Measuring biomechanical loads in team sports – from lab to field

Abstract

The benefits of differentiating between the physiological and biomechanical load-response pathways in
football and other (team) sports have become increasingly recognised. In contrast to physiological loads
however, the biomechanical demands of training and competition are still not well understood, primarily
due to the difficulty of quantifying biomechanical loads in a field environment. Although musculoskeletal
adaptation and injury are known to occur at a tissue level, several biomechanical load metrics are
available that quantify loads experienced by the body as a whole, its different structures and the
individual tissues that are part of these structures. This paper discusses the distinct aspects and challenges
that are associated with measuring biomechanical loads at these different levels in laboratory and/
or field contexts. Our hope is that through this paper, sport scientists and practitioners will be able to
critically consider the value and limitations of biomechanical load metrics and will keep pursuing new
methods to measure these loads within and outside the lab, as a detailed load quantification is essential
to better understand the biomechanical load-response pathways that occur in the field.

Full text

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
Measuring biomechanical loads in team sports – from lab to field
Jasper Verheul 1, Niels J. Nedergaard2, Jos Vanrenterghem2, and Mark A. Robinson1
1Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
2Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
January 9, 2020
(This manuscript has been peer-reviewed and published in Science and Medicine in Football )
https://doi.org/10.1080/24733938.2019.1709654
Abstract
The benefits of differentiating between the physiological and biomechanical load-response pathways in football and
other (team) sports have become increasingly recognised. In contrast to physiological loads however, the biomechanical
demands of training and competition are still not well understood, primarily due to the difficulty of quantifying
biomechanical loads in a field environment. Although musculoskeletal adaptation and injury are known to occur
at a tissue level, several biomechanical load metrics are available that quantify loads experienced by the body as a
whole, its different structures and the individual tissues that are part of these structures. This paper discusses the
distinct aspects and challenges that are associated with measuring biomechanical loads at these different levels in
laboratory and/or field contexts. Our hope is that through this paper, sport scientists and practitioners will be able
to critically consider the value and limitations of biomechanical load metrics and will keep pursuing new methods to
measure these loads within and outside the lab, as a detailed load quantification is essential to better understand the
biomechanical load-response pathways that occur in the field.
1 Introduction
Optimal sports performance with minimal injury risk is
largely determined by the training an athlete has been ex-
posed to. Whilst sufficient training loads are required to
achieve beneficial physical adaptations for enhanced per-
formance in the form of improved fitness, excessive loading
can introduce fatigue and is known to increase the risk of
injury [1,2]. Training loads are, therefore, widely mea-
sured and monitored in football and other (team) sports,
with the aim to better control training prescription and op-
timise load-response pathways. On the one hand there is a
physiological load-response pathway, where the metabolic
challenge to maintain powerful and prolonged skeletal mus-
cle contractions triggers a broad range of biochemical re-
sponses in the body, primarily in the form of metabolic and
cardiorespiratory adaptations [3,4]. On the other hand,
there is a biomechanical load-response pathway, where the
mechanical challenges to withstand high forces repetitively
applied to the musculoskeletal system triggers mechanobio-
logical tissue responses of the muscles, tendons, ligaments,
bones and articular cartilage [5,6,7]. There is a growing
belief that monitoring the physiological and biomechanical
loads separately can contribute to the holistic understand-
ing of an athlete’s adaptive mechanisms that ultimately
determine their physical fitness and performance outcomes
[8]. However, in contrast to a considerable understanding of
the physiological branch, the extent to which (team) sports
imposes loads on the musculoskeletal system and triggers
mechanobiological responses that make the tissues stronger
or weaker are relatively under-investigated and not well un-
derstood.
A major issue that limits the progress in understanding
biomechanical load-response pathways, is that measuring
in vivo biomechanical loads to the musculoskeletal system
as a whole, to the various structures within it, and to the
tissues making up those structures, remains very difficult
or even impossible with the current technologies, especially
in a field-based context. Our aim was therefore 1) to pro-
vide an overview of biomechanical load metrics at differ-
ent levels, 2) to discuss current methods and challenges for
measuring in vivo biomechanical loads, and 3) suggest fu-
ture considerations and avenues to be explored to enhance
field-based biomechanical load monitoring.
Corresponding Author: Jasper Verheul (J.P.Verheul@ljmu.ac.uk)
This manuscript has been peer-reviewed and published in Science and Medicine in Football
Authors Jasper Verheul (@jasper_verheul), Niels J. Nedergaard (@NJ_Nedergaard), Jos Vanrenterghem (@ScienceJos) and Mark A. Robinson
(@mrobbo18) can be reached on Twitter.
This work can be cited as: Verheul, J., Nedergaard, N.J., Vanrenterghem, J., Robinson, M.A. (2020). Measuring biomechanical loads in team
sports – from lab to field. Science and Medicine in Football. doi:10.1080/24733938.2019.1709654
1

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
Figure 1: Schematic overview of currently available biomechanical load metrics. The feasibility of measuring these
metrics, ranging from strictly limited to the laboratory to viable in field environments, is indicated along the y-axis.
The level at which loads act on the musculoskeletal system is indicated along the x-axis. The different hard- and soft-
tissues affected by each load metric are shown in red (muscles), green (tendons and ligaments) and/or blue (bones and
cartilage). Metrics to assess tissue- or structure-specific loads that are viable to be measured in the field are still lacking.
