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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.
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 )
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-
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 (
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
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-
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,
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
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.
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Full-text available
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.
Full-text available
Objective To support future developments of field-based biomechanical load monitoring tools, this study aimed to identify generalised segmental acceleration patterns and their contribution to ground reaction forces (GRFs) across different running tasks. Design Exploratory experimental design. Methods A multivariate principal component analysis (PCA) was applied to a combination of segmental acceleration data from all body segments for fifteen team-sport athletes performing accelerated, decelerated and constant low-, moderate- and high-speed running, and 90° cutting trials. Segmental acceleration profiles were then reconstructed from each principal component (PC) and used to calculate their specific GRF contributions. Results The first PC explained 48.57% of the acceleration variability for all body segments and was primarily related to the between-task differences in the overall magnitude of the GRF impulse. Magnitude and timing of high-frequency acceleration and GRF features (i.e. impact related characteristics) were primarily explained by the second PC (12.43%) and also revealed important between-task differences. The most important GRF characteristics were explained by the first five PCs, while PCs beyond that primarily contained small contributions to the overall GRF impulse. Conclusions These findings show that a multivariate PCA approach can reveal generalised acceleration patterns and specific segmental contributions to GRF features, but their relative importance for different running activities are task dependent. Using segmental acceleration to assess whole-body biomechanical loading generically across various movements may thus require task identification algorithms and/or advanced sensor or data fusion approaches.
Full-text available
Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed to estimate the knee adduction moment and tibiofemoral joint contact force during daily living activities using a musculoskeletal model with inertial motion capture derived kinematics in an elderly population. Eight elderly participants were instrumented with 17 inertial measurement units, as well as 53 opto-reflective markers affixed to anatomical landmarks. Participants performed stair ascent, stair descent, and sit-to-stand movements while both motion capture methods were synchronously recorded. A musculoskeletal model containing 39 degrees-of-freedom was used to estimate the knee adduction moment and tibiofemoral joint contact force. Strong to excellent Pearson correlation coefficients were found for the IMC-derived kinematics across the daily living tasks with root mean square errors (RMSE) between 3° and 7°. Furthermore, moderate to strong Pearson correlation coefficients were found in the knee adduction moment and tibiofemoral joint contact forces with RMSE between 0.006–0.014 body weight × body height and 0.4 to 1 body weights, respectively. These findings demonstrate that inertial motion capture may be used to estimate knee adduction moments and tibiofemoral contact forces with comparable accuracy to optical motion capture.
Full-text available
The purpose of this study was to investigate the relation between external and internal load and the response of the patellar tendon structure assessed with Ultrasound Tissue Characterization (UTC) in elite male volleyball players during preseason. Eighteen players were followed over seven weeks, measuring four load parameters during every training and match: volume (minutes played), Rating of Perceived Exertion (RPE) (ranging from 6 to 20), weekly load (RPE*volume) and jump frequency (number of jumps). Patellar tendon structure was measured biweekly using UTC, which quantifies tendon matrix stability resulting in four different echo types (I‐IV). On average, players spent 615 minutes per week on training and matches with an RPE of 13.9 and a jump frequency of 269. Load evaluation shows significant changes over the seven weeks: volume and weekly load parameters were significantly higher in week 3 than week 7 and in week 4 than week 2. Weekly load performed in week 4 was significantly higher than week 7. No significant changes were observed in tendon structure. On the non‐dominant side no significant correlations were found between changes in load parameters and echo types. At the dominant side a higher weekly volume and weekly load resulted in a decrease of echo type I and a higher mean RPE in an increase of echo type II. The results of this study show that both external and internal load influence changes in patellar tendon structure of elite male volleyball players. Monitoring load and the effect on patellar tendon structure may play an important role in injury prevention. This article is protected by copyright. All rights reserved.
