ChapterPDF Available
PART I
Capturing 3D Kinetic
Data
2
Accuracy of human motion capture sys-
tems for sport applications;
state-of-the-art review
Accurate kinematic data are essential for the biomechanical analyses in speed skating;
capturing kinematic data of a speed skater on an ice rink however, proved to be challenging
due to the large volume one skating stroke covers. This chapter presents a review on the
accuracy of body motion capture systems in sports application. The chapter consists of two
parts; the first part is a literature study on available kinematic measurement systems; the
second part contains guidelines for selecting a system for an experiment and on how to report
on its accuracy.
Motus inter corpora relativus tantum est
-Huygens-
E. van der Kruk & M.M. Reijne,
Accuracy of human motion capture systems for sport
applications; state-of-the-art review
(2017), accepted with revisions at European Journal of
Sport Sciences
PART I CHAPTER 2
Abstract
Objective: Sport research often requires human motion capture of an athlete. It can, however,
be labor-intensive and difficult to select the right system, while manufacturers report on
specifications which are determined in set-ups that largely differ from sport research in
terms of volume, environment and motion. The aim of this review is to assist researchers
in the selection of a suitable motion capture system for their experimental setup for sport
applications. An open online platform is initiated, to support (sport)researchers in the selection
of a system and to enable them to contribute and update the overview. Design: systematic
review; Method: Electronic searches in Scopus, Web of Science, and Google Scholar were
performed, and the reference lists of the screened articles were scrutinized to determine
human motion capture systems used in academically published studies on sport analysis.
Results: An overview of seventeen human motion capture systems is provided, reporting the
general specifications given by the manufacturer (weight and size of the sensors, maximum
capture volume, environmental feasibilities), and calibration specifications as determined
in peer-reviewed studies. The accuracy of each system is plotted against the measurement
range. Conclusion: The overview and chart can assist researchers in the selection of a suitable
measurement system. To increase the robustness of the database and to keep up with
technological developments, we encourage researchers to perform an accuracy test prior to
their experiment and to add to the chart and the system overview (online, open access).
1.Introduction
Sport research often requires human motion capture of an athlete. Human motion capture is
the process of recording human movement; in this review we specifically focus on recording
global position of the body(segments) of an athlete. It can be labor-intensive and difficult
to acquire information on the accuracy and practical usage of measurement systems.
Specifications reported by manufacturers are determined in conditions and set-ups that
diverge from the conditions in which sport research is performed; this can be attributed to
four characteristics of the sport research area.
First, sport research is performed in non-laboratory settings, at the field, rink or arena that
the sport is practiced on. Such an area outside the controlled laboratory environment brings
several challenges, namely different locations (e.g. indoor versus outdoor), weather conditions
(e.g. temperature and humidity), measurement interferences (e.g. noise, scattering or magnetic
disturbances), and obstacles in the area resulting in occlusion.
Second, strongly related to the first characteristic, the measurement (capture) volume is
often large (e.g. a ski-slope or a soccer field) (Figure 2.1). Typically, the accuracy is inversely
proportional to the coverage of a positioning system (i.e. a lower accuracy for a larger
measurement volume), which makes this generally the limiting factor in the selection of a
measurement system. When the displacement of the participants becomes larger, ergometers
are sometimes used to acquire a large number of movement cycles (e.g. treadmill, ergo cycle,
or rowing-kayaking ergometers) (Begon, Colloud, Fohanno, Bahuaud, & Monnet, 2009).
However, this is not always desirable, because movements on an ergometer might differ from
the actual motion, or simply because there is no ergometer to replicate the motion on.
Third, research for sport analysis often deals with highly dynamic motions which are more
difficult to capture than static or slow movements (e.g. gait analysis). High sample frequencies
are a necessity in this case. For sport applications, typical sample frequencies are between
50-250 Hz (Table 2.2). It has the preference to prevent using too high sample frequencies to
avoid excessive amounts of data and to avoid high frequency noise. Only in specific cases very
high frequencies (>1000 Hz) are necessary, e.g. to study impact (such as jumping) or very high
velocity movements (such as baseball pitching). Moreover, the system has to deal with motion
dynamics, which, for instance, proves to be problematic in IMUs (inertial measurement units),
where linear accelerations disturb gravity-based algorithms.
Fourth, the size and weight of the sensors are of importance when a measurement system
Accuracy of human motion capture systems
requires placement of sensors, markers, transponders, or tags directly on an athlete. Especially
in high performance and high dynamic conditions, an athlete should be minimally hindered
in her freedom of actions.
The aim of this paper is to assist researchers in the selection of a suitable motion capture
system for their experimental setup for sport applications. For this purpose, a literature review
was conducted on the available human motion capture systems used in peer-reviewed papers
on sport analyses. This paper provides an overview of the found measurement systems and
their specifications given by the manufacturer (weight and size of the sensors, maximum
capture volume, environmental feasibilities), and reports the instrumental errors (accuracy) as
determined in the peer-reviewed studies. Furthermore, the working principles of each of the
systems are explained, as these determine the system limitations and characteristics. . Data
processing, such as body pose reconstruction methods and filtering, falls outside of the scope
of this survey. These results are made available via an open online platform, to enable (sport)
researchers to contribute and update to the overview on measurement systems.
2. Method
We carried out a literature search between October 2012 and January 2013 and between
December 2016 and February 2017. Both searches were performed in the databases of Scopus,
Web of Science and Google Scholar using combinations of the keywords of the following three
groups. Group 1: measure, analyze, system; Group 2: kinematic, motion, force, coordinate,
rotation, orientation, location, position, velocity, speed, acceleration; Group 3: sport, skating,
cycling, football, track, field, running, tennis, swimming, hockey, baseball, basketball, skiing
and rowing. The search was limited to papers in the English language and published in peer-
reviewed journals or conference proceedings. Additional literature was obtained through the
reference lists of selected papers.
The abstracts of the retrieved papers were read to verify whether a human motion capture
system was used in the work. We focused on papers sportsthat use measurement systems
in a sport experimental setting. If this was not the case, the paper was excluded from further
investigation. The remaining papers were read to obtain information about the accuracy of the
measurement system and the context for which this accuracy was determined (environmental
conditions, test set-up, type of motion and error definition). If the paper did not include an
accuracy evaluation in the experimental context, we tried to retrieve this information from
studies referenced by the paper. This information was then included, although not always
determined in a sport context, and therefore marked in the results section. If no peer-reviewed
papers were found on the accuracy, the paper and system were left out of further evaluation.
The accuracy of a system was set to be the 95th percentile (P95) of the measurement error:
P
95
2=+
µσ
(2.1)
In which μ is the reported mean (RMSE was used in case of absence of mean), and σ is
the reported standard deviation. The range of a system was set to be the area (m2) (global
horizontal plane) of the measurement volume. We choose range instead of volume to obtain
a general variable for both 2D and 3D systems.
3. Results
The literature study resulted in a total of twenty peer-reviewed studies on measurement
accuracy, discussing 17 different human motion capture systems. The systems are listed in
Table 2.1. This table provides the general specifications of the systems regarding environmental
capabilities, weight, size and maximum volume as reported by the manufacturers. Table
2.2 lists the same systems with the corresponding published studies and the accuracy
PART I CHAPTER 2
system
2D/3D
real-time
indoor
outdoor
position
velocity
acceleration
orientation
dimensions
weight
active
passive
markerless
sensor
marker
tag
capture volume
(1 camera)
cameras
markers
sensors
tags
sampling
frequency
Optoelectronic (OMS)
Optotrak 3020 3D yes x - x - - x < 10 gr x - - -
infrared
LED
-3.6x2.6x3.7 m 8512 - - 3500 /(numbe r of markers + 1) Hz
Vicon 460
(datastation)
3D yes x
x
(*not broad daylight)
x - - x 3 - 25 mm < 10 gr - x - - reflective - (depending on lens) 6 - - 2000 fps
Vicon T-40 3D no x
(*underwater)
x
(*not broad daylight) x - - x 3 - 25 mm < 10 gr - x - - reflective - (depending on lens) 10 - - 2000 fps
Vicon MX 13 &
MX40 (cameras) 3D no xx
(*not broad daylight) x - - x 3 - 25 mm < 10 gr - x - - reflective - (depending on lens) >24 - - 2000 fps
iGPS 3D yes x x x - - x
several sizes,
starting at 80x20x20
mm
> 30 gr x - - probe - - 55 m (circle) - - 50 Hz
Electromagnetic (EMS)
WASP 2D yes x x x - - - 90x50x25 mm ? x - - x - x - - - 125 Hz
LPM 2D - x x x - - - 9.2 x 5.7 x 1. 5 cm 60 g x - - x - x - - - 1000 Hz
RFID carpet 2D x - x - - x 8.5 x 5.5 c m - - x - x - x - - - dependent on reader
RTK GNSS
(Javad Alpha-G3T)
3D no - x x - - - 148x85x35 mm 430 g x - - x - - - - - 50 Hz
Ubisense, Series 700 IP 3D yes x x(?) x - - - 40x40x10 mm x - - x - x - - - 10 Hz
Image processing (IMS)
panning camera,
custom tracking algorithm
3D no x - x - - - - - - - x - - - camera dependent 1 - - - ?