2 Tissue Loads
During training and match-play in football and other
(team) sports, the different hard- and soft-tissues of the
body are exposed to an array of forces. These forces cause
mechanical tension within the tissues in the form of stresses
and strains that, together with exercise-induced microdam-
age and metabolic stress, trigger remodelling and repair
responses. Examples of such adaptations include alter-
ations in muscle architecture [9,10], changes in tendon
stiffness and structure [11,12,13,14], and increased bone
mass and mineral density [15,16], which are generally con-
sidered desirable characteristics for enhanced performance
(e.g. higher force production, increased storage and re-
turn of elastic energy). Excessive exposure to stresses and
strains on the other hand, can outpace repair mechanisms
and cause an accumulation of micro-damage that weakens
the tissues over time. This progressive weakening can ulti-
mately lead to mechanical fatigue and tissue failure, such
as muscle tears, tendon rupture or bone fractures [17,18].
The optimal loading thresholds of individual tissues depend
on many factors, including tissue properties and loading
history. In an ideal world one would thus want to quan-
tify and monitor the accumulation of tissue-specific stresses
and strains over time.
From a mechanical perspective stress and strain can be
defined as the force acting per unit surface area and the
resulting relative tissue deformation, respectively. This di-
rect relationship between force, stress and strain allows for
in vitro experiments to be performed to investigate tissue
adaptative or failure responses to predefined biomechani-
cal loads [19,20]. Such experiments can provide a detailed
insight into tissue behaviour under specific loading condi-
tions, but require highly controlled laboratory setups, ho-
mogeneous tissue specimens and strictly constant or repet-
itive loading patterns. As an alternative, advanced compu-
tational modelling approaches (e.g. finite element analysis)
can be used to accurately predict stress and strain distri-
butions throughout tissues in silico, and investigate their
response mechanisms under different mechanical and bio-
2

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
logical conditions [21,22]. However, there is extensive phys-
iological, structural and morphological variability within
musculoskeletal structures, and during sports movements
tissues are exposed to highly varying non-uniform tensile,
compressive and shear forces. This makes it difficult to
translate findings from controlled in vitro and/or in sil-
ico studies to the field, beyond understanding the expected
stress-related deformations and stress tolerances of individ-
ual tissues. Although biomechanical responses to training
loads are thus known to take place at a tissue level, the
quantification of tissue-specific loads is primarily restricted
to laboratory environments only (Figure 1).
3 Structural Loads
Much research has investigated loads experienced by the
musculoskeletal system at a structural level. Individual or-
gans (e.g. muscles, tendons, ligaments, bones) or a combi-
nation thereof (e.g. joints, segments, limbs) form structures
on which forces and moments act. These structural loads
thus describe the combination of stresses and strains work-
ing on the individual tissues comprised by the structure.
Net moments about the knee joint structure for example,
can be used as an indicator of loading magnitude and in-
jury risk of the anterior cruciate ligament [23,24]. Likewise,
measures of joint or leg stiffness, which is the resistance of
a structure to withstand the forces acting on it, have been
demonstrated to be sensitive to training status [25], run-
ning speed [26] and exercise-induced fatigue [27,28] (see
[29] for an extensive discussion of the use of stiffness mea-
sures in sports). Quantifying structure-specific loading pa-
rameters can thus be informative for evaluating the risk of
injury or biomechanical adaptations to training.
To indirectly estimate the in vivo loads acting on indi-
vidual structures, including bone and muscle-tendon forces,
and joint moments, reaction forces and stiffness param-
eters, musculoskeletal modelling techniques can be used
[30,31]. Although such approaches are traditionally labori-
ous and time consuming, recent advancements have shown
the potential for real-time analysis of joint forces and mo-
ments, as well as muscle-tendon forces [32,33,34,35].
The downside of these methods however, is that they
are strongly dependent on kinematic (motion-capture sys-
tems), kinetic (force platforms) and/or neuromuscular
(electromyography) input, the combination of which is yet
largely restricted to laboratories. Several studies have,
therefore, aimed to directly measure the in vivo structure-
specific loads. Surgically implanted force transducers or
strain gauges may, for example, be used to measure muscle-
tendon forces [36,37,38] or bone strains [39] for walk-
ing, running and jumping activities, but their invasive and
temporary nature makes the use of implants unsuitable
for large-scale human experiments, let alone day-to-day
load monitoring in the field. Very recently, a wearable
tensiometer device has shown promising results for non-
invasively assessing mechanical properties and loading of
superficial tendons [40], and could be a first step towards
the direct and field-based measurement of structure-specific
loads.The difficulty of directly measuring structural forces
has also led to the exploration of various indicators (or sur-
rogate measures) of structural load. Tibial accelerations
measured from shank-mounted accelerometers for example,
have been suggested to provide a valid, reliable and sim-
ple field-based indicator of tibial loading [41,42,43], but
it remains uncertain if tibial accelerations are related to
the actual forces, stresses and strains experienced by the
bone [44]. In short therefore, despite the availability of
several techniques to quantify structural loads directly or
indirectly, their application is still primarily bound to a lab
context (Figure 1).
4 Whole-Body Loads
Besides internal stresses and strains that are experienced
by specific tissues and/or structures, the body as a whole
is exposed to external forces. These external loads are
primarily caused by interactions with other athletes (e.g.
during tackling), equipment (e.g. kicking or hitting a ball)
or the ground. Ground reaction forces (GRFs) following
from foot-ground interactions especially, both drive and
are affected by muscular actions, and contribute to impact
forces experienced by individual structures. GRFs thus de-
scribe the biomechanical loading experienced by the mus-
culoskeletal system as a whole and have been investigated
extensively for their potential association with running per-
formance features [45,46,47] or specific overuse related
pathologies [48,49,50]. Such relationships remain ambigu-
ous though [48,50] and GRF may even be a poor predictor
of the loads experienced at a structural level [49,20].
