euler angles.

Because our sensors provided accelerationin meters-per-second squared, we needed to

integrate acceleration over time to nd sen-

sor velocity. We then used that measurement

to determine the position o each sensor.

During the early, exploratory phases o

development, we moved between scripts

and interactive mode in MALAB to exam-

ine variables and try out new ideas quickly.

We could visualize the data using MALAB

plotting capabilities without writing our

own custom routines. We could process

large data sets very efciently by using arrays,

matrices, and vectorized unctions. I we

had used C or C++ instead o MALAB,

these tasks would have taken much longer.

One o our inertial navigation experts

had written an extensive signal processing

library in C++ that we initially called rom

MALAB. Eventually, we recoded the en-

tire library in MALAB because we ound

that MALAB scripts or signal processing,

manipulating matrices, and perorming

rame-o-reerence calculations are easier to

understand and more transparent than an

equivalent set o C++ routines.

Testing the First-GenerationPrototype

As a proo o concept, we tested the rst

generation o the EquuSense system with

help rom an expert veterinarian who useda technique called blocking. Commonly

used to pinpoint the location o an injury,

blocking involves anesthetizing an area o

the lame leg and then examining the horses

gait. I the horse begins to walk normally,

the injury can be assumed to be in the anes-

thetized area.

During tests o our rst-generation pro-

totype system, a veterinarian anesthetized

the leg o an injured horse so that the horse

was no longer visibly lame. Even to the vet-erinarians expert eye, the horse appeared

sound. However, the MALAB plots gen-

erated by our analysis revealed that the

horse was transmitting less power to the

right ront leg (RF) and more power to the

le rear leg (LR)he was leaning back to

put less pressure on the hurt leg (Figure 2).

Te data demonstrated that the horse

put much more pressure on LR than

on RF. Te plot on the right in Figure 2

shows the data or the anesthetized hoo.

Although the injury was no longer evident

rom a visual inspection, the plot clearly

shows that the animal is avoring the right

ront leg.

Tis test demonstrated that transmitted

power, a metric that we had calculated rom

the raw sensor data, provided a reliable in-

dication o the gait asymmetries associatedwith lameness.

We learned another important lesson

rom this test: Te EquuSense interace le

the vet unable to interpret the plots o trans-

mitted power without our help. Clearly, we

would have to improve all aspects o the

system in parallel. As we moved to more

advanced sensors with more sophisticated

signal processing algorithms, we would

also need to develop an interace that gave

users a more intuitive view o how the horse

was moving.

Figure 2. Plots showing power distribution o hooves or a horse with an injured right ront hoo.Each line represents a stride. Let: Transmitted power distribution beore blocking, indicating lesspower transmitted to RF than to LR. Right: Transmitted power distribution ater blocking, showingthat the horse still avors LR, indicating lameness that was undetectable in visual examination othe animals gait.


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Common Lameness Diagnosis Methods

At present, professionals rely primarily on the unaided human eye to observe the motion of

horses and use palpation and blocking of the lame limb to diagnose lameness.

Most quantitative approaches to diagnosing lameness, including high-speed cameras,

treadmills, and pressure pads, have met with limited success. A typical video motion-capture

system is designed for human athletes. It is not scalable to the speed and size of a horse;

its capture volume is typically limited to analyzing one or two steps at full gallop. On the

other hand, treadmills distort the gait of the horse, and attaching the approximately 75

video markers needed for analysis can take hours. Pressure pads simply do not produce

accurate results.

Second-GenerationEnhancements

Te second-generation prototype and sub-

sequent trials o EquuSense provided more

data on equine motion, including more pre-

cise measurements o acceleration and an-

gular rotation. It also included an enhanced

user interace, which we developed with

MALAB GUI-building tools.

When analyzing a horse, veterinarians

and equine proessionals use this soware

to dene sessions rom the trial data. A ses-

sion includes all the data or a particular

gait (trot, canter, and so on) or a specic

episode in the motion o the horse. Te ses-

sion is then processed using the MALAB

algorithms that we have been improving it-

eratively since the initial exploration phases

o development. Te algorithms recognize

gaits and calculate the 3-D hoo trajectory

(Figure 3) and the Euler angles or the ori-

entation o each hoo.

Users can annotate the data to markspecic events, such as a phased transition

rom the hoo landing on the ground to the

hoo taking o, and then replicate that event

throughout the data set o interest.

We achieved another critical break-

through in the second-generation

EquuSense: We developed MALAB al-

gorithms to create an animated repre-

sentation o the hoo ight path shown in

Figure 3. When we showed some o our

early users this animation together with

a video replay o the event, they reported

that this eature helped them visualize

vital aspects o the data and understand

it intuitively.

Figure 3. Three-dimensional plot o hoo flight path.


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91643v00 03/09

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Into Production and Beyond

Production EquuSense systems are already

being used in clinical research, and we

continue to improve the system. Wehave started to use Instrument Control

oolbox to stream data directly rom the

sensors into MALAB, and we plan to cre-

ate a standalone version o the EquuSense

soware with MALAB Compiler. We

are also exploring other applications or

the technology, including biomechanical

studies o human athletes.

For More Information EquuSys

www.equusys.com

Webinar: Technical computing withMATLABwww.mathworks.com/applications/

tech_computing

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91643 v00 Digest Mar09 EquuSys FINAL

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