color cameras combined with
custom tracking algorithm 3D no x - x - - x - - - - x - - - camera dependent 1 - - - 200 fps
Kinect 3D yes x - - - - x - - - - x - - - 1.8 x 2 x 2. 8 m 1 - - - 30 fps
LaBacs 3D x - x - - x x - - -LED -camera dependent 212 - - 100 Hz
Ultrasonic (UMS)
WSN 3D no x x x - - - x - - - - x - - - -
Inertial (InMS)
Fusion (FMS)
GPS, single frequency (u-blox
AEK4) + MEMS IMU (Xsens Mti) 3D no - x x x x x
50x56x32 mm (IMU) +
12.2x16x2.4 mm (u-
blox)
75 - - - - - - - - - - GPS: 1 Hz,
MEMS-IMU (10kHz)
Rolling Motion Capture syste m:
SimiMotion 7.0
(Basler A602f)
3D no X X X X X X 3 - 25 mm < 10 gr - X - - reflective - system rolls along depending on system 30 Hz
maximum
environment
measure
marker
Table 2.1 General Table : specifications of the manufacturers on the measurement systems. Given are the weight and size of the sensors and system, the type
of sensor, and the maximum capture volume, number of markers, and sample frequency. The maximum capture volume is given for one camera or sensor; if a
system is not restricted by the limitations of the number of sensors, this is indicated by ‘ ‘ . * indicates that the system was used in sport applications, but the
accuracy was determined in a different context (found via reference list of paper).
Accuracy of human motion capture systems
specifications. The accuracy specifications include the number of cameras, number of markers,
sample frequency, reference system, motion, statistical value, measurement volume or range,
and the reported accuracy. These results are processed in the online, interactive selection
tool. In Figure 2.2, the accuracies are plotted against the range of the experimental setup. As
expected, the accuracy of the systems (eq. 2.1) is inversely proportional to the coverage of a
positioning system; in other words, a lower accuracy for a larger measurement volume.
The specifications in terms of the practical and technological difficulties associated with the
types of measurement systems are highly dependent on their physical working principles. In
human motion capture we distinguished five working principles: optoelectronic measurement
systems (OMS), electromagnetic measurement systems (EMS), image processing systems
(IMS), ultrasonic localization systems (UMS), and inertial sensory systems (IMU) (van der Kruk,
2013b). Arranged by these working principles, the measurement systems are explained in the
next sections. The general pros and cons of each of the working principles are summarized in
Figure 2.1.
Figure 2.1 Sport categories with the most plausible measurement system categories. A division is made
between team sports (more than three players), and individual sports. Team sports primarily involve
large measurement volumes and occlusions. Since team sports are mainly concerned with tracking, the
accuracy is less important than for individual sports, where technique factors are commonly analysed.
The individual sports are apart from indoor vs outdoor, also divided into larger and smaller volume
sports. Smaller volumes are covered by the highly accurate optoelectronic measurement systems. The
individual sports in larger volumes are currently the most critical in terms of measuring kinematics. The
most suitable options are IMS and IMU (fusion) systems. Gymnastics HB = High Bar, Gymnastics F =
Floor, Track and Field R = Rink, Track and Field D = Discus;
PART I CHAPTER 2
study
system
cameras
markers
samplingfreq
reference
movement
statisticalvalue
range/volume/area
[m]
reportedaccuracy
comments
Range(m^2)
Accuracy(m)
Optoelectronic(OMS)
Maletskyetal.(2006)*9Optotrak3020 3
(oneunit)
2x6
(oneachRB) 30Hz
machinist'srotarytable(resolution
0.005)
andlinearslideandasensor
(reportedresolution0.006mm)
staticrelativeposition
betweentworigidbodies meandifference(SD)range:1.75,2.50,
3.25,4.00,4.75m
Translation,inplane0.036(0.109)
mm,
outofplane0.017(0.108)mm.
Orientation,inplane0.119(0.508)
deg,outofplane0.070(0.591)deg
accuracyisofalldata,
24independenttrialsateachdistance 13.5 0.000297
Windolfetal.(2008)*12 Vicon460 4 4(diameter
25mm) 120Hz servomotordrivensliding
carriage(reportedaccuracy15μm) smalltranslationalmovements RMSE(SD) 0.18x0.18x0.15m 0.063(0.005)mm 0.0324 0.000073
Monnetetal.(2014)2ViconT40 8 10 200Hz rigidbar frontcrawlswimming
(underwater) RMSE 1.1x1x1m6.5mmunderwater,
0.77mminair onlyRMSE 1.1 0.00077
Sporrietal.(2016)10ViconMX13&MX40 24
51(Plugingait
markerset+skisand
poles);3onmagic
wand
250Hz directcalipermeasurements
ofrigidboot&magicwand alpineskiing meandifference(SD) 41.2x20m
wand:0.6(0.4)mm
fixedboot:2.3(2.2)mm
(at24.5km/h)
824 0.0067
VanderKruk201319 iGPS(Nikon) 8 2 30Hz CalibrationFrame Cyclingonicerink(25km/h) mean(SD) 70mx180m 3.0(1.7)mm manygapsinthe
dataindynamicmeasurements 12600 0.0064
Electromagnetic(EMS)
Hedley,etal.(2010)27 WASP 12 2 10Hz distancebetweentwotags
attachedtoapieceofwood quicklywalkingaround SD 28x15m 0.24m indoorBasketballfield,onlySD 420 0.48
Sathyanetal.(2012)29 WASP 12 3 10Hz
relativepositionbetween
twotagsfixedtoarulerattachedto
theupperbackofeachparticipant
runandsprint,straight
andagilitytestcourse(length
approx28m)
Cumulativedensity
function 28x15m
indoor,linear:0.7m,
indoor,nonlinear:0.3m,outdoor,
linear:0.25m,outdoornonlinear:
0.25
accuraciesareread
fromprobabilitydensitygraphat95%420 0.7
Ogrisetal.(2012)3LPM 12 1 45.45Hz Vicon(8cameras,24x26.5m)
(reportedaccuracyof0.9mm)
smallsidesoccergame
(2x2,2x3or3x3) RMSE 80x48m 0.234m(at23km/h) onlyRMSE 3840 0.234
Shirehjinietal.(2012)*30 RFIDcarpet 4 585 unknown tenstaticpositions
withdifferentorientations meanerror(SD) 3x1.8mpostion:6.5(5.4)cm,
orientation:0.96(4.9)deg 5.4 0.173
Rhodesetal.(2014)32 Ubisense,
Series700IP 63
sensors:137
Hz,
tags:16Hz
position:lasertotalstation
(LeicaTS30,reportedaccuracy0.004
m),velocity:wirelesstiminggates
(BrowserTimingSystems)
position:static,velocity:
maximumsprintand
multidirectional(wheelchair
rugby)inindoorsportshall
equippedwithwoodensprung
flooring
meanerror(SD) 28x15m
position:0.19m,velocitysprint:
4.00(0.009)m/s,velocity
multidirectionalmovements:2.07
(0.13m/s)
systemfocussesmoreon
measuringdistanceandvelocityinstead
ofposition.
420 0.19
Perratetal.(2015)31 Ubisense,
Series700IP 59
tags:(3x16Hz,
3x8Hzand3x
4Hz)
LeicaTS30(reportedaccuracy3mm) practicewheelchairrugbymatch meanerror(SD) 28x15m 0.37(0.24)m 420 0.1776
Gilgienetal.(2014)4RTKGNSS
(JavadAlphaG3T) 350Hz
GPS+Gionassdualfrequency
atcircularelevationangleof10deg
(reportedaccuracy0.075(0.025)m,
basedonphotogrammetricreference
s
y
stem
)
alpineskiinggiantslalom (seebelow)
300x50m
(estimatedfrom
figure)
(seebelow) notedaccuracyat30deg
circularelevationangle
GPS+GIONASS,
bothfrequencies meanerror(SD) 0.02(0.01)m 15000 0.04
GPS+GIONASS,
singlefrequency meanerror(SD) 0.69(2.22)m 15000 3.0636
GPS,
bothfrequencies meanerror(SD) 0.47(1.35)m 15000 3.17
GPS,
singlefrequency meanerror(SD) 0.70(1.67)m 15000 4.04
Imageprocessing(IMS)
Table 2.2 Accuracy Table: measurement systems and their accuracy in a certain range, as reported in peer-reviewed articles (column 2). The specifications of the
experiment set-up are given in column 3-7. The last two columns (12-13) report the range and accuracy that are adopted in the chart of figure 1; chosen for this
purpose was the maximum reported range (column 9), with the accuracy at 95% confidence interval (P95) (column 10). If the reported statistical values (column
8)) did not permit the estimation of the P95 , this is indicated as a comment in column 11. Note that the maximum range in the peer-reviewed articles is not the
maximum capture volumes of a system (for this see the general table (table 1)).
