Investigation of Sensor Parameters for Kinematic Assessment of Steady

State Running Using Foot Mounted IMUs

G. P. Bailey and R. K. Harle

Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge, U.K.

Keywords:

Running, Gait, Foot Kinematics, Continuous Sensing, Sampling Rates, Sensor Requirements, Impact

Acceleration.

Abstract:

The continuous sensing of kinematics provides an opportunity to monitor changes in sporting technique or

to aid in injury rehabilitation. Inertial sensors are now small enough to integrate into footwear, providing a

potential platform for continuous monitoring that does not require additional components to be worn by the

athlete and can be used to assess foot kinematics during running. To facilitate widespread adoption, sensor

systems must be as cheap as possible. To achieve this it is required that such systems be engineered with

sampling rates that are not unnecessarily high and with sensor components that meet the requirements of

the task, including required accuracy. We investigate multiple sensor parameters (sampling rate, acceleration

range) and their effect on the accuracy of kinematic assessment using foot worn inertial sensors. We ﬁnd that

Extended Kalman Filter based trajectory recovery seems to be little affected by sampling rates until below

250Hz. We investigate impact accelerations using an inertial measurement unit attached to the foot and ﬁnd

that, at 250Hz, the acceleration signal peaks at up to 70g around heel strike.

1 INTRODUCTION

Biomechanical assessment of movement is a compli-

cated but valuable component of today’s elite sports

training. Assessment of running gait is particularly

important and is usually performed within a labora-

tory setting using video or optical motion capture.

These assessments are often characterised by expen-

sive equipment, manual analysis and subjective met-

rics. Furthermore the restricted space of a laboratory

necessitates evaluation either using a small number of

steps or, more often, a treadmill. In neither case is the

athlete free to move naturally and there is little guar-

antee that the gait exhibited is that found in the true

sporting arena.

In order to address these issues and to bring such

kinematic assessment to a wider audience, low-cost

inertial sensors are being embedded within consumer

products, allowing athletes to be assessed in their nat-

ural setting and, additionally, more frequently. Such

in-ﬁeld constant-assessment brings with it additional

beneﬁts, including tracking the progress of injury re-

habilitation and enabling longitudinal sports science

and biomechanical studies.

Foot-mounted sensors are popular since

lightweight sensors can be embedded within

shoes in a convenient, unobtrusive way. They may be

able to capture rich data, and have already attracted

commercial interest (e.g. the Nike+ shoe). In the

future, such sensors may be able to track relevant

performance metrics or detect compensatory patterns

that are the result of poor biomechanics.

Previous studies have shown that foot-worn sen-

sors are capable of providing a full three dimen-

sional trajectory of the foot during steady state run-

ning (Bailey and Harle, 2014) and walking (Mariani

et al., 2010) when combined with the inertial strap-

down navigation algorithm. This allows various met-

rics to be calculated that may be of use to coaches

or biomechanists, for example peak foot height or

mean step velocity. Our previous work found that

that usable results could be achieved using both an

Extended Kalman Filter and a linear dedrifting tech-

nique in combination with the strapdown algorithm

(Bailey and Harle, 2014). The work also suggested

limitations in the accelerometer resulted in short pe-

riods of sensor saturation around heel strike, and that

this may have compromised results.

The purpose of this study is to assess the effect

of sensor parameters on the accuracy of tracking the

three dimensional trajectory of the foot during steady

state running.

154

P. Bailey G. and K. Harle R..

Investigation of Sensor Parameters for Kinematic Assessment of Steady State Running Using Foot Mounted IMUs.

DOI: 10.5220/0005093601540161

In Proceedings of the 2nd International Congress on Sports Sciences Research and Technology Support (icSPORTS-2014), pages 154-161

ISBN: 978-989-758-057-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

We address the following research questions:

• How do sampling rates affect the accuracy of tra-

jectory recovery?

Below a certain point, lower sampling rates might

be expected to produce less accurate results. At

what point does the sampling rate compromise re-

sults? There will be a trade off with sensor re-

quirements.

• What are the requirements of inertial sensors in

terms of range?

In order to capture the trajectory of the foot us-

ing inertial sensors, the captured signals should

not contain periods of saturation. The required

range of the sensor will depend on the sampling

rate used. For lower sampling rates, higher fre-

quencies will be attenuated during the low pass

ﬁlter stage reducing requirements on the sensor.

The running surface may also affect the sensor re-

quirements and so we include outdoor running on

a variety of surfaces.

• How much does a small amount of sensor satura-

tion affect results?

