Evaluation of Gait Parameters Determined by InvestiGAIT against a

Reference System

Katja Orlowski

1,2

, Harald Loose

1

, Falko Eckardt

2

, J

¨

urgen Edelmann-Nusser

2

and Kerstin Witte

2

1

Department of Computer Science and Media, Brandenburg University of Applied Sciences, Brandenburg, Germany

2

Institute of Sport Science, Otto-von-Guericke University, Magdeburg, Germany

Keywords:

Gait Analysis, Gait Parameters, Validity, Inertial Measurements, Motion Capture.

Abstract:

The purpose is to investigate the validity of an inertial-sensor based gait analysis system (InvestiGAIT) con-

sisting of off-the-shelf sensors and an in-house capturing and analyzing software. The gait of ﬁve persons with

transfermoral limb loss were captured with the inertial system (Shimmer sensors) and the motion capture sys-

tem (Vicon) integrating two force plates chosen as reference system in this study. Eleven gait parameters are

determined from the data of the captured gait sequences. These gait parameters were compared descriptively

and statistically using boxplots, Bland-Altman-plots, including the mean of difference (MOD) and the limits

of agreement (LoA), the standard error of the mean (SEM), the Wilcoxon test and the Pearson’s correlation

coefﬁcient. A complete validity of the gait parameters was not assumed due to the different measurement

methods and the impact of the IMU sensor attachment (on the lower shank above the ankle). For the sound

and the amputated leg four gait parameters show no signiﬁcant difference (stride duration, cadence, velocity,

stride length). All the other parameters have a p-value smaller than 0.05. Most of the gait parameters have

a small MOD, SEM and LoA. These values show a very small absolute difference between the gait parame-

ters of both systems. Based on the results the InvestiGAIT system can be assumed as valid and suitable for

follow-up investigations of human gait in research projects or the clinical environment. Nevertheless, further

investigations with healthy subjects and a sensor attachment on the subjects’ shoe are planned.

1 INTRODUCTION

The instrumental gait analysis is a well-known exam-

ination method. The gold standard of the instrumen-

tal gait analysis are motion capture systems, such as

Vicon, combined with force plates (Richards, 1999;

Azad, 2009). Due to the costs and time which are

related to an examination, that established method is

only available for university hospitals and research

centers. Smaller institutions or physiotherapeutical

units do not have either the time during the therapeu-

tic process nor the money to purchase such a system

(Cloete and Scheffer, 2008). In the daily routine the

gait analysis is done visually or with the assistance of

simple video recording. The latter is useful to show

the patient’s existing deviations from the normal gait.

This is a supporting tool used to be sensible for gait

anomalies. For that purpose the visual recording is

very useful, but it cannot be used for quantifying these

anomalies. Inertial measurement units (IMU) could

be helpful for that application area and necessary for

follow-up examinations. (Cuesta-Vargas et al., 2010)

Due to the technical development IMUs are nowadays

small in size, cost-efﬁcient and permit a fast and easy

analysis of the human gait. Our system called Investi-

GAIT consists of two to four IMU (Shimmer) and the

in-house capturing and analyzing software are used

in different projects in teaching and research. Before

such an inertial-sensor based gait analysis system can

be used in the clinical environment the reliability, va-

lidity and objectivity of the system has to be checked

(Atkinson and Nevill, 1998). The reliability was al-

ready investigated and results are prepared for publi-

cation.

Schwameder et al. (Schwameder et al., 2015) exam-

ined the validity of the GaitUp system (Physiolog4,

GaitUp System, Lausanne, Switzerland, 200 Hz) un-

der normal and limping gait conditions. The sensors

are attached directly to the shoes of the subjects. As-

pects of validity are given for ﬁve gait parameters as

absolute differences between the both system, as cor-

relation coefﬁcient and Bland-Altman plots.