Whilst GRF alone unlikely suffices as a source of infor-
mation for the prevention or treatment of particular tissue-
or structure-specific pathologies, GRF can still provide a
generic indicator of cumulative loading of the musculoskele-
tal system as a whole. In contrast to tissue- and structure-
specific loads, GRFs can be measured relatively easily and
non-invasively from force platforms. Unfortunately, force
platforms are not suitable for sport-specific training and
competition environments, and different approaches have
been explored to estimate GRF from wearable devices in
the field. Probably the most intuitive method is by using
instrumented insoles, which are typically worn in or under
the shoe and provide a summed measure of the pressure
that the foot exerts on the ground [51]. Although pres-
sure insoles can estimate GRF for running and jumping
fairly well [52,53,54,55,56], their compromised accuracy
for high-intensity movements [52,54,55,56] and practical
limitations (e.g. movement restrictions, added mass in the
shoe, discomfort) [52], leaves the feasibility of using insoles
for monitoring GRF on a large-scale in the field currently
still questionable.
Based on the relationship between force and accelera-
tion according to Newton’s second law (F=m a), segmen-
tal movements may be used to indirectly estimate GRF
[57,58,59]. Currently popular body-worn accelerometers
have, therefore, received special attention for their poten-
tial to measure GRF in this manner [41,60,61,62,63,
3

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
64,65]. Several studies have, however, demonstrated that
either whole GRF waveforms [60,61,62], or even specific
GRF features [41,61,63], cannot be estimated well from in-
dividual trunk-, pelvis- or shank-mounted accelerometers.
In fact, the majority of segmental accelerations are likely
required to accurately estimate GRF [57,58], making the
use of one or even a combination of several accelerometer
units to predict GRF probably insufficient.
Besides GRF, other accelerometry-based metrics have
been suggested to assess whole-body loading, including ver-
tical stiffness [66,67,68] and cumulative acceleration met-
rics [69,70,71,72,73,74]. Vertical stiffness is assumed
to represent the whole-body response to the dynamic ex-
ternal forces and may be used to assess neuromuscular
fatigue and performance after different types of training
[67,68]. Likewise, cumulative acceleration metrics (e.g.
PlayerLoadTM, New Body Load, Dynamic Stress Load,
Force Load [69,70,71,72,73,74]) are thought to pro-
vide an indication of the accumulated external impacts the
body is exposed to. However, the premise underpinning
these metrics that accelerations of individual segments ap-
propriately represent the whole-body acceleration is prob-
ably not valid [60], while evidence for a relationship with
loads acting on a structural or tissue level is yet lacking.
As such, if associations between any of these metrics and
performance improvements or increased injury risk are ob-
served, this does not provide an explanation for the un-
derlying mechanisms of such associations. In other words,
although GRF, stiffness or accelerometry-derived metrics
offer field-based methods to quantify whole-body loading
(Figure 1), their relevance and intrinsic value for assess-
ing load-response pathways at a structural or tissue level
remains to be determined.
5 From Lab to Field
A big hurdle for translating research into the biomechanical
load-response pathways from the lab to the field is the dif-
ficulty of quantifying biomechanical loads. This is primar-
ily due to the lack of means to accurately measure biome-
chanical information in an athlete’s natural training and/or
competition environment (e.g. a football pitch). Recent
developments have, however, demonstrated that such in-
formation might become more easily available in applied
sport settings in the near future. For example, full-body
wireless inertial sensor suits have been shown to be a re-
liable and valid method to simultaneously measure kine-
matic information of all body segments outside the labo-
ratory (e.g. Xsens MVN [75]), and can already provide
GRF and joint moment estimates during stereotypical ac-
tivities such as walking [76,77]. To overcome discomfort
and movement restriction issues associated with the use
of multiple body-worn devices, markerless motion capture
techniques are a non-invasive method for measuring differ-
ent biomechanical variables in various sport environments
[78,79,80,81,82,83]. These techniques may in the future
allow for load metrics to be estimated at different levels. If
for example, information from body-worn sensors or mark-
erless motion capture can be used to accurately estimate
GRF [58,84], the combination of kinematics and GRF may
eventually be used to estimate structure-specific loading
and thus open the door to field-based measurements and
monitoring of internal biomechanical loads.
Given the often-limited availability of information in
day-to-day football environments (as well as other applied
sports settings), estimating biomechanical loads using con-
ventional mechanical methods that attempt to directly
measure load is not always possible. An imminent area
in sports biomechanics that overcomes this issue is the
use of advanced machine learning approaches to identify
and/or predict biomechanical variables of interest [85]. For
example, neural network methods have been used to pre-
dict GRF and moments [86,87] and joint forces [88] from
body-worn inertial sensors for different running tasks. Al-
though these studies show promising results, interpreting
the underlying biomechanical mechanisms of the predicted
variable can be difficult [85,89], which could limit their
application for e.g. explaining adaptation criteria or injury
mechanisms. If similar techniques can be used to accu-
rately predict tissue- or structure-specific forces however,
this may enable large-scale and non-invasive internal load
monitoring in the field.