Our previous study found that any sensor satura-

tion usually happens at heel strike and typically

only for a few milliseconds (Bailey and Harle,

2014). Is it necessary to have a high range iner-

tial sensor or do periods of sensor saturation have

minimal affect on the accuracy of the results?

In answering these questions we will structure the

paper as follows. A section detailing the experimental

platform will be provided, followed by a section for

each of the research questions outlined above. These

sections will contain methods and results for the ex-

periment required to answer each question. We will

conclude by discussing how the results of each of the

three experiments trade off.

2 BACKGROUND

Limited research has been conducted with foot worn

sensors for running. However, some studies have

looked at impact accelerations at heel strike with sen-

sors attached to the shank. For example, (Mizrahi,

2000) investigated the effect of fatigue on impact

accelerations by attaching a ±50g accelerometer to

the tibial tuberosity. A high sampling rate was used

(1667Hz) and the authors found during the study that

the average impact acceleration increased with fatigue

to 11.1± 4.2g. However, impact accelerations at the

tibial tuberosity are likely to be much smaller than

those present at the foot.

Strapdown techniques have previously been inves-

tigated for use in assessing running kinematics but

sensor saturation was found around heel strike using

a ±16g sensor at 1kHz (Bailey and Harle, 2014). An-

other study (Bichler et al., 2012) used a similar tech-

nique with a 100Hz sampling rate but limitations in

the video reference system used as ground truth make

it difﬁcult to compare the two studies in terms of ac-

curacy.

For pedestrian localisation applications, optimal

sampling frequency has been investigated (Munoz

Diaz et al., 2013). Although the authors did investi-

gate a wide range of scenarios including running, they

did not evaluate the effects lower sampling rates had

on accuracy for running. They concluded from a fre-

quency based analysis that the lowest usable sampling

rate for running would be 300Hz.

3 SENSOR PLATFORM AND

DATA CAPTURE

The sensor platform and methods common to each

part of the paper will be described in the following

section.

3.1 Inertial Sensors

Capture of inertial sensor data was facilitated using

the ION (Imperceptible On-body Node) sensor plat-

form (Harle et al., 2011) with the addition of an In-

ertial Measurement Unit (IMU) providing a three-

axis ±16g accelerometer and ±2000

◦

−1

gyroscope

(MPU-6000, InvenSense Inc.) and containing an in-

ternal 16 bit Analog-Digital Converter (ADC).

Additionally an analogue three-axis accelerome-

ter (ADXL377, Analog Devices Inc.) with a ±200g

range was included on the same PCB as the MPU-

6000, mounted on the reverse side such that both sen-

sors were co-located to expose them to the same mo-

tion. The ADXL377 was connected to the ION sensor

platform’s 14 bit ADC.

The sensor platform is lightweight, weighting ap-

proximately 15 grams in total, including battery.

The platform was extended to contain two ac-

celerometers due to sensor saturation observed in ear-

lier experiments. The lower 16g range sensor was

used to capture the majority of the data with the 200g

sensor capturing the high frequency peaks that occur

near heel strike.

The MPU-6000 has an internal Digital Low Pass

Filter, meaning the accelerometer and gyroscope sig-

nals on the MPU-6000 had a bandwidth of 260Hz and

InvestigationofSensorParametersforKinematicAssessmentofSteadyStateRunningUsingFootMountedIMUs

155

256Hz respectively. The analogue accelerometer was

set up with a 500Hz bandwidth.

All IMU signals were sampled at 1kHz and logged

to on-board ﬂash memory. In all experiments the ION

sensor was placed on the lateral side of the shoe in

line with the ankle, as seen in Figure 1. The sensor

was ﬁrmly taped to the outside of the shoe to simulate

the scenario where it was built into the shoe, perhaps

embedded in the sole in a similar manner to the Nike+

shoe. Finally the validity of the zero-velocity assump-

tion (Foxlin, 2005) used in foot mounted inertial nav-

igation techniques has been evaluated (Peruzzi et al.,

2011). The study investigates different mounting lo-

cations on the foot and shows that the sensor position

we have used is among the best suited to using this

assumption.

3.2 Ground Truth

Ground truth, where applicable, was captured using

an optical motion capture system (Vicon Motion Sys-

tems, UK) sampling at 240Hz.

For experiments requiring ground truth, a tread-

mill was used in order to capture many steps in a lim-

ited motion capture area. While the biomechanics of

treadmill running may be different to overground run-

ning, results should be applicable to kinematic assess-

ment of overground running. This follows from the

observation that, from a sensing perspective, tread-

mill running differs from overground running only in

frame of reference.