The aim of that paper is to present the results of the

validity examination. The gait parameters determined

256

Orlowski, K., Loose, H., Eckardt, F., Edelmann-Nusser, J. and Witte, K.

Evaluation of Gait Parameters Determined by InvestiGAIT against a Reference System.

DOI: 10.5220/0005783502560262

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 256-262

ISBN: 978-989-758-170-0

Copyright

c

2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

by InvestiGAIT are compared to those parameters de-

termined by the reference system, the motion capture

system Vicon. Due to the fact that the gait parameter

stride length can be calculated in different ways, three

methods are presented. The results are compared to

the stride length determined by the Vicon system. The

most valid calculation method should be speciﬁed and

used in further investigations.

2 METHODS AND MATERIALS

The gait of ﬁve persons with transfemoral limb loss

was captured with Shimmer sensors (9-DoF-Sensors,

Shimmer, Dublin, Ireland) and the motion capturing

system Vicon (Vicon Motion Systems Ltd. UK). In

parallel the gait sequences were recorded with a dig-

ital camera. The Shimmer sensors were attached lat-

erally to the lower shank above the ankle. The Vicon

system consisted of 12 infrared cameras and was syn-

chronized with two force plates (AMTI, AMTI Force

and Motion, Watertown, USA). 36 passive markers

were attached to the body of the subject to capture

the motion of the body during the gait sequences.

The full-body markerset Plug-In-Gait was used to de-

tect the gait events and calculate the gait parameters.

The measured motion signals (acceleration and angu-

lar velocity) were analyzed with an in-house software

(InvestiGAIT) implemented in MATLAB (TheMath-

works Inc., Natick, MA, USA).

Figure 1: Marker and sensor placement on the amputated

leg.

The gait events (initial and terminal contact, IC

and TC) were detected from the angular velocity

signal of the z axis representing the rotation in the

sagittal plane. The gait events are deﬁned as the local

minima (IC and TC) and maxima (midswing) of the

gyroscope signal described in (Orlowski and Loose,

2013) similar to (Greene et al., 2010; Mannini and

Sabatini, 2012; Hundza et al., 2014). The heel contact

(IC) is detected using a threshold of 20 N based on

the force plates. Without the force plates the height

of the heel marker is used to detect the heel contact.

In case of the inertial sensors the motion of the lower

shank is tracked during the gait. It is known that the

detected IC and TC events correspond to the events

detected by the reference system, but are not identical.

2.1 Setting

The group of subjects consisted of four men and one

woman. The age was 47.7 ± 10.3 years, the duration

of wearing the prosthesis was 23.2 ± 21.8 years, the

height of the subjects was 176.8 ± 6.3 cm with body

weight of 89,6±17.6 kg. Two subjects have the pros-

thesis on the right and three on the left side.

Each subject walked a 12 m line straight forward with

his/her normal self-selected speed. The gait sequence

was repeated at least 12 times.

Vicon measurements were classiﬁed as valid when

only one single foot contact was registered for each

force plate. Some inertial measurements had to be re-

jected due to storage errors. Consequently, only nine

to ten gait sequences per subject were analyzed. From

the whole gait sequence only the gait cycle, which

was done on the force plate, was investigated by both

systems.

For both systems the gait cycles made on the force

plates was investigated. In case of the IMU analy-

sis, the additional video recording was used to ﬁnd

out which gait cycles were done on the force plates.

A visual inspection of the video recording has to be

conducted.

2.2 Gait Parameters

The gait parameters are calculated by stride-by-stride

based algorithm after detecting the gait events TC,

midswing, and IC of each leg from the angular ve-

locity signal in the sagittal plane (see ﬁgure 2). The

validity of the following gait parameters listed and de-

scribed below were investigated:

• Stride Duration [s]: duration from one IC to the

next IC

stride

i

= IC

i+1

− IC

i

• Swing Duration [s]: duration from TC to IC

swing

i

= IC

i

− TC

i

• Single Leg Support [s]: duration of the stance

phase while the contralateral leg has no contact to

the ground, corresponds to the swing phase of the

contralateral leg given as example for the right leg

SLS

R

i

= swing

L

i

Evaluation of Gait Parameters Determined by InvestiGAIT against a Reference System

257

Figure 2: Angular velocity of the sound (blue solid) and amputated leg (red dotted) with marked gait events and gait parameters

- stride, stance and swing (step).