To effectively investigate and describe biomechanical
load-response pathways in the field, the relevance of met-
rics used to quantify loads acting on the musculoskeletal
system, as well as the outcome measures against which
these loads are validated, should be considered. Popular
body-worn sensor technologies especially, have opened the
door for relatively easy measurements of several indicators
of whole-body loading, but the applied researcher or prac-
titioner should be reminded that their relationship with
established tissue or structural load metrics, or their rele-
vance in the context of the adaptive or injury mechanisms,
has not been validated. For example, changes observed at
a whole-body level (e.g. technique changes in a fatigued
state) can be insightful when assessing generic whole-body
adaptations to training but as yet, cannot be used to di-
rectly infer on load-response pathways experienced by in-
dividual tissues or structures. Therefore, careful validation
is required for such field-based metrics against measures of
tissue and/or structural responses (e.g. from tissue biop-
sies or ultrasound scanning) to establish the relationships
between available biomechanical load metrics and the adap-
tive or injury mechanisms occurring at internal levels.
6 Conclusion
Biomechanical load-response pathways can be explained at
different levels of the musculoskeletal system. Due to the
currently limited availability of field-based biomechanical
load metrics, enhancing our understanding of what biome-
chanical load metrics can and cannot be used for is essen-
tial. Our hope is that through this paper, sport scientists
and practitioners alike will revisit their views on the value
and limitations of biomechanical load metrics at differ-
ent levels. Nevertheless, we would like to encourage sport
4

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
scientists and biomechanics researchers to keep pursuing
ways to overcome the challenges of measuring these loads
within and outside the lab, as the detailed quantification
of biomechanical loads experienced during sport activities
is essential to further understand the in vivo biomechan-
ical load-response pathways and ultimately monitor them
in the field.
References
[1] T. G. Eckard, D. A. Padua, D. W. Hearn, B. S. Pexa,
and B. S. Frank, The Relationship Between Training
Load and Injury in Athletes: A Systematic Review,
vol. 48. Springer International Publishing, 2018.
[2] M. K. Drew and C. F. Finch, “The Relationship Be-
tween Training Load and Injury, Illness and Sore-
ness: A Systematic and Literature Review,” Sports
Medicine, 2016.
[3] F. M. Impellizzeri, E. Rampinini, and S. M. Mar-
cora, “Physiological assessment of aerobic training in
soccer,” Journal of Sports Sciences, vol. 23, no. 6,
pp. 583–92, 2005.
[4] M. J. MacInnis and M. J. Gibala, “Physiological adap-
tations to interval training and the role of exercise
intensity,” Journal of Physiology, vol. 595, no. 9,
pp. 2915–2930, 2017.
[5] N. Rosa, R. Simoes, F. D. Magalh˜aes, and A. T. Mar-
ques, “From mechanical stimulus to bone formation:
A review,” Medical Engineering and Physics, vol. 37,
no. 8, pp. 719–728, 2015.
[6] S. Bohm, F. Mersmann, and A. Arampatzis, “Human
tendon adaptation in response to mechanical loading:
a systematic review and meta-analysis of exercise in-
tervention studies on healthy adults,” Sports Medicine
- Open, vol. 1, no. 1, p. 7, 2015.
[7] K. M. Wisdom, S. L. Delp, and E. Kuhl, “Use it or lose
it: multiscale skeletal muscle adaptation to mechanical
stimuli,” Biomechanics and Modeling in Mechanobiol-
ogy, vol. 14, no. 2, pp. 195–215, 2015.
[8] J. Vanrenterghem, N. J. Nedergaard, M. A. Robinson,
and B. Drust, “Training Load Monitoring in Team
Sports: A Novel Framework Separating Physiologi-
cal and Biomechanical Load-Adaptation Pathways,”
Sports Medicine, 2017.
[9] S. Nimphius, M. R. McGuigan, and R. U. Newton,
“Changes in Muscle Architecture and Performance
During a Competitive Season in Female Softball Play-
ers,” Journal of Strength and Conditioning Research,
vol. 26, no. 10, pp. 2655–2666, 2012.
[10] J. L. Secomb, O. R. Farley, S. Nimphius, L. Lund-
gren, T. T. Tran, and J. M. Sheppard, “The training-
specific adaptations resulting from resistance training,
gymnastics and plyometric training, and non-training
in adolescent athletes,” Sports Science & Coaching,
vol. 12, no. 6, pp. 762–773, 2017.
[11] C. Couppe, M. Kongsgaard, P. Aagaard, P. Hansen,
J. Bojsen-Moller, M. Kjaer, and S. P. Magnusson, “Ha-
bitual loading results in tendon hypertrophy and in-
creased stiffness of the human patellar tendon,” Jour-
nal of Applied Physiology, vol. 105, no. 3, pp. 805–810,
2008.
[12] F. Mersmann, S. Bohm, A. Schroll, H. Boeth,
G. Duda, and A. Arampatzis, “Muscle and tendon
adaptation in adolescent athletes: A longitudinal
study,” Scandinavian Journal of Medicine & Science
in Sports, vol. 27, pp. 75–82, 2017.
[13] A. Esmaeili, A. M. Stewart, W. G. Hopkins, G. P.
Elias, and R. J. Aughey, “Effects of Training Load
and Leg Dominance on Achilles and Patellar Tendon
Structure,” International Journal of Sports Physiology
and Performance, vol. 12, pp. S2–122–S2–126, 2017.
[14] L. M. Rabello, J. Zwerver, R. E. Stewart, I. van den
Akker-Scheek, and M. S. Brink, “Patellar tendon
structure responds to load over a 7 - week preseason
in elite male volleyball players,” Scandinavian Journal
of Medicine & Science in Sports, pp. 1–8, 2019.
[15] M. Fredericson, K. Chew, J. Ngo, T. Cleek, J. Kiratli,
and K. Cobb, “Regional bone mineral density in male
athletes: a comparison of soccer players, runners and
controls,” British Journal of Sports Medicine, vol. 41,
pp. 664–668, 2007.