The treadmill was set up without any inclination

as measured with a spirit level. The ION sensor was

attached to a custom jig containing 3 retro-reﬂective

markers (Fig. 1) for the motion capture system. The

jig adds an additional 30 grams of weight to the sys-

tem (45 grams total, including ION) but remains com-

fortable for test runs. The jig was laser cut and the

MPU-6000 and retro-reﬂective markers were aligned

with laser-etched outlines to ensure alignment be-

tween the jig and the inertial sensor axes.

Figure 1: Shoe with IMU and Jig for facilitating ground

truth capture using the Vicon Motion capture system.

3.3 Combining Accelerometer Signals

Our previous work shows that for treadmill running

at 3.4m/s and below, and at a sampling rate of 1kHz,

sensor saturation is present in the accelerometer sig-

nals when a 16g accelerometer is in use for short pe-

riods of time (Bailey and Harle, 2014).

Since the majority of the step contains signals

within the 16g range and only a few samples per step

are saturated, the 16g accelerometer data was used.

Any sensor saturation was corrected for using the

200g accelerometer to ‘ﬁll-in’ the saturated samples.

This means that the lower noise and higher resolution

(16 bit vs. 14 bit) of the MPU-6000 (16g) was utilised

for the majority of the step.

Combination of the accelerometer signals was

done in the following way. In order to avoid any

artifacts produced by sensor nonlinearities near the

limit of the MPU-6000s range, samples with a value

greater than 140ms

−2

were replaced by those from the

higher range sensor. When applicable, this replace-

ment was done before any further processing (e.g.

down-sampling).

4 METHODS AND RESULTS

In order to address the research questions outlined in

section 1, a number of experiments were designed to

test sensor requirements. The methods and results of

each experiment are described in this section.

4.1 Effect of Sampling Rates

In order to assess sensor requirements at varying sam-

pling rates, inertial data were collected at a 1kHz sam-

pling rate before being digitally downsampled to sim-

ulate lower sampling rates.

Four participants took part in the study (2 male,

2 female). All participants had a heel-strike running

pattern. Ethics committee approval was obtained.

Participants were asked to warm up on the treadmill

for a few minutes to familiarise themselves with the

environment and treadmill speeds. Once the warm

up period was complete, the athlete rested for two

minutes as the experimental process was explained.

Four ninety-second runs were completed, with data

logging, by each participant. Data logging included

inertial data and motion capture data as described pre-

viously. Each run was performed at a predetermined

treadmill speed, approximately 2.3ms

−1

, 2.7ms

−1

3.0ms

−1

and 3.4ms

−1

, as measured by the Vicon sys-

tem. Prior to and immediately after each run, the ath-

lete was asked to stamp their feet three times in order

to facilitate simple synchronisation between the Vi-

con and ION systems. A single sensor placed on the

right foot was used to conduct the experiment. Due

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to the acceleration and deceleration of the treadmill at

the start and end of each run the middle 90 steps were

taken from each run to provide a total of 1440 steps

for analysis.

Once data collection was complete the signals

were subsequently downsampled in order to simulate

lower sampling rates. Integer downsampling factors

were used so that interpolation was avoided.

Downsampling for a given downsampling factor,

M, proceeded as a two step process. Firstly, to avoid

aliasing affects, the data was low-pass ﬁltered. A 4th

order butterworth ﬁlter was used with a cut off fre-

quency of 0.8 f s

where f s

f s

and f s

is the target

sampling rate with f s

the base sampling rate used by

the ION sensor platform (always 1kHz). Secondly,

the resulting signal was decimated by retaining every

sample.

An Extended Kalman Filter (EKF), paired with an

inertial strapdown algorithm, was used to recreate the

trajectory of the foot from the inertial data (Bailey and

Harle, 2014; Foxlin, 2005). The speed of the treadmill

belt was applied as a pseudo measurement during the

mid-stance phase of gait along with a zero-foot height

pseudo measurement.

As an example of the kind of output that this tech-

nique can enable, Figure 2 contains two example steps

from two different people running at the same speed.

Differences in technique can be seen between the two

in these 2D plots. While 3D plots are possible we use

a 2D plot here to make the differences clearer.

This method was applied to each run from each

participant at each downsampling factor. The down-

sampling factors used produced the equivalent of

500Hz, 250Hz, 125Hz, and 62.5Hz in addition to the

1000Hz raw signal.