• Double Leg Support [s]: duration of the contact

of both legs to the ground, comprise of initial and

terminal double leg support given as example for

the right leg

DLS

R

initial

i

= TC

R

− IC

L

,

DLS

R

terminal

i

= TC

L

− IC

R

,

DLS

R

i

= DLS

R

initial

i

+ DLS

R

terminal

i

• Cadence [Steps/min]: step frequency; number of

steps (stepsL, stepsR) during the gait sequence

(duration) related to a one minute walk

cadence = 60 ∗ ((stepsL + stepsR)/duration)

• Velocity [m/s]: the average velocity calculated by

the known distance (l) divided by the duration of

the gait sequence (velocity = l/duration)

• Stride Length (Acc) [m]: stride length calculated

by the integration of the acceleration of the hori-

zontal sensor axis (x-axis)

strideLengthAcc =

RR

a

x

dt

2

• Stride Length (Dist) [m]: known distance (l) di-

vided by the number of steps of the considered leg

strideLengthDist = l/steps

• Stride Length (Vel) [m]: stride duration multi-

plied by determined velocity

strideLengthVel = stride ∗ velocity

As the list above shows the stride length is determined

in different ways. The ﬁrst calculation method is

based on the double integration of the acceleration in

the horizontal direction (strideLengthAcc). As known

from other investigation (Orlowski and Loose, 2014)

or from the literature (Thong et al., 2002) that inertial

sensors (in particular accelerometer) suffer from

noise and drifts which are accumulated with time

during the integration process. A priori knowledge

or additional sensors are necessary to avoid errors in

the distance estimation (Latt et al., 2011). Due to the

fact that the sensor’s coordinate system rotates during

the swing phase of the leg, a projection would be

Figure 3: Boxplots of the gait parameters swing duration

and stride length (Vel). Left the distribution of the parame-

ter determined by InvestiGAIT and right of the Vicon sys-

tem.

necessary to get the real acceleration in the horizontal

axis. Without this projection an overestimating of

stride length is assumed. Alternatively, the stride

length (strideLengthDist) can be calculated if the

walked distance and the number of steps during

the walking sequence are known or automatically

determined. The stride length is the ratio of the

entire distance and the number of steps. The third

calculation method (strideLengthVel) is the product

of the stride duration (in seconds) and the average

velocity (meter per seconds).

The gait parameters were determined for both legs:

the amputated and the sound leg. Due to the fact that

some of the subjects had the prosthesis on the left and

others on the right side, the investigations referred to

the amputated and sound side.

2.3 Statistics

To investigate the validity of the inertial-sensor

based gait analysis system the values of the gait

parameters were compared with those of the Vicon

system. Therefore, boxplots of each parameter were

prepared. Furthermore, Bland-Altman-Plots were

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

258

created to ﬁnd the mean of differences (MOD), its

standard deviation as well as the limits of agree-

ment (LoA) between the values of both systems

(Atkinson and Nevill, 1998; Bland, 1986). Based on

the Kolmogorov-Smirnov-test the existing datasets

were inspected regarding normal distribution. In

presence of normal distribution the Students T-test

and Pearson’s correlation coefﬁcient was conducted.

Otherwise, in case of not normal distributed data, the

Wilcoxon rank sum test and the Spearmans Rho was

used. The level of signiﬁcance was set to = 0.05. The

strength of the correlation coefﬁcient is based on the

assessment scheme of Pavetic: weak (r < 0.2), low

(r > 0.2 & r < 0.4), moderate (r > 0.4 & r < 0.7),

strong (r > 0.7 & r < 0.9) and very strong r(> 0.9)

(Pavetic, 2015). Additionally, the standard error of

the mean (SEM) was calculated.