Accuracy of human motion capture systems
study
system
cameras
markers
samplingfreq
reference
movement
statisticalvalue
range/volume/area
[m]
reportedaccuracy
comments
Range(m^2)
Accuracy(m)
Optoelectronic(OMS)
Maletskyetal.(2006)*9Optotrak3020 3
(oneunit)
2x6
(oneachRB) 30Hz
machinist'srotarytable(resolution
0.005)
andlinearslideandasensor
(reportedresolution0.006mm)
staticrelativeposition
betweentworigidbodies meandifference(SD)range:1.75,2.50,
3.25,4.00,4.75m
Translation,inplane0.036(0.109)
mm,
outofplane0.017(0.108)mm.
Orientation,inplane0.119(0.508)
deg,outofplane0.070(0.591)deg
accuracyisofalldata,
24independenttrialsateachdistance 13.5 0.000297
Windolfetal.(2008)*12 Vicon460 4 4(diameter
25mm) 120Hz servomotordrivensliding
carriage(reportedaccuracy15μm) smalltranslationalmovements RMSE(SD) 0.18x0.18x0.15m 0.063(0.005)mm 0.0324 0.000073
Monnetetal.(2014)2ViconT40 8 10 200Hz rigidbar frontcrawlswimming
(underwater) RMSE 1.1x1x1m6.5mmunderwater,
0.77mminair onlyRMSE 1.1 0.00077
Sporrietal.(2016)10ViconMX13&MX40 24
51(Plugingait
markerset+skisand
poles);3onmagic
wand
250Hz directcalipermeasurements
ofrigidboot&magicwand alpineskiing meandifference(SD) 41.2x20m
wand:0.6(0.4)mm
fixedboot:2.3(2.2)mm
(at24.5km/h)
824 0.0067
VanderKruk201319 iGPS(Nikon) 8 2 30Hz CalibrationFrame Cyclingonicerink(25km/h) mean(SD) 70mx180m 3.0(1.7)mm manygapsinthe
dataindynamicmeasurements 12600 0.0064
Electromagnetic(EMS)
Hedley,etal.(2010)27 WASP 12 2 10Hz distancebetweentwotags
attachedtoapieceofwood quicklywalkingaround SD 28x15m 0.24m indoorBasketballfield,onlySD 420 0.48
Sathyanetal.(2012)29 WASP 12 3 10Hz
relativepositionbetween
twotagsfixedtoarulerattachedto
theupperbackofeachparticipant
runandsprint,straight
andagilitytestcourse(length
approx28m)
Cumulativedensity
function 28x15m
indoor,linear:0.7m,
indoor,nonlinear:0.3m,outdoor,
linear:0.25m,outdoornonlinear:
0.25
accuraciesareread
fromprobabilitydensitygraphat95%420 0.7
Ogrisetal.(2012)3LPM 12 1 45.45Hz Vicon(8cameras,24x26.5m)
(reportedaccuracyof0.9mm)
smallsidesoccergame
(2x2,2x3or3x3) RMSE 80x48m 0.234m(at23km/h) onlyRMSE 3840 0.234
Shirehjinietal.(2012)*30 RFIDcarpet 4 585 unknown tenstaticpositions
withdifferentorientations meanerror(SD) 3x1.8mpostion:6.5(5.4)cm,
orientation:0.96(4.9)deg 5.4 0.173
Rhodesetal.(2014)32 Ubisense,
Series700IP 63
sensors:137
Hz,
tags:16Hz
position:lasertotalstation
(LeicaTS30,reportedaccuracy0.004
m),velocity:wirelesstiminggates
(BrowserTimingSystems)
position:static,velocity:
maximumsprintand
multidirectional(wheelchair
rugby)inindoorsportshall
equippedwithwoodensprung
flooring
meanerror(SD) 28x15m
position:0.19m,velocitysprint:
4.00(0.009)m/s,velocity
multidirectionalmovements:2.07
(0.13m/s)
systemfocussesmoreon
measuringdistanceandvelocityinstead
ofposition.
420 0.19
Perratetal.(2015)31 Ubisense,
Series700IP 59
tags:(3x16Hz,
3x8Hzand3x
4Hz)
LeicaTS30(reportedaccuracy3mm) practicewheelchairrugbymatch meanerror(SD) 28x15m 0.37(0.24)m 420 0.1776
Gilgienetal.(2014)4RTKGNSS
(JavadAlphaG3T) 350Hz
GPS+Gionassdualfrequency
atcircularelevationangleof10deg
(reportedaccuracy0.075(0.025)m,
basedonphotogrammetricreference
s
y
stem
)
alpineskiinggiantslalom (seebelow)
300x50m
(estimatedfrom
figure)
(seebelow) notedaccuracyat30deg
circularelevationangle
GPS+GIONASS,
bothfrequencies meanerror(SD) 0.02(0.01)m 15000 0.04
GPS+GIONASS,
singlefrequency meanerror(SD) 0.69(2.22)m 15000 3.0636
GPS,
bothfrequencies meanerror(SD) 0.47(1.35)m 15000 3.17
GPS,
singlefrequency meanerror(SD) 0.70(1.67)m 15000 4.04
Imageprocessing(IMS)
Liuetal.(2009)40
panningcamera,
customtracking
algorithm
1 14 ͲstaticmarkersonshortͲtrackrink RMSE(SD) shortͲtrackrink
(45x18m)
xͲdirection:0.22m,
yͲdirection:0.19m
averageof14markers
(table4inRosenhahn(2006)) 810 0.22
Corazzaetal.(2009)8
colorcamerascombined
withcustomtracking
algorithm
4Ͳ60Hz
12cameraViconsystem
(120Hz),markerprotocolproposed
bymanufacturer
walking
meanabsoluteerror(SD) 79(11.5)mm datausedfrom
HumanEvadatabase 6 0.102
Corazzaetal.(2009)8
colorcamerascombined
withcustomtracking
algorithm
8Ͳ120Hz 8cameraQualysissystem(120Hz),
pointclustertechniqueprotocol walking
meanabsoluteerror(SD) 15(10)mm rangeestimatedbased
on'backwardsgymnasticflip' 6 0.035
Dutta(2012)*41 Kinect 1 Ͳ30Hz 7cameraViconsystem
(3MX3+4MX40cameras,100Hz)
104staticpositionsof
10cmlargecubes RMSE(SD)
range:1Ͳ3.6m,
volume:
prismfrom1.02Ͳ
3.06x2x0.71Ͳ2.13
xͲdirection:0.0169(0.0299)m,
yͲdirection:0.0348(0.0765)m,zͲ
direction:0.0141(0.0250)m
7.5 0.1878
Stancicetal.(2013)*14 LaBACS 2 2 100Hz
manipulatorarmwas
rotatedwithprecisionservomotor
controlledbyATMEGA2560
microcontroller
singledegreeoffreedom
rotationofrigidbodywithfive
incrementsoflinearvelocityfrom
0.5to2.5m/s.
meandifference notmentioned 0.23Ͳ1.1mm
(lowestͲhighestvelocity) 3 0.0011
Klousetal.(2010)39 panning,tilting
andzoomingcamera 5
25mmsquared
markers(approx100
markers)
50Hz fixedmeasureddistance
ofmarkersonskipole
5skiͲtrialsand2snowboard
trials,slopeinclinationwas
uniformly21deg.Oneturn.
averagedifference(SD) 35x15m 31(3.3)mm 2500 0.0376
Ultrasonic(UMS)
Bischoffetal.(2012)*46 WSN
2
transmitte
rs,
5
receivers
ͲͲ8staticpositions RMSE(SD) 3x3m 4.21(0.57)cm fusion
Ultrasound+radiofrequency 9 0.0535
Fusion(FMS)
Waegli&Skaloud(2009)5
GPS,singlefrequency
(uͲbloxAEK4)+MEMS
IMU(XsensMti)
GPS+1
MEMS
IMU
Ͳ
GPS:1Hz,
MEMSͲIMU
(100Hz)
GPS+GLONASS,
dualfrequency(Javad)andtactical
gradeIMU(LN200),reportedaccuracy
5cm(position),2cm/s(velocity),0.01
deg(roll,pitch)and0.03deg
(heading).