The technique creates a rich set of data detailing

the velocity, position and angle of the foot at the time

each inertial sample was taken. For trajectory evalua-

tion the position, velocity and attitude error is calcu-

lated for each step. Errors are calculated stepwise as

offsets in position at the start of the step are irrelevant

for an assessment of the step, therefore the ground

truth and inertial solutions are aligned in space before

calculating the following metrics.

Position error was calculated in the following

way:

error

(i, k) = ks

inertial

(i, k) −s

vicon

(i, k)k (1)

and velocity error was calculated as:

error

(i, k) = kv

inertial

(i, k) −v

vicon

(i, k)k (2)

where i is the step number and k sample number

within step i. Error in attitude was assessed as

err

(i, k) = arccos



A.B

kAkkBk



(3)

Where A and B represent the vector [0, 0, 1]

in the

sensor’s frame of reference as measured by the INS

solution and Vicon respectively.

Figure 3 shows how sampling rates affect the

mean error in position, velocity and attitude. The

graphs show that with sampling rates lower than

250Hz the position and velocity error starts to in-

crease rapidly meaning that 1kHz is unnecessary and

in order to reduce sensor requirements a lower sam-

pling rate may be used without a large affect on accu-

racy up to 250Hz. Attitude errors were not as affected

by lower sampling rates staying stable until 125Hz.

Examples of the full 3D trajectory recovered by

the system are shown for two representative steps in

Figure 4 where the lower sampling rate has resulted

in much poorer performance.

4.2 Sensor Requirements

In order to ﬁnd optimal sensor parameters we con-

ducted an experiment to determine the requirements

for the range of the accelerometer and gyroscope.

This is important to make sure that sensors do not sat-

urate during running as this may impair the accuracy

of the measurement obtained using strapdown tech-

niques.

Parameters that affect these requirements are sam-

pling rate, running speed and the characteristics of the

running surface.

Sampling rates affect sensor requirements due to

the low pass ﬁltering required before the signals en-

ter the ADC. Before sensor signals are quantised, it

is usually necessary to low-pass ﬁlter the signal (in

the analogue domain) to a bandwidth of less than half

of the sampling rate (Nyquist rate) to ensure aliasing

artifacts are avoided. This low-pass ﬁltering has the

effect of reducing peak accelerometer and gyroscope

signals. Sensor range requirements are therefore re-

duced as the sampling rate is also reduced. We there-

fore assess peak accelerations for running while using

differing sampling rates.

The accuracy of the algorithms used in (Bailey

and Harle, 2014) to assess running kinematics have,

so far, been assessed using a treadmill. While in use

treadmills may ﬂex visibly as the runner hits the tread-

mill belt. This may reduce the peak accelerations ob-

served at impact. Since the primary use case of such

sensing is in overground running outside, we investi-

gated the effect of a number of outdoor surfaces on

the sensor range requirements as these are likely to be

larger than for a treadmill. For example, impact accel-

erations on tarmac are likely to be distinct from those

of grass or treadmill running.

Accelerometer and gyroscope data were collected

InvestigationofSensorParametersforKinematicAssessmentofSteadyStateRunningUsingFootMountedIMUs

157

(a) Participant A (b) Participant B

Figure 2: 2D plots of position taken from inertial sensor data and ground truth using a motion capture system. The red line

represents ground truth and the blue the inertial solution. Both solutions taken from running on a treadmill at 3.4ms

−1

0 200 400 600 800 1000

Sampling Frequency (Hz)

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Mean Error (m)

3.4ms

−2

3.0ms

−2

2.7ms

−2

2.3ms

−2

(a) Position

0 200 400 600 800 1000

Sampling Frequency (Hz)

0.1

0.2

0.3

0.4

0.5

Mean Error (ms

−1

)

3.4ms

−2

3.0ms

−2

2.7ms

−2

2.3ms

−2

(b) Velocity

0 200 400 600 800 1000

Sampling Frequency (Hz)

1.6

1.8

2.0

2.2

2.4

2.6

Mean Error (deg)

3.4ms

−2

3.0ms

−2

2.7ms

−2

2.3ms

−2

Figure 3: Mean error for position, velocity and attitude, for differing sampling rates.

(a) 125Hz (b) 250Hz

Figure 4: Figures showing a single step’s trajectory recovery at two different sampling rates.

for 3 participants (2 male, 1 female) and 5 surfaces.

The 5 surfaces tested were picked as likely scenarios

for outdoor running. Surfaces chosen were running

track, astro turf, tarmac, gravel, and grass.