Full-text available
Introduction Tibial stress fractures are a common overuse injury resulting from the accumulation of bone microdamage due to repeated loading. Researchers and wearable device developers have sought to understand or predict stress fracture risks, and other injury risks, by monitoring the ground reaction force (GRF, the force between the foot and ground), or GRF correlates (e.g., tibial shock) captured via wearable sensors. Increases in GRF metrics are typically assumed to reflect increases in loading on internal biological structures (e.g., bones). The purpose of this study was to evaluate this assumption for running by testing if increases in GRF metrics were strongly correlated with increases in tibial compression force over a range of speeds and slopes. Methods Ten healthy individuals performed running trials while we collected GRFs and kinematics. We assessed if commonly-used vertical GRF metrics (impact peak, loading rate, active peak, impulse) were strongly correlated with tibial load metrics (peak force, impulse). Results On average, increases in GRF metrics were not strongly correlated with increases in tibial load metrics. For instance, correlating GRF impact peak and loading rate with peak tibial load resulted in r = -0.29±0.37 and r = -0.20±0.35 (inter-subject mean and standard deviation), respectively. We observed high inter-subject variability in correlations, though most coefficients were negligible, weak or moderate. Seventy-six of the 80 subject-specific correlation coefficients computed indicated that higher GRF metrics were not strongly correlated with higher tibial forces. Conclusions These results demonstrate that commonly-used GRF metrics can mislead our understanding of loading on internal structures, such as the tibia. Increases in GRF metrics should not be assumed to be an indicator of increases in tibial bone load or overuse injury risk during running. This has important implications for sports, wearable devices, and research on running-related injuries, affecting >50 scientific publications per year from 2015–2017.
Full-text available
The assessment of loading during walking and running has historically been limited to data collection in laboratory settings or with devices that require a computer connection. This study aims to determine if the loadsol®—a single sensor wireless insole—is a valid and reliable method of assessing force. Thirty (17 male and 13 female) recreationally active individuals were recruited for a two visit study where they walked (1.3 m/s) and ran (3.0 and 3.5 m/s) at a 0%, 10% incline, and 10% decline, with the visits approximately one week apart. Ground reaction force data was collected on an instrumented treadmill (1440 Hz) and with the loadsol® (100 Hz). Ten individuals completed the day 1 protocol with a newer 200 Hz loadsol®. Intraclass correlation coefficients (ICC3,k) were used to assess validity and reliability and Bland–Altman plots were generated to better understand loadsol® validity. Across conditions, the peak force ICCs ranged from 0.78 to 0.97, which increased to 0.84–0.99 with the 200 Hz insoles. Similarly, the loading rate ICCs improved from 0.61 to 0.97 to 0.80–0.96 and impulse improved from 0.61 to 0.97 to 0.90–0.97. The 200 Hz insoles may be needed for loading rate and impulse in running. For both walking and running, the loadsol® has excellent between-day reliability (>0.76).
Full-text available
Background Monitoring the external ground reaction forces (GRF) acting on the human body during running could help to understand how external loads influence tissue adaptation over time. Although mass-spring-damper (MSD) models have the potential to simulate the complex multi-segmental mechanics of the human body and predict GRF, these models currently require input from measured GRF limiting their application in field settings. Based on the hypothesis that the acceleration of the MSD-model’s upper mass primarily represents the acceleration of the trunk segment, this paper explored the feasibility of using measured trunk accelerometry to estimate the MSD-model parameters required to predict resultant GRF during running. Methods Twenty male athletes ran at approach speeds between 2–5 m s ⁻¹ . Resultant trunk accelerometry was used as a surrogate of the MSD-model upper mass acceleration to estimate the MSD-model parameters (ACC param ) required to predict resultant GRF. A purpose-built gradient descent optimisation routine was used where the MSD-model’s upper mass acceleration was fitted to the measured trunk accelerometer signal. Root mean squared errors (RMSE) were calculated to evaluate the accuracy of the trunk accelerometry fitting and GRF predictions. In addition, MSD-model parameters were estimated from fitting measured resultant GRF (GRF param ), to explore the difference between ACC param and GRF param . Results Despite a good match between the measured trunk accelerometry and the MSD-model’s upper mass acceleration (median RMSE between 0.16 and 0.22 g), poor GRF predictions (median RMSE between 6.68 and 12.77 N kg ⁻¹ ) were observed. In contrast, the MSD-model was able to replicate the measured GRF with high accuracy (median RMSE between 0.45 and 0.59 N kg ⁻¹ ) across running speeds from GRF param . The ACC param from measured trunk accelerometry under- or overestimated the GRF param obtained from measured GRF, and generally demonstrated larger within parameter variations. Discussion Despite the potential of obtaining a close fit between the MSD-model’s upper mass acceleration and the measured trunk accelerometry, the ACC param estimated from this process were inadequate to predict resultant GRF waveforms during slow to moderate speed running. We therefore conclude that trunk-mounted accelerometry alone is inappropriate as input for the MSD-model to predict meaningful GRF waveforms. Further investigations are needed to continue to explore the feasibility of using body-worn micro sensor technology to drive simple human body models that would allow practitioners and researchers to estimate and monitor GRF waveforms in field settings.