sixdownhillskiingrunsof
approximately1minlength
performedbyaprofessionalskier
RMSE 400*450 postion:0.65m,velocity:
0.15m/s,orientation:1.6deg
accuracyreadfromgraph,
accuracydeterminedbymanufacturer
software,reducedthenumberof
satellitesonpurposetoevaluate
performancewithIMUfusion
180000 0.65
Kerstingetal.200816
RollingMotionCapture
system:SimiMotion7.0
(BaslerA602f)attached
tomovingframe
3 51 30Hz 8Ͳcameramotioncapturesystem
ViconMX
Rowing,nineelitelevelathletesin
variousboatcategorieswere
analysedduringtrainingandrace
pace
meanjointcentre 4.5*3*2.5m 0.03m
exactverificationprocessisunclear,
priorworkshowsresearchsetͲupfor
gait(Begonetal.2009)
33.75 0.03
PART I CHAPTER 2
Figure 2.2 A) Chart on range versus accuracy as reported in peer-reviewed papers (see Table 2). Indicated are the ranges of several common sport fields. Note that the reported ranges
are not the maximal ranges of a measurement systems (for this see Table 1)). B) Selection procedure for a suited measurement system. The graph can be divided into four quadrants,
which are defined by the minimal measurement range and the minimal accuracy requirement of the research set-up in question. The upper left area now contains the systems that have
a small range and low accuracy, we will refer to them as not applicable (NA). The lower right area contains the systems that satisfy the specific requirements of both range and accura-
cy, we will refer to those as applicable (A). The lower left area is the area with measurement systems that meet the accuracy requirement, but do not have the required range; we refer
to those systems as low in range (LR). The upper right area is the area with systems that meet the range requirement, but lack the right accuracy, referred to as low in accuracy (LA). It
might be the case that there are no systems in the chart that meet the volume-accuracy requirements (A). Then it might be possible to combine systems from the LR or LA quadrant,
via sensor integration (James B. Lee, Ohgi, & James, 2012). Data from different measurement systems are then combined to determine one variable. A fusion motion capture system
requires a fusion algorithm to combine the data of both measurement units (e.g. a Kalman filter or Comparative filter).
Accuracy of human motion capture systems
3.1 Optoelectronic measurement systems
The optoelectronic measurement systems (OMS) are more accurate than the other systems
(see Figure 2.1). Not surprisingly, the optical systems (e.g. Optotrak or Vicon) are in literature
often regarded as the gold standard in motion capture (Corazza, Mündermann, Gambaretto,
Ferrigno, & Andriacchi, 2010). An OMS detects light and uses this detection to estimate the 3D
position of a marker via time-of-flight triangulation. Accuracy of the systems is dependent on
the following parts of the experimental set-up: the locations of the cameras relative to each
other, the distance between the cameras and the markers, the position, number, and type of
the markers in the field, and the motion of the markers within the capture volume (Maletsky,
Sun, & Morton, 2007). Also, there is a trade-off between camera resolution and sample
frequency.
OMS are based on fixed cameras and can therefore acquire data only in a restricted area (Begon
et al., 2009). The capture volume is dependent on the maximum number of cameras and the
field of view of each camera. The largest measured range with OMS is 824 m2, described in
Spörri et al. (2016), obtained with a Vicon MX13 measurement system (Spörri, Schiefermüller,
& Müller, 2016). For this range, 24 cameras were required. This number of cameras results in
significant practical difficulties regarding cost, portability, calibration, synchronization, labor,
and set-up. Further limitations of the system are the necessity of a line-of-sight, which means
that the data output will be interrupted when the cameras lose sight of the markers (Panjkota,
Stancic, & Supuk, 2009; Spörri et al., 2016). Furthermore, the systems are highly sensitive
to alterations in the setup, e.g. due to accidental shifting of a camera (Windolf, Götzen, &
Morlock, 2008). The systems are mostly used in dark areas (indoors), because bright sunlight
interferes with the measurements (Spörri et al., 2016).
There are two categories within the optoelectronic systems: active marker systems and passive
marker systems. Passive systems use markers that reflect light back to the sensor. The Vicon
systems (460, T-40, MX13 and MX40) in the chart (Figure 2.1) are examples of passive motion
capture systems. Active systems utilize markers that contain the source of light for the sensors
(often infrared) (Richards, 1999). In the chart, Optotrak 3020 is an active marker optical
system. The benefit of active markers over passive ones is that the measurements are more
robust. However, active markers do require additional cables and batteries, so the freedom
of movement is more limited (Stancic, Supuk, & Panjkota, 2013). In addition, the maximum
sample frequency is lowered when multiple markers are used as the signal of each individual
marker needs to have distinguishable frequency by which it can be identified.
A rather original way of increasing the range of a marker-based optoelectronic measurement
system is the rolling motion capture system (Begon et al., 2009; Colloud, Chèze, André, &
Bahuaud, 2008). With this method, cameras are placed on a fixed moving frame, to meet the
requirement of fixed relative positions between the cameras. The method was applied in a
3D kinematic analysis of rowing, with a three-camera-recording-system mounted on a boat,
which stayed next to the rowers (Kersting, Kurpiers, Darlow, & Nolte, 2008); this study showed
an accuracy of about 30 mm in mean joint centres. Kersting et al. concluded, however, that
the method is very time consuming - mainly due to calibration- and not suitable for general
training purposes.
Indoor GPS (iGPS) is a OMS that is not based on markers, but on receivers that are attached
to the tracked object or participant (Nikon, 2017). In contrast to what the name may indicate,
the (physical) working principle is entirely different from a regular GPS system: the system
has a transmitter which uses laser and infrared light to transmit position information from the
transmitter to the receiver (Nikon, 2017). This is a one-way procedure. The advantage of this
system is that there is practically no limit to the scalability of the system. Therefore it is possible
to add as many transmitters as needed to cover a (factory) wide area and an unlimited number
of receivers can be used (Khoury & Kamat, 2009). The accuracy of the system, determined on
an indoor ice rink (12600 m2), was 6.4 mm (van der Kruk, 2013a). Important drawbacks for the
application of this system in sport, are the size and weight of the receivers that need to be
PART I CHAPTER 2
attached to the athlete (see Table 2.1).
3.2 Electromagnetic measurement systems
Electromagnetic systems (EMS) find the unknown positions of the measurement transponders
by means of time-of-flight of the electromagnetic waves - radio waves - travelling from the
transponder to the base stations (Stelzer, 2004). EMS provide large capture volumes (see
Figure 2.1), but are less accurate than OMS: each EMS in the chart has a lower accuracy than
the worst performing optoelectronic system. Unlike an OMS, no line-of-sight is necessary to
find the positions of the transponders; also the human body is transparent for the field applied
(Schepers & Veltink, 2010). Limitations of the system are the sensitivity for ferromagnetic
material in the environment, which decrease the accuracy of the data (Day, Dumas, &
Murdoch, 1998); moreover, when the distance between the base station and the transponder
is increased, noise increases and the quality of the signal decreases (Day et al., 1998; Schuler,
Bey, Shearn, & Butler, 2005). Furthermore, EMS often have a low sample frequency, which, as
discussed in the introduction, is a drawback for sport analysis. The frequencies are lowered
when using multiple markers.
Of the EMS systems, the GPS-GLONASS dual frequency system (one of the GNSS systems)
shows a promising range-accuracy combination: 0.04m accuracy in a range of 15000 m2.
GNSS are satellite navigation systems of which GPS, GLONASS and GALILEO are examples.
Satellites transmit data containing information on the location of the satellite and the global
time. Since all satellites have a different position, the time it takes for the data to reach the
receiver is different, which gives the option of determining the distance of the satellites. If
the receiver gets the information from four satellites, the position in 3D can be estimated,
although height information is determined 2 to 3 times worse than horizontal displacement
(Berber, Ustun, & Yetkin, 2012). Note that in the graph, all GNSS systems are differential GNSS
systems, which have an additional GNSS receiver as static base station within 5 km of the test
site. The measurement of the satellite signals of the base station can be combined with the
measurements of the mobile GNSS to increase accuracy.
Drawback of GNSS systems are the limitations associated with the cost, weight, and dimensions
of the GNSS receivers and antenna. The GNSS system cannot be used indoors and is also
sensitive to occlusions and the weather outside (a clear sky is necessary). The accuracy of a
GNSS system is dependent on its specifications; for example, (low cost) single frequency GNSS
units are of substantially lower accuracy (up to 4 m) than high cost dual frequency units (up
to 0.04 m), especially under poor conditions (Duffield, Reid, Baker, & Spratford, 2010; Tan,
Wilson, & Lowe, 2008). The high-end dual frequency units are however more bulky.
Contrary to GNSS, all other EMS systems can be used indoors, since they utilize local base
stations instead of satellite signals. LPM (Local Position Measurement) consists of base
stations, positioned throughout the area, and transponders, worn by the subjects. The main
base station first sends a trigger to each transponder, whereupon each transmitter sends
tagged electromagnetic waves to all other base stations. The same as for GNSS, at least four
base stations need to receive a signal to determine the 3D position of the transponder via
time-of-flight. The system functions both indoors and outdoors. The accuracy of the system
presented in the chart is 0.23 m for a dynamic situation (23 km/h) in an area of 3840 m2.
Comparable to the working principle of LPM, but less accurate, is the WASP system (Wireless
Ad-hoc System for Positioning); WASP uses tags and anchor nodes, placed at fixed positions,
to track participants in 2D. The accuracy that can be achieved is dependent upon the venue,
varying from 0.25m in indoor sporting venues to a couple of meters when operating through
multiple walls (Hedley et al., 2010). In sport studies, accuracies between 0.48-0.7 m were
found at an indoor basketball field (420 m2) (Hedley, Sathyan, & MacKintosh, 2011; Sathyan,
Shuttleworth, Hedley, & Davids, 2012). The accuracy is also limited by the bandwidth of the
transmitted radio signal.