Participants were asked to run on each of the

different surfaces, which were located in the same

area. Each participant ran 100 meters on each surface.

The data were then segmented into steps, midstance-

midstance and from each step the peak acceleration

was recorded.

Participants were asked to run at a self-selected

speed that would be representative of their steady

state running. The speed of each participant’s run was

estimated using a linear dedrifted strapdown algo-

rithm (Bailey and Harle, 2014; Mariani et al., 2010).

This was chosen over a Kalman ﬁlter based approach

due to the lower number of steps logged. The linear

dedrift method does not need time for a ﬁlter to set-

tle and so was better suited to these shorter runs with

similar levels of accuracy as veriﬁed by previous stud-

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3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

Mean Foot Velocity, SV (ms

−1

)

200

400

600

800

1000

Peak Acceleration (ms

−2

)

1000Hz

astro

grass

gravel

tarmac

track

(a) 1000Hz

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

Mean Foot Velocity, SV (ms

−1

)

200

400

600

800

1000

Peak Acceleration (ms

−2

)

250Hz

astro

grass

gravel

tarmac

track

(b) 250Hz

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

Mean Foot Velocity, SV (ms

−1

)

200

400

600

800

1000

Peak Acceleration (ms

−2

)

125Hz

astro

grass

gravel

tarmac

track

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

Mean Foot Velocity, SV (ms

−1

)

200

400

600

800

1000

Peak Acceleration (ms

−2

)

62.5Hz

astro

grass

gravel

tarmac

track

(d) 62.5Hz

Figure 5: Figures showing peak acceleration plotted against mean foot speed for each step. The horizontal lines marks the

limit of the 16g sensor above which the sensor with a ±16g range would saturate.

ies.

The results obtained show that the largest factor in

peak acceleration was the foot speed but that surface

also affected requirements. Figure 5 shows the im-

pact acceleration for all steps logged plotted against

the mean foot velocity (SV) for each step. SV was de-

ﬁned as in (Mariani et al., 2010), that is mean velocity

in the ground plane (the XY plane). For each step, be-

tween midstance events, the following was calculated:

SV (i) =

∑

k=0

(i, k)

+ v

(i, k)

(4)

where i is the step number, k is the sample number

within step i, and N

is the number of samples within

step i.

Figure 5 shows that even at 62.5Hz there were a

few samples that would saturate a 16g sensor at higher

speeds. At 1kHz the majority of steps would show

sensor saturation with a 16g sensor and the situation

improves only slightly at 250Hz, the point which we

found to be the optimal sampling rate.

Mean and max peak accelerations are shown in

Table 1 showing high maximum accelerations on tar-

mac of around 90g. Study of peak gyroscope signals

were inconclusive as at higher speeds the sensor satu-

rated and it was not possible to obtain a higher range

sensor than the ±2000

◦

−1

sensor contained in the

MPU-6000. Figure 6 shows the data obtained at the

1kHz sampling rate, sensor saturation can be seen at

higher speeds.

4.3 Effect of Saturation on

Measurement Accuracy

While sensor saturation has been suggested as a po-

tential factor that may decrease accuracy of measure-

ment results no work has yet investigated how signif-

icant the loss in accuracy might be. Here we compare

the accuracy of results using the 16g accelerometer

only, to that which replaces saturated samples with

those taken from the 200g accelerometer.

This investigation re-uses the data listed in section

4.1 and the Extended Kalman Filter was applied in

InvestigationofSensorParametersforKinematicAssessmentofSteadyStateRunningUsingFootMountedIMUs

159

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

Mean Foot Velocity, SV (ms

−1

)

500

1000

1500

2000

Peak Angular Velocity (deg.s

−1

)

astro

grass

gravel

tarmac

track

Figure 6: Plot of maximum rate against mean foot speed

showing saturation of the ±2000

◦

−1

gyroscope.

Table 1: Mean and maximum step accelerations for the ac-

celerometer.

Sampling

Rate (Hz)

Surface Mean

(ms

−2

)

Max.

(ms

−2

)

1000

astro 244 575

grass 229 503

gravel 438 954

tarmac 481 922

track 430 729

250

astro 207 539

grass 168 428

gravel 261 651

tarmac 286 694

track 272 635

the same manner. However the ﬁlter was run twice,

once with the data as described in section 3.3 and once

with data from only the 16g accelerometer containing

sensor saturation. The data were processed for the

highest sampling rate available, 1kHz.