[16] E. Helge, T. Andersen, J. Schmidt, N. Jørgensen,
T. Hornstrup, P. Krustrup, and J. Bangsbo, “Recre-
ational football improves bone mineral density and
bone turnover marker profile in elderly men,” Scan-
dinavian Journal of Medicine & Science in Sports,
vol. 24, no. 1, pp. 98–104, 2014.
[17] W. B. Edwards, “Modeling Overuse Injuries in Sport
as a Mechanical Fatigue Phenomenon,” Exercise and
Sport Sciences Reviews, vol. 46, no. 4, pp. 224–231,
2018.
[18] M. Bertelsen, A. Hulme, J. Petersen, R. Brund,
H. Sørensen, C. Finch, E. Parner, and R. Nielsen,
“A framework for the etiology of running-related in-
juries,” Scandinavian Journal of Medicine and Science
in Sports, vol. 27, pp. 1170–1180, 2017.
[19] T. Wang, Z. Lin, R. E. Day, B. Gardiner, E. Landao-
Bassonga, J. Rubenson, T. B. Kirk, D. W. Smith,
D. G. Lloyd, G. Hardisty, A. Wang, Q. Zheng, and
M. H. Zheng, “Programmable mechanical stimulation
influences tendon homeostasis in a bioreactor system,”
Biotechnology and Bioengineering, vol. 110, no. 5,
pp. 1495–1507, 2013.
5

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
[20] L. L. Loundagin, T. A. Schmidt, and W. B. Ed-
wards, “Mechanical Fatigue of Bovine Cortical Bone
Using Ground Reaction Force Waveforms in Run-
ning,” Journal of Biomechanical Engineering, vol. 140,
pp. 031003–1–031003–5, jan 2018.
[21] F. Amirouche and A. Bobko, “Bone Remodeling and
Biomechanical Processes- A Multiphysics Approach,”
Austin Journal of Biotechnology & Bioengineering,
vol. 2, no. 2, p. id1041, 2015.
[22] D. W. Smith, J. Rubenson, D. Lloyd, M. Zheng, J. Fer-
nandez, T. Besier, J. Xu, and B. S. Gardiner, “A
conceptual framework for computational models of
Achilles tendon homeostasis,” WIREs Systems Biol-
ogy and Medicine, vol. 5, no. October, pp. 523–538,
2013.
[23] T. E. Hewett, G. D. Myer, K. R. Ford, R. S. Heidt,
A. J. Colosimo, S. G. McLean, A. J. Van den Bogert,
M. V. Paterno, and P. Succop, “Biomechanical Mea-
sures of Neuromuscular Control and Valgus Loading
of the Knee Predict Anterior Cruciate Ligament In-
jury Risk in Female Athletes: A Prospective Study,”
American Journal of Sports Medicine, vol. 33, no. 4,
pp. 492–501, 2005.
[24] C.-f. Lin, H. Liu, M. T. Gros, P. Weinhold, W. E.
Garrett, and B. Yu, “Biomechanical risk factors of
non-contact ACL injuries: A stochastic biomechanical
modeling study,” Journal of Sport and Health Science,
vol. 1, pp. 36–42, 2012.
[25] J. Verheul, A. C. Clansey, and M. J. Lake, “Ad-
justments with running speed reveal neuromuscu-
lar adaptations during landing associated with high
mileage running training,” Journal of Applied Physi-
ology, vol. 122, pp. 653–665, 2017.
[26] A. Arampatzis, G.-P. Br¨uggemann, and V. Metzler,
“The effect of speed on leg stiffness and joint kinetics
in human running,” Journal of Biomechanics, vol. 32,
pp. 1349–1353, 1999.
[27] J. B. Morin, P. Samozino, and G. Y. Millet, “Changes
in running kinematics, kinetics, and spring-mass be-
havior over a 24-h run,” Medicine and Science in
Sports and Exercise, vol. 43, no. 5, pp. 829–836, 2011.
[28] J. L. Oliver, M. B. De Ste, R. S. Lloyd, and
C. A. Williams, “Altered neuromuscular control of leg
stiffness following soccer-specific exercise,” European
Journal of Applied Physiology, vol. 114, pp. 2241–2249,
2014.
[29] S. J. Maloney and I. M. Fletcher, “Lower limb stiff-
ness testing in athletic performance: a critical review,”
Sports Biomechanics, vol. 3141, no. May, pp. 1–22,
2018.
[30] A. Seth, J. L. Hicks, T. K. Uchida, A. Habib, C. L.
Dembia, J. J. Dunne, C. F. Ong, M. S. DeMers, A. Ra-
jagopal, M. Millard, S. R. Hamner, E. M. Arnold, J. R.
Yong, S. K. Lakshmikanth, M. A. Sherman, J. P. Ku,
and S. L. Delp, “OpenSim: Simulating musculoskeletal
dynamics and neuromuscular control to study human
and animal movement,” PLoS Computational Biology,
vol. 14, no. 7, p. e1006223, 2018.
[31] S. H. Scott and D. A. Winter, “Internal forces at
chronic running injury sites,” Medicine and Science
in Sports and Exercise, vol. 22, no. 3, pp. 357–369,
1990.
[32] C. Pizzolato, D. J. Saxby, E. Ceseracciu, L. Mode-
nese, and D. G. Lloyd, “Biofeedback for gait retrain-
ing based on real-time estimation of tibiofemoral joint
contact forces,” Transactions on Neural Systems and
Rehabilitation Engineering, vol. 25, no. 9, pp. 1612–
1621, 2017.
[33] C. Pizzolato, M. Reggiani, L. Modenese, and D. G.