RFID is a wireless non-contact system which uses electromagnetic waves and electromagnetic
Accuracy of human motion capture systems
fields to transfer data from a tag attached to an object, to the RFID reader. There are two sort
of tags: active tags, which actively emit radio waves, and passive tags, which can be read only
over short ranges since they are powered and read via magnetic fields (induction). Passive
tags practically have no lifetime, since they do not require any power from batteries (Shirehjini,
Yassine, & Shirmohammadi, 2012). The RFID carpet of Shirehjini et al. (2012) consists of
passive tags and reported accuracies of 0.17 m in a 5.4 m2 area(Shirehjini et al., 2012). Ubisense
is a commercially available system, originally designed for enterprises to track assets and
personnel, that uses the active RFID technology. In sports, the system was tested at an indoor
basketball field (420 m2), reporting an accuracy of 0.19 m (Perrat, Smith, Mason, Rhodes, &
Goosey-Tolfrey, 2015; Rhodes, Mason, Perrat, Smith, & Goosey-Tolfrey, 2014).
Factors such as attenuation, cross paths of signals and interference from other RFID tags, RFID
readers, and different Radio Frequency devices can affect the communication between the
tags and RFID readers (Ting, Kwok, Tsang, & Ho, 2011).
3.3 Image Processing systems
Image processing systems (IMS) generally have better accuracy compared to the EMS, and an
improved range when compared to the OMS. In image processing captured films or photos
are digitally analyzed. Oppositely to the other measurement methods which are sensor-
based, this method is vision-based, using optical cameras and computer vision algorithms.
This marker-less tracking can be a big advantage in sports, such as for event-detection (Zhong
& Chang, 2004). Image processing also has some drawbacks: it is not easy to perform image
recognition in real-time, it might require expensive high quality and/or high speed cameras.
The accuracy is also dependent on the experimental set-up, namely the position of the camera
in relation to the object trajectory, and the number of cameras. (Lluna, Santiago, Defez, Dunai,
& Peris-Fajarnes, 2011). Furthermore, generally, an increase in camera resolution results in a
decrease in feasible maximum sampling frequencies.
Vision based systems can be divided into two categories: Model-based tracking and feature-
based tracking. Model-based tracking uses a 3D model of the tracked object. In the basic
concept of the model-based tracking, the pose information is updated in each video frame,
first by using a dynamic model via a prediction filter and then by measurements in the video
frame . A drawback of model-based tracking systems is that they are hard to use in unknown
environments and restrict camera motion, due to the necessity of additional information such
as 3D models of participants and environment (Bader, 2011; Ceseracciu et al., 2011).
Feature-based tracking algorithms use interest points in the frames to track the object. There
are two kind of feature-based tracking algorithms: marker tracking, which uses known-markers,
and marker-less tracking, which focuses on tracking 2D features such as corners, edges or
texture (Akman, 2012). Note that the marker tracking in IMS differs from OMS, because IMS
uses (for humans) visible light, whereas OMS works with infrared light.
For marker tracking, known-markers are used to track the object. This is usually more accurate
than to detect natural features (e.g. existing corners or edges), however the markers must be
put precisely in place before the experiment (grid set-up) and occlusion of markers may occur.
In sports, marker-based feature tracking has been applied in the collection of kinematic data
on a ski and snowboard track, where an accuracy of 0.04m was obtained in a 2500 m2 range
(Klous, Müller, & Schwameder, 2010).
Marker-less tracking eliminates the dependency on prior knowledge about the environment
and extents the operation range. This natural tracking is a hot topic in, for instance, robot vision
and augmented reality. However, in those applications, the cameras are actually attached to
the object that is being tracked, in contrast to the sports application, where, up-to-know, the
camera is static, while panning, tilting, and/or zooming (Liu, Tang, Cheng, Huang, & Liu, 2009).
Liu et al. (2009) mounted a panning camera to the ceiling to track short-track speed skaters
during a match, using a color-histogram of the skaters; they obtained an accuracy of 0.23 m
(area 810 m2).
PART I CHAPTER 2
The KinectTM sensor – which was originally designed to allow users to interact with a gaming
system without the need of a traditional handheld controller – can also be classified as a
marker-less tracking device, although the working principle is slightly different from what was
previously described. The system projects an infrared laser speckle pattern onto the viewing
area of the infrared camera. This infrared camera detects the pattern and enables the creation
of a 3-D map by measuring deformations in the reference speckle pattern. Due to its low-costs
and reasonable accuracy (0.19 m at 7.5 m2 (Dutta, 2012)), the device is often used in scientific
research (Bonnechere et al., 2014; Choppin, Lane, & Wheat, 2014; Dutta, 2012). The drawback
of the Kinect camera is the small field of view; furthermore, the system struggles with the
detection of dark surfaces that absorb light, shiny surfaces that result in specular reflection
and rough surfaces if the angle of incidence of incoming light is too large (Dutta, 2012).
At present, available computer-vision-based measurement systems are outperformed by
either optoelectronic or electromagnetic measurement systems and their maximal range is
small. Although no mature system exists at the present (July 2017), a large number of open
source codes are available and progress is rapid (Scaramuzza & Fraundorfer, 2011). Open-
source databases with human kinematic data are provided to enable developers to verify their
algorithms (HumanEva, 2017). This not only enables the verification of the developed systems,
but also eases the comparison between systems for researchers developing their study setup.
3.4 Ultrasonic localization systems
Ultrasonic localization systems (UMS) are most commonly used in short-range measurements.
UMS determine the position of an object by means of Time-of-Flight of an ultrasound wave
travelling through the air. These systems are also called acoustic measurement systems,
because the system functions by means of sound waves. The difference between sound and
ultrasound is that ultrasound is stealthy for the human ear. This is, of course, beneficial in
research. A drawback of ultrasound is that the range is limited compared to sound. Also, the
directionality of ultrasound can be a disadvantage when working with dynamic measurements.
In the chart (Figure 2.2), one system is included, which is based on ultrasonic localization in
sports, with an accuracy of 0.05 m in an area of 9 m2 (Bischoff, Heidmann, Rust, & Paul, 2012).
Note, however, that this result was obtained via a fusion with a radio frequency transceiver.
3.5 Inertial sensor measurement systems
An inertial measurement unit (IMU) is a device consisting of an accelerometer, gyroscope, and
often a magnetometer. By combining the information from the accelerometer – gravitational
force – with the data from the gyroscope – rotational velocity -, the orientation of the device
can be determined (M Brodie, Walmsley, & Page, 2008). The magnetometer is used to track
the magnetic-north, to determine the heading of the IMU. There are many commercially
available IMUs on the market.
As stand-alone system, the device cannot determine its (global) position, and is therefore not
added to the chart. In principle, the accelerometer could be used to determine position by
performing a double integration; however, the data will suffer from large integration drifts.
When, however, the systems are placed on body segments, the orientation of these segments
are determined, and the position in global space can be estimated with a rigid-body model
of a human (Neuron, 2017; Xsens, 2017). IMUs do not have a base station and are therefore
the most mobile of all available measurement systems. Additionally, the system is capable
of detecting very rapid motion (Zohlandt, Walk, & Nawara, 2012) and is non-invasive for the
user, which makes it an attractive system in sports (e.g. gymnastics (Zohlandt et al., 2012),
swimming (James Bruce Lee, Burkett, Thiel, & James, 2011)). A drawback is that the system is
susceptible to measurement errors due to nearby metal (experimental set-up). Moreover, the
gravity-based algorithms are sensitive to linear acceleration.
IMU systems cannot be used for global position measurements as a stand-alone system (only
orientation accuracy (M Brodie et al., 2008)); however, the systems do appear in the table as
Accuracy of human motion capture systems
fusion motion capture systems (more on this in the next section).
4. Discussion & Conclusion: system selection
Choosing the right motion capture system for sport experiments can be difficult. Figure 2.2
is designed to support researchers in this choice. The selection procedure is explained in the
caption of Figure 2.2, and also available online via an interactive selection tool.
Based on the results of this survey, we defined some broad sport categories, which require
roughly the same characteristics in a measurement system (Figure 2.1). A division is made
between team sports and individual sports. In team sports, systems are typically used
for position, distance, velocity, and acceleration tracking of players, whereas individual
sports usually involve some sort of technique analysis. Team sports primarily involve large
measurement volumes, and occlusions are common. Accuracy is for these tracking applications
not as important as for technique analysis. Therefore, EMS are the most suitable. The
individual sports are apart from indoor versus outdoor, also divided into larger and smaller
volume sports. Individual sports typically require higher accuracies. Smaller volumes can be
covered by the highly accurate OMS. Individual sports in larger volumes are currently the most
critical in terms of measuring kinematics. The most suitable options are IMS and IMU (fusion)
systems, however these measurement categories often require development of a suitable
algorithm (either for tracking in case of IMS, or fusion filtering in case of IMU). Therefore,
overall we can conclude that there is a gap in measurement system supply for capturing
large volumes at high accuracy (Figure 2.2). These specifications are mainly necessary for large
volume individual sports, both indoor (among others swimming, speed skating, gymnastics),
and outdoor (among others rowing, tennis, track and field).