All the calculations and statistical analyses were done

in the MATLAB programming environment.

3 RESULTS

Figure 3 shows the boxplots for the swing duration

and the strideLengthVel of the amputated leg. In both

cases the distribution of the values of both systems

(Inertial and Vicon) seems to be similar. Neverthe-

less, as the further investigation will be shown the dif-

ference of the gait parameter swing duration is more

pronounced than that of the StrideLengthVel.

Tables 1 and 2 present the results of the absolute va-

lidity test in terms of the MOD, its standard devia-

tion, the SEM, the LoA, the p-value of the Wilcoxon

test and the correlation coefﬁcient (Spearmans Rho).

The gait parameters stride duration, cadence, velocity

and strideLengthVel have p-values larger than 0.05 for

both sides (sound and amputated) which means that

these parameters have no signiﬁcant difference and

show a good agreement with the reference system.

Figure 4 shows the Bland-Altman plots of the gait

parameters: stride duration and stride length (stride-

LengthVel) of the amputated leg. The MOD, its stan-

dard deviation and the LoA is given for each param-

eter. The MOD and its standard are for both param-

eters very small showing a good agreement of both

systems. These parameters are those that show no sig-

niﬁcant difference between both systems (see tables 1

and 2).

For the most gait parameters of both legs the MOD, its

SD and the LoA are similar. As the summarized val-

ues in the tables 1 and 2 demonstrate, there are only

small differences between the gait parameters of the

amputated and sound leg determined by the systems.

Considering the SEM of the gait parameters it is a bit

Figure 4: Bland-Altman Plots of the gait parameters stride

duration and stride length (Vel) of the amputated leg. The

MOD, its standard deviation and the limits of agreement are

presented.

higher for the amputated leg regarding the parame-

ters stride duration, cadence, velocity, and single sup-

port. The values of the gait parameters of the sound

leg determined by InvestiGAIT correlate a bit better

with those values of the chosen reference system Vi-

con than the parameters of the amputated leg. Six

parameters have a large correlation coefﬁcient greater

than 0.7. According to Cohen (Cohen, 1988), these

correlations can be seen as strong.

Tables 1 and 2 depict that the parameter stride-

LengthVel has a good agreement to the stride length

which is determined by the Vicon system. The

p-values larger than 0.05 (p

amputated

= 0.88 and

p

sound

= 0.83) establish that there is no signiﬁcant dif-

ference between the parameters. In contrast, the other

parameters (strideLengthAcc and strideLengthDist)

show a signiﬁcant difference. Furthermore, the MOD

is larger than that of the parameter strideLengthVel.

The same applies to the SEM which is larger. It has to

be noted that the parameter strideLengthAcc perform

worst of the three alternatives.

4 DISCUSSION

A complete validity of the gait parameters of the

inertial-sensor based gait analysis system (Inves-

tiGAIT) with gait parameters of the reference

system was not expected because of the different

measurement principals. The gait events, initial and

terminal contact, are determined in different ways

Evaluation of Gait Parameters Determined by InvestiGAIT against a Reference System

259

Table 1: Sound leg: Statistical values (Mean of the Difference (MOD), SD of MOD, standard error of the mean (SEM),

limits of agreement (LoA), p-value of the Wilcoxon test, correlation coefﬁcient) for all gait parameters as the difference of

both systems. The asterisk characterizes the values showing no signiﬁcant difference between the measurements of the two

systems.