Lloyd, “Real-time inverse kinematics and inverse dy-
namics for lower limb applications using OpenSim,”
Computer Methods in Biomechanics and Biomedical
Engineering, vol. 20, no. 4, pp. 436–445, 2017.
[34] A. J. van den Bogert, T. Geijtenbeek, O. Even-Zohar,
F. Steenbrink, and E. C. Hardin, “A real-time system
for biomechanical analysis of human movement and
muscle function,” Medical and Biological Engineering
and Computing, vol. 51, pp. 1069–1077, 2013.
[35] K. Manal, K. Gravare-Silbernagel, and T. S.
Buchanan, “A real-time EMG-driven musculoskele-
tal model of the ankle,” Multibody System Dynamics,
vol. 28, pp. 169–180, 2012.
[36] S. Fukashiro, P. V. Komi, M. J¨arvinen, and
M. Miyashita, “In vivo achilles tendon loading’ dur-
ing jumping in humans,” European Journal of Applied
Physiology and Occupational Physiology, vol. 71, no. 5,
pp. 453–458, 1995.
[37] P. V. Komi, M. Salonen, M. J¨arvinen, and O. Kokko,
“In Vivo Registration of Achilles Tendon Forces in
Man. I. Methodological Development,” Internation
Journal of Sports Medicine, vol. 8, pp. 3–8, 1987.
[38] P. V. Komi, “Relevance of in vivo force measurements
to human biomechanics,” Journal of Biomechanics,
vol. 23, no. Suppl. 1, pp. 23–34, 1990.
[39] D. B. Burr, C. Milgrom, D. Fyhrie, M. Forwood,
M. Nyska, A. Finestone, S. Hoshaw, E. Saiag, and
A. Simkin, “In Vivo Measurement of Human Tib-
ial Strains During Vigorous Activity,” Bone, vol. 18,
no. 5, pp. 405–410, 1996.
[40] J. A. Martin, S. C. Brandon, E. M. Keuler, J. R. Her-
mus, A. C. Ehlers, D. J. Segalman, M. S. Allen, and
D. G. Thelen, “Gauging force by tapping tendons,”
6

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
Nature Communications, vol. 9, no. 1592, pp. 1–9,
2018.
[41] D. P. Raper, J. Witchalls, E. J. Philips, E. Knight,
M. K. Drew, and G. Waddington, “Use of a tibial ac-
celerometer to measure ground reaction force in run-
ning: A reliability and validity comparison with force
plates,” Journal of Science and Medicine in Sport,
vol. 21, no. 1, pp. 84–88, 2018.
[42] C. E. Milner, R. Ferber, C. D. Pollard, J. Hamill, and
I. S. Davis, “Biomechanical factors associated with tib-
ial stress fracture in female runners,” Medicine and
Science in Sports and Exercise, vol. 38, no. 2, pp. 323–
328, 2006.
[43] E. M. Hennig and M. A. Lafortune, “Relationships be-
tween ground reaction force and tibial bone accelera-
tion parameters,” Internation Journal of Sport Biome-
chanics, vol. 7, pp. 303–309, 1991.
[44] K. R. Sheerin, D. Reid, and T. F. Besier, “The mea-
surement of tibial acceleration in runners. A review
of the factors that can affect tibial acceleration dur-
ing running and evidence based guidelines for its use,”
Gait & Posture, vol. 67, pp. 12–24, 2019.
[45] R. Nagahara, M. Mizutani, A. Matsuo, H. Kanehisa,
and T. Fukunaga, “Association of sprint performance
with ground reaction forces during acceleration and
maximal speed phases in a single sprint,” Journal of
Applied Biomechanics, pp. 1–20, 2017.
[46] N. E. Bezodis, J. S. North, and J. L. Razavet, “Alter-
ations to the orientation of the ground reaction force
vector affect sprint acceleration performance in team
sports athletes,” Journal of Sports Sciences, vol. 35,
no. 18, pp. 1817–1824, 2017.
[47] G. Rabita, S. Dorel, J. Slawinski, E. S`aez-de Villarreal,
A. Couturier, P. Samozino, and J. B. Morin, “Sprint
mechanics in world-class athletes: A new insight into
the limits of human locomotion,” Scandinavian Jour-
nal of Medicine & Science in Sports, vol. 25, no. 5,
pp. 583–594, 2015.
[48] A. A. Zadpoor and A. A. Nikooyan, “The relation-
ship between lower-extremity stress fractures and the
ground reaction force: A systematic review,” Clinical
Biomechanics, vol. 26, pp. 23–28, 2011.
[49] E. S. Matijevich, L. M. Branscombe, L. R. Scott, and
K. E. Zelik, “Ground reaction force metrics are not
strongly correlated with tibial bone load when run-
ning across speeds and slopes: Implications for science,
sport and wearable tech,” Plos One, vol. 14, no. 1,
p. e0210000, 2019.
[50] H. van der Worp, J. W. Vrielink, and S. W. Bredeweg,
“Do runners who suffer injuries have higher vertical
ground reaction forces than those who remain injury-
free? A systematic review and meta-analysis.,” British
journal of sports medicine, vol. 50, no. 8, pp. 450–7,
2016.
[51] J. A. Ramirez-Bautista, J. A. Huerta-Ruelas, S. L.
Chaparro-C´ardenas, and A. Hern´andez-Zavala, “A Re-
view in Detection and Monitoring Gait Disorders Us-
ing In-Shoe Plantar Measurement Systems,” IEEE Re-
views in Biomedical Engineering, vol. 10, pp. 299–309,
2017.