The (online) selection tool enables researchers to make a faster and better informed selection
for a measurement system suited to their experimental setup. Instrumental errors are
dependent on the context of the study (section 2.2). Therefore, we encourage researchers to
always perform and report a calibration procedure prior to their experiment (system, number
of cameras, markers, sampling frequency, calibration procedure, statistical value (e.g. SD,
mean, RMSE), range/volume, and accuracy). Furthermore, we invite researchers to add to the
here presented chart (Figure 2.2) and system overview online.
Acknowledgements
We want to thank dr. Dimitra Dodou for proof reading our work. This study was supported
by NWO-STW 12870.
References
Akman, O. (2012). Robust augmented reality. TU Delft, Delft University of Technology.
Bader, J. (2011). Validation of a dynamic calibration method for video supported movement
analysis. Unpublished Master’s Thesis). Technische Universitat, Munchen.
Begon, M., Colloud, F., Fohanno, V., Bahuaud, P., & Monnet, T. (2009). Computation of the 3D
kinematics in a global frame over a 40 m-long pathway using a rolling motion analysis system.
Journal of Biomechanics, 42(16), 2649–2653. http://doi.org/10.1016/j.jbiomech.2009.08.020
Berber, M., Ustun, A., & Yetkin, M. (2012). Comparison of accuracy of GPS techniques.
Measurement: Journal of the International Measurement Confederation, 45(7), 1742–1746.
http://doi.org/10.1016/j.measurement.2012.04.010
Bischoff, O., Heidmann, N., Rust, J., & Paul, S. (2012). Design and implementation of an
ultrasonic localization system for wireless sensor networks using angle-of-arrival and distance
PART I CHAPTER 2
measurement. Procedia Engineering, 47, 953–956. http://doi.org/10.1016/j.proeng.2012.09.304
Bonnechere, B., Jansen, B., Salvia, P., Bouzahouene, H., Omelina, L., Moiseev, F., … Jan, S. V. S.
(2014). Validity and reliability of the Kinect within functional assessment activities: comparison
with standard stereophotogrammetry. Gait & Posture, 39(1), 593–598.
Brodie, M., Walmsley, A., & Page, W. (2008). Dynamic accuracy of inertial measurement units
during simple pendulum motion: Technical Note. Computer Methods in Biomechanics and
Biomedical Engineering, 11(3), 235–242.
Brodie, M., Walmsley, A., & Page, W. (2008). Fusion motion capture : a prototype system using
inertial measurement units and GPS for the biomechanical analysis of ski racing Research
Article. Sports Technology, 1(1), 17–28. http://doi.org/10.1002/jst.6
Ceseracciu, E., Sawacha, Z., Fantozzi, S., Cortesi, M., Gatta, G., Corazza, S., & Cobelli, C. (2011).
Markerless analysis of front crawl swimming. Journal of Biomechanics, 44(12), 2236–2242.
http://doi.org/10.1016/j.jbiomech.2011.06.003
Choppin, S., Lane, B., & Wheat, J. (2014). The accuracy of the Microsoft Kinect in joint angle
measurement. Sports Technology, 7(1–2), 98–105.
Colloud, F., Chèze, L., André, N., & Bahuaud, P. (2008). An innovative solution for 3d kinematics
measurement for large volumes. Journal of Biomechanics, 41, S57.
Corazza, S., Mündermann, L., Gambaretto, E., Ferrigno, G., & Andriacchi, T. P. (2010). Markerless
motion capture through visual hull, articulated icp and subject specific model generation.
International Journal of Computer Vision, 87(1), 156–169.
Day, J. S., Dumas, G. a., & Murdoch, D. J. (1998). Evaluation of a long-range transmitter for use
with a magnetic tracking device in motion analysis. Journal of Biomechanics, 31(10), 957–961.
http://doi.org/10.1016/S0021-9290(98)00089-X
Duffield, R., Reid, M., Baker, J., & Spratford, W. (2010). Accuracy and reliability of GPS devices
for measurement of movement patterns in confined spaces for court-based sports. Journal of
Science and Medicine in Sport, 13(5), 523–525.
Dutta, T. (2012). Evaluation of the Kinect??? sensor for 3-D kinematic measurement in the
workplace. Applied Ergonomics, 43(4), 645–649. http://doi.org/10.1016/j.apergo.2011.09.011
Gilgien, M., Spörri, J., Limpach, P., Geiger, A., & Müller, E. (2014). The effect of different Global
Navigation Satellite System methods on positioning accuracy in elite alpine skiing. Sensors,
14(10), 18433–18453.
Hedley, M., Mackintosh, C., Shuttleworth, R., Humphrey, D., Sathyan, T., & Ho, P. (2010).
Wireless tracking system for sports training indoors and outdoors. Procedia Engineering, 2(2),
2999–3004. http://doi.org/10.1016/j.proeng.2010.04.101
Hedley, M., Sathyan, T., & MacKintosh, C. (2011). Improved wireless tracking for indoor sports.
Procedia Engineering, 13, 439–444. http://doi.org/10.1016/j.proeng.2011.05.111
HumanEva. (2017). HumanEva.
Kersting, U. G., Kurpiers, N., Darlow, B. J. S., & Nolte, V. W. (2008). Three-dimensional assessment
of on water rowing technique: a methodological study. In ISBS-Conference Proceedings
Accuracy of human motion capture systems
Archive (Vol. 1).
Khoury, H. M., & Kamat, V. R. (2009). Evaluation of position tracking technologies for user
localization in indoor construction environments. Automation in Construction. http://doi.
org/10.1016/j.autcon.2008.10.011
Klous, M., Müller, E., & Schwameder, H. (2010). Collecting kinematic data on a ski/snowboard
track with panning, tilting, and zooming cameras: is there sufficient accuracy for a biomechanical
analysis? Journal of Sports Sciences, 28(12), 1345–1353. http://doi.org/10.1080/02640414.20
10.507253
Lee, J. B., Burkett, B. J., Thiel, D. V., & James, D. A. (2011). Inertial sensor, 3D and 2D assessment
of stroke phases in freestyle swimming. Procedia Engineering, 13, 148–153.
Lee, J. B., Ohgi, Y., & James, D. a. (2012). Sensor fusion: Let’s enhance the performance of
performance enhancement. Procedia Engineering. http://doi.org/10.1016/j.proeng.2012.04.136
Liu, G., Tang, X., Cheng, H. D., Huang, J., & Liu, J. (2009). A novel approach for tracking high
speed skaters in sports using a panning camera. Pattern Recognition, 42(11), 2922–2935.
http://doi.org/10.1016/j.patcog.2009.03.022
Lluna, E., Santiago, V., Defez, B., Dunai, L., & Peris-Fajarnes, G. (2011). Velocity vector (3D)
measurement for spherical objects using an electro-optical device. Measurement: Journal of
the International Measurement Confederation, 44(9), 1723–1729. http://doi.org/10.1016/j.
measurement.2011.07.006
Maletsky, L. P., Sun, J., & Morton, N. a. (2007). Accuracy of an optical active-marker system to
track the relative motion of rigid bodies. Journal of Biomechanics, 40(3), 682–685. http://doi.
org/10.1016/j.jbiomech.2006.01.017
Monnet, T., Samson, M., Bernard, A., David, L., & Lacouture, P. (2014). Measurement of three-
dimensional hand kinematics during swimming with a motion capture system: a feasibility
study. Sports Engineering, 3(17), 171–181.
Neuron, P. (2017). https://neuronmocap.com/.
Nikon. (2017). https://www.nikonmetrology.com/en-gb/product/igps.
Ogris, G., Leser, R., Horsak, B., Kornfeind, P., Heller, M., & Baca, A. (2012). Accuracy of the LPM
tracking system considering dynamic position changes. Journal of Sports Sciences, 30(14),
1503–1511.
Panjkota, A., Stancic, I., & Supuk, T. (2009). Outline of a qualitative analysis for the human
motion in case of ergometer rowing. In WSEAS International Conference. Proceedings.
Mathematics and Computers in Science and Engineering. WSEAS.
Perrat, B., Smith, M. J., Mason, B. S., Rhodes, J. M., & Goosey-Tolfrey, V. L. (2015). Quality
assessment of an Ultra-Wide Band positioning system for indoor wheelchair court sports.
Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering
and Technology, 229(2), 81–91.
Rhodes, J., Mason, B., Perrat, B., Smith, M., & Goosey-Tolfrey, V. (2014). The validity and
reliability of a novel indoor player tracking system for use within wheelchair court sports.