param MOD SD (MOD) SEM (95 %) LoA p-value correlation

stride [s] -0.003 0.027 0.037 -0.06 to 0.05 0.779* 0.887

swing [s] -0.137 0.068 0.094 -0.27 to -0.00 0.000 -0.454

single support [s] 0.043 0.026 0.036 -0.01 to 0.09 0.000 0.872

double support [s] -0.161 0.073 0.101 -0.30 to -0.02 0.000 0.565

cadence [steps/min] 0.409 2.664 3.693 -4.81 to 5.63 0.779* 0.887

velocity [m/s] -0.013 0.059 0.081 -0.13 to 0.10 0.839* 0.953

strideLengthAcc [m] 0.496 0.470 0.652 -0.43 to 1.42 0.000 -0.482

strideLengthDist [m] -0.178 0.116 0.160 -0.40 to 0.05 0.000 0.830

strideLengthVel [m] -0.006 0.077 0.107 -0.16 to 0.15 0.834* 0.889

Table 2: Amputated leg: Statistical values based on the difference of both considered systems (mean of differences (MOD), SD

of MOD, standard error of the mean (SEM), limits of agreement (LoA), p-value of the Wilcoxon test, correlation coefﬁcient)

for all gait parameters. The asterisk characterizes the values showing no signiﬁcant difference between the measurements of

the two systems.

param MOD SD (MOD) SEM (95 %) LoA p-value correlation

stride [s] 0.012 0.029 0.041 -0.05 to 0.07 0.448* 0.841

swing [s] -0.084 0.041 0.057 -0.16 to -0.00 0.000 0.835

single support [s] 0.034 0.044 0.061 -0.05 to 0.12 0.000 0.335

double support [s] -0.218 0.073 0.101 -0.36 to -0.07 0.000 0.358

cadence [steps/min] -0.997 2.882 3.994 -6.65 to 4.65 0.448* 0.841

velocity [m/s] -0.018 0.062 0.085 -0.14 to 0.10 0.811* 0.968

StrideLengthAcc [m] 0.123 0.395 0.548 -0.65 to 0.90 0.001 -0.166

StrideLengthDist [m] -0.114 0.082 0.113 -0.27 to 0.05 0.001 0.882

StrideLengthVel [m] 0.004 0.069 0.096 -0.13 to 0.14 0.881* 0.890

and it is known that IC and TC of both systems are

corresponding but not identical. Consequently, the

derived gait parameters are similar. For the most

of the parameters a small deviation was expected.

Similar results were published by (B

¨

ohme, 2012;

Derlien et al., 2010)

This expectation was conﬁrmed by the results of

the Wilcoxon test whose p-values show a signiﬁcant

difference between both systems for the most gait

parameters. Although, the other results in terms of

the MOD, SEM and LoA prove the similarity of the

gait parameters determined by the different systems.

The results of the validity examination of

Schwameder et al. (Schwameder et al., 2015)

are similar to our results. The authors present

correlation coefﬁcients and LoA. The correlation

coefﬁcients are comparable and in three cases smaller

(stride duration 0.887 vs 0.986, stride length 0.889

vs 0.951, cadence 0.887 vs 0.981) in one case a

bit smaller (velocity 0.953 vs 0.967) in our system.

Schwameder et al. presented the limits of agreement

for one gait parameter (velocity) in visual way

(Bland-Altman plots) without given the concrete

value. That is why the LoA are hardly comparable to

those achieved in our examination due to the fact that

different speeds of limping walking are integrated in

the analysis. The LoA are slightly narrower (-0.13 to

0.10 vs -0.6 to 0.9) in comparison to our system.

The investigation of the different calculation method

for determining the gait parameter stride length show

remarkable differences between the three methods.

The best agreement with the reference system was

achieved by the parameter strideLengthVel which

involves the stride duration and the average veloc-

ity. The parameter strideLengthAcc which based

on the integration of the horizontal acceleration

demonstrates the worst agreement with the Vicon

measurements. The reasons are that no a priori

knowledge is used in the proposed stride-by-stride

algorithm, no additional sensors, such as magne-

tometers, are used to reliably estimate the position

of the sensor during walking, and a projection of the

acceleration to the horizontal axis was not conducted.