[52] K. E. Renner, D. B. Williams, and R. M. Queen,
“The Reliability and Validity of the Loadsol under
Various Walking and Running Conditions,” Sensors,
vol. 19, no. 265, pp. 1–14, 2019.
[53] G. T. Burns, J. D. Zendler, and R. F. Zernicke, “Val-
idation of a wireless shoe insole for ground reaction
force measurement,” Journal of Sports Sciences, pp. 1–
10, 2018.
[54] W. Seiberl, E. Jensen, J. Merker, M. Leitel, and
A. Schwirtz, “Accuracy and precision of loadsol in-
sole force- sensors for the quantification of ground
reaction force-based biomechanical running parame-
ters parameters,” European Journal of Sport Science,
vol. 18, no. 8, pp. 1100–1109, 2018.
[55] J. Park, Y. Na, G. Gu, and J. Kim, “Flexible in-
sole ground reaction force measurement shoes for
jumping and running,” Proceedings of the IEEE RAS
and EMBS International Conference on Biomedi-
cal Robotics and Biomechatronics, vol. 2016-July,
pp. 1062–1067, 2016.
[56] A. T. Peebles, L. A. Maguire, K. E. Renner, and R. M.
Queen, “Validity and Repeatability of Single-Sensor
Loadsol Insoles during Landing,” Sensors, vol. 18,
no. 4082, pp. 1–10, 2018.
[57] G. Pavei, E. Seminati, D. Cazzola, and A. E. Minetti,
“On the estimation accuracy of the 3D body center
of mass trajectory during human locomotion: Inverse
vs. forward dynamics,” Frontiers in Physiology, vol. 8,
no. MAR, pp. 1–13, 2017.
[58] J. Verheul, J. Warmenhoven, P. J. Lisboa, W. Greg-
son, J. Vanrenterghem, and M. A. Robinson, “Identi-
fying generalised segmental acceleration patterns that
contribute to ground reaction force features across dif-
ferent running tasks,” Journal of Science and Medicine
in Sport, 2019.
[59] M. F. Bobbert, H. C. Schamhardt, and B. M. Nigg,
“Calculation of Vertical Ground Reaction,” Journal
of biomechanics, vol. 24, no. 2, pp. 1095–1105, 1991.
[60] N. J. Nedergaard, M. A. Robinson, E. Eusterwiemann,
B. Drust, P. J. Lisboa, and J. Vanrenterghem, “The
Relationship Between Whole-Body External Loading
and Body-Worn Accelerometry During Team-Sport
Movements,” International Journal of Sports Physiol-
ogy and Performance, vol. 12, no. 1, pp. 18–26, 2017.
7

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
[61] S. Edwards, S. White, S. Humphreys, R. Robergs,
and N. O’Dwyer, “Caution using data from triaxial
accelerometers housed in player tracking units during
running,” Journal of Sports Sciences, vol. 37, no. 7,
pp. 810–818, 2019.
[62] N. J. Nedergaard, J. Verheul, B. Drust, T. Etchells,
P. J. Lisboa, M. A. Robinson, and J. Vanrenterghem,
“The feasibility of predicting ground reaction forces
during running from a trunk accelerometry driven
mass-spring-damper model,” PeerJ, vol. 6, p. e6105,
2018.
[63] D. W. Wundersitz, K. J. Netto, B. Aisbett, and
P. B. Gastin, “Validity of an upper-body-mounted ac-
celerometer to measure peak vertical and resultant
force during running and change-of-direction tasks,”
Sports Biomechanics, vol. 12, no. 4, pp. 403–412, 2013.
[64] R. D. Gurchiek, R. S. McGinnis, A. R. Needle, J. M.
McBride, and H. van Werkhoven, “The use of a single
inertial sensor to estimate 3-dimensional ground reac-
tion force during accelerative running tasks,” Journal
of Biomechanics, vol. 61, pp. 263–268, 2017.
[65] J. M. Neugebauer, K. H. Collins, and D. A. Hawkins,
“Ground reaction force estimates from ActiGraph
GT3X+ hip accelerations,” PLoS ONE, vol. 9, no. 6,
p. e99023, 2014.
[66] P. Gaudino, C. Gaudino, G. Alberti, and A. E.
Minetti, “Biomechanics and predicted energetics of
sprinting on sand: Hints for soccer training,” Journal
of Science and Medicine in Sport, vol. 16, pp. 271–275,
2013.
[67] M. Buchheit, A. Gray, and J.-B. Morin, “Assess-
ing stride variables and vertical stiffness with GPS-
embedded accelerometers: preliminary insights for the
monitoring of neuromuscular fatigue on the field,”
Journal of Sports Science and Medicine, no. 14,
pp. 698–701, 2015.
[68] M. Buchheit, M. Lacome, Y. Cholley, and B. M. Simp-
son, “Neuromuscular Responses to Conditioned Soc-
cer Sessions Assessed via GPS-Embedded Accelerom-
eters: Insights Into Tactical Periodization,” Interna-
tional Journal of Sports Physiology and Performance,
vol. 13, pp. 577–583, 2018.
[69] R. M. Page, K. Marrin, C. M. Brogden, and M. Greig,
“Biomechanical and physiological response to a con-
temporary soccer match-play simulation,” Journal of
Strength and Conditioning Research, vol. 29, no. 10,
pp. 2860–2866, 2015.
[70] M. J. Colby, B. Dawson, J. Heasman, B. Rogalski,
and T. J. Gabbett, “Accelerometer and GPS-Derived
Running Loads and Injury Risk in Elite Australian
Footballers,” Journal of Strength and Conditioning
Research, vol. 28, no. 8, pp. 2244–2252, 2014.