PART I CHAPTER 2
Journal of Sports Sciences, 32(17), 1639–1647.
Richards, J. G. (1999). The measurement of human motion: A comparison of commercially
available systems. Human Movement Science, 18(5), 589–602. http://doi.org/10.1016/S0167-
9457(99)00023-8
Sathyan, T., Shuttleworth, R., Hedley, M., & Davids, K. (2012). Validity and reliability of a radio
positioning system for tracking athletes in indoor and outdoor team sports. Behavior Research
Methods, 44(4), 1108–1114.
Scaramuzza, D., & Fraundorfer, F. (2011). Visual odometry [tutorial]. IEEE Robotics & Automation
Magazine, 18(4), 80–92.
Schepers, H. M., & Veltink, P. H. (2010). Stochastic magnetic measurement model for relative
position and orientation estimation. Measurement Science and Technology, 21(6), 65801.
http://doi.org/10.1088/0957-0233/21/6/065801
Schuler, N. B., Bey, M. J., Shearn, J. T., & Butler, D. L. (2005). Evaluation of an electromagnetic
position tracking device for measuring in vivo, dynamic joint kinematics. Journal of
Biomechanics, 38(10), 2113–2117.
Shirehjini, A. A. N., Yassine, A., & Shirmohammadi, S. (2012). An RFID-based position and
orientation measurement system for mobile objects in intelligent environments. IEEE
Transactions on Instrumentation and Measurement, 61(6), 1664–1675. http://doi.org/10.1109/
TIM.2011.2181912
Spörri, J., Schiefermüller, C., & Müller, E. (2016). Collecting Kinematic Data on a Ski Track with
Optoelectronic Stereophotogrammetry: A Methodological Study Assessing the Feasibility of
Bringing the Biomechanics Lab to the Field. PloS One, 11(8), e0161757.
Stancic, I., Supuk, T. G., & Panjkota, A. (2013). Design, development and evaluation of optical
motion-tracking system based on active white light markers. IET Science, Measurement &
Technology, 7(4), 206–214.
Stelzer, a. (2004). Concept and application of LPM-a novel 3-D local position measurement
system. IEEE Transactions on Microwave Theory and Techniques, 52(12), 2664–2669. http://
doi.org/10.1109/TMTT.2004.838281
Tan, H., Wilson, A. M., & Lowe, J. (2008). Measurement of stride parameters using a wearable
GPS and inertial measurement unit. Journal of Biomechanics. http://doi.org/10.1016/j.
jbiomech.2008.02.021
Ting, S. L., Kwok, S. K., Tsang, A. H. C., & Ho, G. T. S. (2011). The Study on Using Passive RFID
Tags for Indoor Positioning. International Journal of Engineering Business Management, 3(1),
1. http://doi.org/10.5772/45678
van der Kruk, E. (2013a). Modelling and Measuring 3D Movements of a Speed Skater. TU Delft.
van der Kruk, E. (2013b). Smooth Measuring; What measurement systems could serve the
purpose of finding accurate kinematic data of skaters on an ice rink?
Waegli, A., & Skaloud, J. (2009). Optimization of two GPS/MEMS-IMU integration strategies
with application to sports. GPS Solutions, 13(4), 315–326.
Accuracy of human motion capture systems
Windolf, M., Götzen, N., & Morlock, M. (2008). Systematic accuracy and precision analysis
of video motion capturing systems-exemplified on the Vicon-460 system. Journal of
Biomechanics, 41(12), 2776–2780. http://doi.org/10.1016/j.jbiomech.2008.06.024
Xsens. (2017). www.xsens.com.
Zhong, D., & Chang, S. F. (2004). Real-time view recognition and event detection for sports
video. Journal of Visual Communication and Image Representation, 15(3), 330–347. http://doi.
org/10.1016/j.jvcir.2004.04.009
Zohlandt, C., Walk, L., & Nawara, W. (2012). Classification of Vault Jumps in Gymnastics, 1–9.
PART I CHAPTER 2
Article
Full-text available
Gait phase detection is of great significance in the field of motion analysis and exoskeleton-assisted walking, and can realize the accurate control of exoskeleton robots. Therefore, in order to obtain accurate gait information and ensure good gait phase detection accuracy, a gait recognition framework based on the New Hidden Markov Model (NHMM) is proposed to improve the accuracy of gait phase detection. A multi-sensor gait data acquisition system was developed and used to collect the training data of eight healthy subjects to measure the acceleration and plantar pressure of the human body. Accuracy of the recognition framework, filtering algorithm and window selection, and the missing validation of the generalization performance of the method were evaluated. The experimental results show that the overall accuracy of NHMM is 94.7%, which is better than all other algorithms. The generalization of the performance is 84.3%. The results of this study provide a theoretical basis for the design and control of the exoskeleton.
Article
Full-text available
Wearable and movable lodged health monitoring gadgets, micro-sensors, human system locating gadgets, and other gadgets started to appear as low-power communication mechanisms and microelectronics mechanisms grew in popularity. More people are interested in energy capture technology, which turns the energy created by motion technology into electric energy. To understand the difference in motor skill levels, a nonlinear feature-oriented method was proposed. A bi-stable magnetic-coupled piezoelectric cantilever was designed to detect the horizontal difference of motion technology. The horizontal difference was increased by the acceleration generated by the oscillation of the leg and the impression betwixt the leg and the ground during the movement. Based on the Hamiltonian principle and motion technique signal, a nonlinear dynamic model for energy capture in motion technique is established. According to the shaking features of human leg motion, a moveable nonlinear shaking energy-gaining system was the layout, which realized the dynamic characteristics of straight, nonlinear, mono-stable, and bi-stable. The experimental outcome shows that nonlinearity can effectively detect the difference of motion techniques. The experimental results of different human movement states confirm the benefits of the uncertain bi-stable human power capture mechanism and the effectiveness of the electromechanical combining design established. The nonlinear mono-stable beam moves in the same way as the straight mono-stable beam in the assessment, but owing to its higher stiffness, its frequency concentration range (13.85 Hz) is moved to the right compared to the linear mono-stable beam, and the displacement of the cantilever beam is reduced. If the velocity is 8 km/h, the mean energy of the bi-stable method extends to the utmost value of 23.2 μW. It is proved that the nonlinear method can understand the difference in the level of motion technique effectively.
Article
Assessing gaze behavior during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines was assessed, a validation task requiring short fixations on task-relevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and “in the wild” studies. Further, we generated and compared models using spherical and Cartesian coordinate systems and monocular (left or right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimeter errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend 1-min calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest-quality gaze vectors.
Conference Paper
Full-text available
Human body-strap-based, and sensor-mounted clothing-based easy-to-wear motion capture systems have generated a lot of interest and been thoroughly investigated in recent times. Body movements are typically assessed using miniature inertial measurement unit (IMU) sensors. Based on body strap IMU sensors, this paper presents a system enabling real-time human motion monitoring and reconstruction. To track fullbody motions, the system utilizes a human-body kinematics assured orientation and position estimation technique. A three dimensional (3D) humanoid model-based motion reconstruction module is included, letting people visualize human motions in real-time. Furthermore, We can edit the motion data that has already been captured and validate joint-segment kinematics. Finally, we utilize complicated yoga exercise movements to demonstrate the accuracy in terms of orientation estimation.
Article
Full-text available
In the laboratory, optoelectronic stereophotogrammetry is one of the most commonly used motion capture systems; particularly, when position-or orientation-related analyses of human movements are intended. However, for many applied research questions, field experiments are indispensable, and it is not a priori clear whether optoelectronic stereophotogrammetric systems can be expected to perform similarly to in-lab experiments. This study aimed to assess the instrumental errors of kinematic data collected on a ski track using optoelectronic stereophotogrammetry, and to investigate the magnitudes of additional skiing-specific errors and soft tissue/suit artifacts. During a field experiment, the kinematic data of different static and dynamic tasks were captured by the use of 24 infrared-cameras. The distances between three passive markers attached to a rigid bar were stereophotogrammetrically reconstructed and, subsequently, were compared to the manufacturer-specified exact values. While at rest or skiing at low speed, the optoelectronic stereophotogrammetric system's accuracy and precision for determining inter-marker distances were found to be comparable to those known for in-lab experiments (< 1 mm). However, when measuring a skier's kinematics under " typical " skiing conditions (i.e., high speeds, inclined/angulated postures and moderate snow spraying), additional errors were found to occur for distances between equipment-fixed markers (total measurement errors: 2.3 ± 2.2 mm). Moreover, for distances between skin-fixed markers , such as the anterior hip markers, additional artifacts were observed (total measurement errors: 8.3 ± 7.1 mm). In summary, these values can be considered sufficient for the detection of meaningful position-or orientation-related differences in alpine skiing. However, it must be emphasized that the use of optoelectronic stereophotogrammetry on a ski track is seriously constrained by limited practical usability, small-sized capture volumes and the occurrence of extensive snow spraying (which results in marker obscuration). The latter limitation possibly might be overcome by the use of more sophisticated cluster-based marker sets.