An impact on the missing validity of that parameter is

the chosen position of the sensor on the lower shank.

Other investigations have shown that the double

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

260

integration based on shoe-mounted sensors achieved

excellent results in distance estimation (Loose and

Orlowski, 2015b; Loose and Orlowski, 2015a).

Singleton et al. (Singleton et al., 1992) published a

study where they have shown that the prediction of

step time from step length and average velocity is

not a good choice for asymmetric gait patterns. In

contrast to the step length / duration, the stride length

and stride duration used in our algorithm are largely

the same for both legs. Following the effect described

by Singleton has no impact on our algorithm. Based

on the achieved results in the present validity study

using the average velocity and the stride duration for

the determination of stride length, a valid method

is found. Furthermore, the use of average velocity

could affect negatively if the walking velocity is not

constant during the walking sequence. Regarding

further investigations in the clinical environment with

patients having different asymmetries, the problem

depicted by Singleton et al. (Singleton et al., 1992)

should be kept in mind and a valid step length

algorithm should be used in order to examine the

asymmetric gait pattern of the patients.

5 CONCLUSION

At the end of these investigations we can conclude

that the gait parameters determined by the inertial-

sensor based gait analysis system InvestiGAIT show

partly excellent agreement with those parameters de-

termined by the reference system. Based on the re-

sults of the Wilcoxon test the agreement of four pa-

rameters have be presented. Furthermore, other pa-

rameters show a good agreement between the systems

in terms of a strong correlation. Small MOD, SEM

and LoA show similar effects. Consequently, the gait

parameter determined by InvestiGAIT are assumed as

valid and the system can be used in follow-up exami-

nations.

Nevertheless, further investigation are planned with

an adapted attachment of sensors on the subjects’

shoe in order to get a higher conformity of the gait

parameters. Furthermore, these measurements should

be conducted with a group of subjects without any

gait limitations in order to exclude the impact of the

disease. In this connection another method for calcu-

lating stride length proposed by (Mercer and Chona,

2015) should be investigated regarding validity in re-

lation to the reference system. They use the stride

length as the ratio of velocity and stride frequency

which is the number of foot contacts per second mea-

sured in Hertz (Magness, 2010).

ACKNOWLEDGEMENT

The authors would like to thank all the participants

taking part in that preliminary study.

REFERENCES

Atkinson, G. and Nevill, A. M. (1998). Statistical methods

for assessing measurement error (reliability) in vari-

ables relevant to sports medicine. Sports Medicine,

26(4):217–238.

Azad, P. (2009). Visual Perception for Manipulation and

Imitation in Humanoid Robots. Springer.

Bland, J. Martin; Altman, D. G. (1986). Statistical meth-

ods for assessing agreement between two methods of

clinical measurement. The Lancet, 8:307–309.

B

¨

ohme, B. (2012). Studie zur Untersuchung der Va-

lidit

¨

at des neuen Ganganalysesystems RehaWatch der

Firma Hasomed an gesunden Probanden. PhD thesis,

Friedrich-Schiller-Universit Jena.

Cloete, T. and Scheffer, C. (2008). Benchmarking of a full-

body inertial motion capture system for clinical gait

analysis. 30th Annual International IEEE EMBS Con-

ference.

Cohen, J. (1988). Statistical power analysis for the behav-

ioral sciences. (2nd ed.). Hillsdale, NJ: Erlbaum.

Cuesta-Vargas, A. I., Galan-Mercant, A., and Williams,

J. M. (2010). The use of inertial sensors system for

human motion analysis (systematic review). Physical

Therapy Reviews, 15(6):462–473.

Derlien, S., B

¨

ohme, B., and Smolenksi, U. (2010). Va-

lidit

¨

atsuntersuchung zum neuen, innovativen gang-

analysesystem rehawatch von hasomed. Manuelle

Medizin, 48(254-259).