[71] S. Barrett, A. Midgley, and R. Lovell, “Player-
Load : Reliability, convergent validity, and influence
of unit position during treadmill running,” Interna-
tional Journal of Sports Physiology and Performance,
vol. 9, no. 6, pp. 945–952, 2014.
[72] P. Gaudino, G. Alberti, and F. M. Iaia, “Estimated
metabolic and mechanical demands during different
small-sided games in elite soccer players,” Human
Movement Science, vol. 36, pp. 123–133, 2014.
[73] F. E. Ehrmann, C. S. Duncan, D. Sindhusake, W. N.
Franzsen, and D. A. Greene, “GPS and Injury Preven-
tion in Professional Soccer,” Journal of Strength and
Conditioning Research, vol. 30, no. 2, pp. 360–367,
2016.
[74] L. J. Boyd, K. Ball, and R. J. Aughey, “The reliabil-
ity of minimaxX accelerometers for measuring physical
activity in australian football,” International Journal
of Sports Physiology and Performance, vol. 6, pp. 311–
321, 2011.
[75] D. Roetenberg, H. Luinge, and P. Slycke, “Xsens
MVN: Full 6DOF Human Motion Tracking Using
Miniature Inertial Sensors,” tech. rep., Xsens Tech-
nologies, 2013.
[76] A. Karatsidis, G. Bellusci, M. H. Schepers, M. de Zee,
M. S. Andersen, and P. H. Veltink, “Estimation of
Ground Reaction Forces and Moments During Gait
Using Only Inertial Motion Capture,” Sensors, vol. 17,
no. 75, pp. 1–22, 2017.
[77] J. Konrath, A. Karatsidis, M. H. Schepers, G. Bel-
lusci, M. de Zee, and M. S. Andersen, “Estimation of
the Knee Adduction Moment and Joint Contact Force
during Daily Living Activities Using Inertial Motion
Capture,” Sensors, vol. 19, no. 1681, pp. 1–12, 2019.
[78] S. Corazza, L. M¨undermann, A. M. Chaudhari, T. De-
mattio, C. Cobelli, and T. P. Andriacchi, “A mark-
erless motion capture system to study musculoskele-
tal biomechanics: Visual hull and simulated annealing
approach,” Annals of Biomedical Engineering, vol. 34,
no. 6, pp. 1019–1029, 2006.
[79] G. D. Abrams, A. H. Harris, T. P. Andriacchi, and
M. R. Safran, “Biomechanical analysis of three tennis
serve types using a markerless system,” British Jour-
nal of Sports Medicine, vol. 48, no. 4, pp. 339–342,
2014.
[80] S. K. Fung, K. Sundaraj, N. U. Ahamed, L. C. Kiang,
S. Nadarajah, A. Sahayadhas, A. Ali, A. Islam, and
R. Palaniappan, “Hybrid markerless tracking of com-
plex articulated motion in golf swings,” Journal of
Bodywork & Movement Therapies, vol. 18, pp. 220–
227, 2014.
[81] J. Grigg, E. Haakonssen, E. Rathbone, R. Orr, and
J. W. Keogh, “The validity and intra-tester reliability
8

Measuring biomechanical loads doi: 10.1080/24733938.2019.1709654
of markerless motion capture to analyse kinematics of
the BMX Supercross gate start,” Sports Biomechan-
ics, vol. 17, no. 3, pp. 383–401, 2018.
[82] K. Saylor, D. Nicolella, D. Chambers, and B. Swenson,
“Markerless biomechanics analysis for optimization of
soldier physical performance,” Journal of Science and
Medicine in Sport, vol. 20S, p. S119, 2017.
[83] M. A. Perrott, T. Pizzari, J. Cook, and J. A. McClel-
land, “Comparison of lower limb and trunk kinematics
between markerless and marker-based motion capture
systems,” Gait and Posture, vol. 52, pp. 57–61, 2017.
[84] S. Skals, M. K. Jung, M. Damsgaard, and M. S. An-
dersen, “Prediction of ground reaction forces and mo-
ments during sports-related movements,” Multibody
System Dynamics, vol. 39, pp. 175–195, 2017.
[85] E. Halilaj, A. Rajagopal, M. Fiterau, J. L. Hicks, T. J.
Hastie, and S. L. Delp, “Machine Learning in Human
Movement Biomechanics: Best Practices, Common
Pitfalls, and New Opportunities,” Journal of Biome-
chanics, 2018.
[86] F. J. Wouda, M. Giuberti, G. Bellusci, E. Maartens,
J. Reenalda, B.-J. F. van Beijnum, and P. H. Veltink,
“Estimation of Vertical Ground Reaction Forces and
Sagittal Knee Kinematics During Running Using
Three Inertial Sensors,” Frontiers in Physiology, vol. 9,
pp. 1–14, 2018.
[87] W. R. Johnson, A. Mian, M. A. Robinson, J. Ver-
heul, D. G. Lloyd, and J. A. Alderson, “Multidimen-
sional ground reaction forces and moments from wear-
able sensor accelerations via deep learning,” arXiv
Preprint, 2019.
[88] B. J. Stetter, S. Ringhof, F. C. Krafft, S. Sell,
and T. Stein, “Estimation of Knee Joint Forces in
Sport Movements Using Wearable Sensors and Ma-
chine Learning,” Sensors, vol. 19, p. 3690, 2019.
[89] F. Doshi-Velez and B. Kim, “Towards A Rigorous
Science of Interpretable Machine Learning,” ArXiv
Preprint, vol. 1702.08608, 2017.
9