Error statistics were calculated as in 4.1 to give

error values for each sample in each step. Subse-

quently, for each step, the maximum and mean er-

rors were calculated for position, velocity and atti-

tude. For example s

error

max

(i) = max

error

(i, k)) and

error

mean

(i) = mean

error

(i, k)) give the maximum er-

ror in position and the mean error in position for step

i. These statistics were similarly calculated for veloc-

ity and attitude. The mean and standard deviation for

these statistics are presented in Table 2.

The results show an increase in the error for

the 16g only trajectory recovery. Table 2 shows

the results for the fastest treadmill speed recorded,

3.4ms

−1

. It can be seen that there is a reduction in

the mean and maximal error when using the combined

16g/200g sensor as opposed to 16g only.

As an example of a potentially interesting metric

that could be calculated, the mean foot velocity was

also assessed over all steps. Mean foot velocity (SV)

was calculated as deﬁned in section 4.2.

Bias, standard deviation and limits of agreement

were calculated for the SV metric over all steps for

both sensor setups. Using the combined setup the

bias and standard deviation were 0.007±0.0156ms

−1

with limits of agreement −0.023ms

−1

to 0.038ms

−1

Using the 16g only sensor the bias and standard de-

viation were 0.006 ±0.020ms

−1

with limits of agree-

ment -0.033ms

−1

to 0.044ms

−1

. Bland-Altman plots

for the results can be seen in Figure 7. Both show

some correlation in error with speed and the com-

bined sensor setup shows slightly tighter limits of

agreement. However, both sensor setups produce a

level of accuracy that may be useful for this metric.

Table 2: Mean and standard deviation of per-step met-

rics for different sensor setups at 1kHz while running at

3.4ms

−1

Measurement

Sensor Mean and SD

error

max

(m)

Combined 0.087 ± 0.061

16g 0.142 ± 0.115

error

mean

(m)

Combined 0.048 ± 0.033

16g 0.079 ± 0.063

error

max

(ms

−1

)

Combined 0.52 ± 0.12

16g 0.60 ± 0.21

error

mean

(ms

−1

)

Combined 0.16 ± 0.08

16g 0.24 ± 0.15

error

max

(Deg)

Combined 3.30 ± 0.90

16g 4.71 ± 1.85

error

mean

(Deg)

Combined 1.77 ± 0.71

16g 2.98 ± 1.48

5 CONCLUSIONS

This work has shown that a trade off in sampling rate

is possible in order to obtain lower sensor require-

ments. Extended Kalman Filter based trajectory re-

covery seems to be little affected by sampling rates

until below 250Hz. However at 250Hz there would

still be regular saturation in overground running on

outdoor surfaces using a ±16g accelerometer.

We also show that sensor saturation does affect the

accuracy of the trajectory recovery. We found that the

addition of a high range accelerometer resulted in up

to a 40% reduction in error for some metrics, for ex-

ample the reduction of mean attitude error reduced

from 2.89

◦

to 1.77

◦

. However, it remains unclear

how much larger peak accelerations present in higher

speeds in an outdoor scenario would affect the accu-

racy as we were limited in the maximum speed of the

treadmill. As such, it remains unclear as to how the

icSPORTS2014-InternationalCongressonSportSciencesResearchandTechnologySupport

160

1.6 1.8 2.0 2.2 2.4 2.6

Mean SV measured by Vicon and EKF 16g (ms

−1

)

−0.08

−0.06

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

Difference of SV measured by Vicon and EKF 16g (ms

−1

)

(a) 16g

1.6 1.8 2.0 2.2 2.4 2.6

Mean SV measured by Vicon and EKF Combined (ms

−1

)

−0.08

−0.06

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

Difference of SV measured by Vicon and EKF Combined (ms

−1

)

(b) Combined

Figure 7: Bland-Altman plots for SV as calculated from Combined and 16g sensor data.

method would perform for elite endurance athletes at

greater running speeds. Further research should seek

to attempt to evaluate these methods at higher speed.

We have been unable to assess requirements for

the range in angular velocity measurement to inform

gyroscope selection due to limitations in our sensor

at higher speeds and it may prove difﬁcult to test in

the future due to the lack of commercial availability

of MEMS gyroscopes that have a higher range than

the ±2000

◦

−1

one used in this paper. Further work

may therefore seek to assess whether or not gyroscope

saturation has a signiﬁcant affect on measurement ac-

curacy. If it is found to affect results further work

should seek to mitigate these periods of saturation if

possible.

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InvestigationofSensorParametersforKinematicAssessmentofSteadyStateRunningUsingFootMountedIMUs

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