Greene, B. R., McGrath, D., ONeill, R., ODonovan, K. J.,

Burns, A., and Caul?eld, B. (2010). An adaptive

gyroscope-based algorithm for temporal gait analysis.

Med Biol Eng Comput, 48:1251–1260.

Hundza, S. R., Hook, W. R., Harris, C. R., Mahajan, S. V.,

Leslie, P. A., Spani, C. A., Spalteholz, L. G., Birch,

B. J., Commandeur, D. T., and Livingston, N. J.

(2014). Accurate and reliable gait cycle detection in

parkinson’s disease. IEEE transactions on neural sys-

tems and rehabilitation engineering : a publication of

the IEEE Engineering in Medicine and Biology Soci-

ety, 22(1):127–137.

Latt, W. T., Veluvolu, K. C., and Ang, W. T. (2011).

Drift-free position estimation of periodic or quasi-

periodic motion using inertial sensors. Sensors

(Basel), 11(6):5931–5951.

Loose, H. and Orlowski, K. (2015a). Gait patterns in stan-

dard scenarios - using xsens mtw inertial measure-

ment units. 16th International Conference on Re-

search and Education in Mechatronics REM2015.

Loose, H. and Orlowski, K. (2015b). Model based determi-

nation of gait parameters using imu sensor data. Pro-

ceeding of Meachatronic Systems and Material (MSM

2015), 7-9 July 2015, Kaunas, Lithuania.

Evaluation of Gait Parameters Determined by InvestiGAIT against a Reference System

261

Magness, S. (2010). Understanding stride rate and stride

length. http://www.scienceofrunning.com/2010/11/-

speed-stride-length-x-stride-frequency.html, Online:

28.10.2015.

Mannini, A. and Sabatini, A. M. (2012). Gait phase de-

tection and discrimination between walking - jog-

ging activities using hidden markov models applied to

foot motion data from a gyroscope. Gait & Posture,

36:657–661.

Mercer, J. and Chona, C. (2015). Stride lengthvelocity

relationship during running with body weight sup-

port. Journal of Sport and Health Science (2015).

http://dx.doi.org/10.1016/j.jshs.2015.01.003.

Orlowski, K. and Loose, H. (2013). Evaluation of kinect

and shimmer sensors for detection of gait parame-

ters. Proceedings of BIOSIGNALS 2013, Int. Confer-

ence on Bio-Inspired Systems and Signal Processing,

Barcelona, Spain, 11-14 Feb. 2013, pages 157–162.

Orlowski, K. and Loose, H. (2014). Simple algorithms for

the determination or the walking distance based on

the acceleration sensor. Proceedings of BIOSIGNALS

2014, Int. Conference on Bio-Inspired Systems and

Signal Processing, Angers, France, 3-6 March 2014,

pages 264–269.

Pavetic, M. (2015). Streudiagramme und korrelation.

https://www.uni-due.de/imperia/md/content/-

soziologie/stein/uebungsskript deskriptiv-

statistik teil vi.pdf, Online: 28.10.2015.

Richards, J. G. (1999). The measurement of human motion:

A comparison of commercially available systems. Hu-

man Movement Science, 18:589–602.

Schwameder, H., Andress, M., Graf, E., and Strutzen-

berger, G. (2015). Validation of an imu-system (gait-

up) to identify gait parameters in normal and induced

limping walking conditions. International society of

biomechanics in sports.

Singleton, S., Keating, S., McDowell, S., Coolen, B., and

Wall, J. (1992). Predicting step time from step length

and velocity. Australian Physiotherapy, 38(1):43–46.

Thong, Y. K., Woolfson, M. S., Crowe, J. A., Hayes-Gill,

B. R., and Challis, R. E. (2002). Dependence of iner-

tial measurements of distance on accelerometer noise.

Meas. Sci. Technol., 13.

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

262