Article
Full-text available
The Microsoft Kinect is a cheap consumer device capable of markerless body segment tracking. While it was not originally designed for research applications, studies have been performed which utilise its capabilities. In order to better define the suitability of the device in a clinical and biomechanical context, a study was performed which assessed the accuracy of the device in 12 separate movements and for two different software-based tracking algorithms (IPIsoft and NITE). The movements were chosen to represent a variety of joint motions and speeds. Ten participants (height, 185 ± 6 cm; mass, 77 ± 9 kg) performed each movement while the Kinect and a Motion Analysis Corporation capture system recorded simultaneously. The procedure was performed twice, once for each tracking algorithm. Median values for RMSE, maximum error, systematic bias and proportional bias were 12.6°, 58.2°, 4.38° and 1.15°, respectively, for the IPIsoft algorithm and 13.8°, 63.1°, 3.16° and 1.19°, respectively, for the NITE algorithm. While maximum errors are high the system has many advantages over existing multi-camera markerless tracking systems. The Kinect could be used in low speed analysis of simple human motions where cost and ecological validity are of high priority.
Article
Full-text available
This paper presents a localization system for Wireless Sensor Networks (WSN) based on ultrasonic (US) Time-of-Flight (ToF) measurements. The participants send out US pulses while a central localization unit measures the Time-Difference-of-Arrival (TDoA) between four US sensors to calculate the Angle-of-Arrival (AoA). The radio frequency (RF) transceiver of the sensor nodes enables distance measurements using TDoA (US vs. RF) in addition. This improves the localization accuracy significantly since the estimated distance from triangulation suffers excessively from even small angle errors. Several filter stages including Kalman-filtering minimize the number of outliers and fluctuations of the calculated distances and angles. Those computed polar coordinates (angle/distance) are converted into a Cartesian form and forwarded to a base station which is connected to a PC. The mean error and standard deviation of the angle and distance measurements (1.36 degrees +/- 0.39 degrees / 1.00 cm +/- 0.14 cm) lead to a small mean localization error of 4.21 cm and a standard deviation of 0.57 cm. (C) 2012 Elsevier Ltd....Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o.
Article
Full-text available
Typical in-filed technology used for human movement assessment is a single tool such as a video camera. However, there are drawbacks with this system. Understanding various human actions e. g. swimming kinematics, or the legality of a bowling action in cricket, is important for performance analysis. Inertial sensors can biomechanically capture temporal kinematics. Measuring the kinematics of human movement, allows performance analysers to monitor progression of an athlete's development. Fusing video and inertial sensor technology supplies visual feedback in conjunction with technical information. The video-sensor fusion presented here provides assessment for athletes where gross visual measures combined with fine movement monitoring possible. Therefore, a novel system can be offered which can assist athlete performance development. (C) 2012 Published by Elsevier Ltd.
Article
Full-text available
In sport science, Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration. Application of GNSS includes a large range of different GNSS technologies and methods. To date no study has comprehensively compared the different GNSS methods applied. Therefore, the aim of the current study was to investigate the effect of differential and non-differential solutions, different satellite systems and different GNSS signal frequencies on position accuracy. Twelve alpine ski racers were equipped with high-end GNSS devices while performing runs on a giant slalom course. The skiers' GNSS antenna positions were calculated in three satellite signal obstruction conditions using five different GNSS methods. The GNSS antenna positions were compared to a video-based photogrammetric reference system over one turn and against the most valid GNSS method over the entire run. Furthermore, the time for acquisitioning differential GNSS solutions was assessed for four differential methods. The only GNSS method that consistently yielded sub-decimetre position accuracy in typical alpine skiing conditions was a differential method using American (GPS) and Russian (GLONASS) satellite systems and the satellite signal frequencies L1 and L2. Under conditions of minimal satellite signal obstruction, valid results were also achieved when either the satellite system GLONASS or the frequency L2 was dropped from the best configuration. All other methods failed to fulfill the accuracy requirements needed to detect relevant differences in the kinematics of alpine skiers, even in conditions favorable for GNSS measurements. The methods with good positioning accuracy had also the shortest times to compute differential solutions. This paper highlights the importance to choose appropriate methods to meet the accuracy requirements for sport applications.
Article
Full-text available
Abstract The aim of the current study was to investigate the validity and reliability of a radio frequency-based system for accurately tracking athlete movement within wheelchair court sports. Four wheelchair-specific tests were devised to assess the system during (i) static measurements; (ii) incremental fixed speeds; (iii) peak speeds; and (iv) multidirectional movements. During each test, three sampling frequencies (4, 8 and 16 Hz) were compared to a criterion method for distance, mean and peak speeds. Absolute static error remained between 0.19 and 0.32 m across the session. Distance values (test (ii)) showed greatest relative error in 4 Hz tags (1.3%), with significantly lower errors seen in higher frequency tags (<1.0%). Relative peak speed errors of <2.0% (test (iii)) were revealed across all sampling frequencies in relation to the criterion (4.00 ± 0.09 m · s-(1)). Results showed 8 and 16 Hz sampling frequencies displayed the closest-to-criterion values, whilst intra-tag reliability never exceeded 2.0% coefficient of variation (% CV) during peak speed detection. Minimal relative distance errors (<0.2%) were also seen across sampling frequencies (test (iv)). To conclude, the indoor tracking system is deemed an acceptable tool for tracking wheelchair court match play using a tag frequency of 8 or 16 Hz.
Article
Full-text available
Current trends in swimming biomechanics are focused on accurate measurements. Nowadays, reliable calibration methods have been proposed to reach an accuracy of about 1 mm on rigid structure. But the question remains about the final accuracy for three-dimensional hand kinematics measurement during the underwater phase of front crawl swimming. Furthermore, most research is based on manual tracking with two or more cameras. In this paper we propose a protocol to acquire three-dimensional hand kinematics when swimming in a specific pool with a motion analysis system behind windows. Results highlight the benefits of using such a system in terms of accuracy and feasibility: the time allowed for post-processing is ten times lower and the quantified improved accuracy is better than with manual tracking.
Thesis
A newly developed dynamic calibration method for video supported movement analysis systems is validated and tested in this thesis with two different SIMI Motion systems. This is done by analyzing the accuracy of the dynamic calibration on the one side, and on the other side by comparing the new calibration method to the results of a currently used static calibration, a DLT based method, as well as to the results of a Vicon system which uses a dynamic calibration. Markers are attached to a rigid T-shaped object to allow measuring three distances, two angles and the computation of a reprojection error. One single reference video was recorded for each system and applied with several calibrations. All tests were conducted in a specially prepared laboratory to avoid the influence of disturbing variables. During the tests a missing correction of distortion for the dynamic calibration is identified as one of the main problems. The main tests are extended with an undistorting checkerboard calibration to indicate an included undistorting function for the dynamic calibration. The results gained during the tests prove that the new dynamic calibration yields a valid calibration and is a huge improvement for accuracy and usability usability for a video support movement analysis if the distortion is handled well.
Article
The aim of this study was to assess the accuracy and reliability of global positioning system (GPS) measures of distance and speed, compared to a high-resolution motion analysis system, for confined movement patterns used in many court-based sports. A single male participant performed 10 repetitions of four respective drills replicating court-based movement patterns and six repetitions of a random movement drill that replicated tennis match-play movement patterns. Two 1 Hz and two 5 Hz GPS devices concurrently measured distance covered and speed of all court-based drills. A 22 camera VICON motion analysis system, operating at 100 Hz, tracked the position of an 18 mm reflective marker affixed to one of the GPS devices to provide the criterion movement data. Results indicated that both 1 and 5 Hz GPS devices under reported distance covered as well as both mean and peak speed compared to the VICON system (P < 0.05). The coefficient of variation for both GPS devices for distance and speed measures ranged between 4 and 25%. Further, the faster the speed and more repetitive the movement pattern (over a similar location), the greater the measurement error. The inter-unit reliability for distance and speed measures of both 1 and 5 Hz systems for movements in confined spaces was generally low to moderate (r = 0.10–0.70). In conclusion, for court-based sports or movements in confined spaces, GPS technology under reports distance covered and both mean and peak speed of movement
Article
Ultra-Wide Band radio positioning systems are maturing very quickly and now represent a good candidate for indoor positioning. The aim of this study was to undertake a quality assessment on the use of a commercial Ultra-Wide Band positioning system for the tracking of athletes during indoor wheelchair court sports. Several aspects have been investigated including system set-up, calibration, sensor positioning, determination of sport performance indicators and quality assessment of the output. With a simple set-up procedure, it has been demonstrated that athletes tracking can be achieved with an average horizontal positioning error of 0.37 m (σ = ± 0.24 m). The distance covered can be computed after data processing with an error below 0.5% of the course length. It has also been demonstrated that the tag update rate and the number of wheelchairs on the court do not affect significantly the positioning quality; however, for highly dynamic movement tracking, higher rates are recommended for a finer dynamic recording.