Full-text available
Clinically feasible methods for quantifying landing kinetics could help identify patients at risk for secondary anterior cruciate ligament injuries. The purpose of this study was to evaluate the validity and between-day repeatability of the loadsol insole during a single-hop and bilateral stop-jump. Thirty healthy recreational athletes completed seven single-hops and seven stop-jumps while simultaneous loadsol (100 Hz) and force plate (1920 Hz) measurements were recorded. Peak impact force, loading rate, and impulse were computed for the dominant limb, and limb symmetry was calculated between limbs for each measure. All outcomes were compared between the loadsol and force plate using intraclass correlation coefficients (ICC) and Bland–Altman plots. Fifteen participants completed a second day of testing to assess between-day repeatability of the loadsol. Finally, an additional 14 participants completed the first day of testing only to assess the validity of the newest generation loadsol, which sampled at 200 Hz. At 100 Hz, validity ICC results were moderate to excellent (0.686–0.982), and repeatability ICC results were moderate to excellent (0.616–0.928). The 200 Hz loadsol demonstrated improved validity ICC (0.765–0.987). Bland–Altman plots revealed that the loadsol underestimated load measures. However, this bias was not observed for symmetry outcomes. The loadsol device is a valid and repeatable tool for evaluating kinetics during landing.
Full-text available
Ground reaction force measurements are a fundamental element of kinetic analyses of locomotion, yet they are traditionally constrained to laboratory settings or stationary frames. This study assessed the validity and reliability of a new wireless in-shoe system (Novel Loadsol/Pedoped) for field-based ground reaction force measurement in hopping, walking, and running. Twenty participants bilaterally hopped on a force plate and walked (5 km/hr) and ran (10 km/hr) on an instrumented treadmill on two separate days while wearing the insoles. GRFs were recorded simultaneously on each respective system. Peak GRF in hopping and peak GRF, contact time (CT), and impulse (IMP) in walking and running were compared on a per-hop and step-by-step basis. In hopping, the insoles demonstrated excellent agreement with the force plate (ICC: 0.96). In walking and running, the insoles demonstrated good-to-excellent agreement with the treadmill across all measures (ICCs: 0.88–0.96) and were reliable across sessions (ICCs within 0.00–0.03). A separate verification study with ten participants was conducted to assess a correction algorithm for further agreement improvement but demonstrated little meaningful change in systemic agreements. These results indicated that the Novel Loadsol system is a valid and reliable tool for wireless ground reaction force measurement in hopping, walking, and running.
The goals of this study were to examine the following hypotheses: (a) there is a difference between the theoretically calculated (McMahon and Cheng, 1990. Journal of Biomechanics 23, 65-78) and the kinematically measured length changes of the spring-mass model and (b) the leg spring stiffness, the ankle spring stiffness and the knee spring stiffness are influenced by running speed. Thirteen athletes took part in this study. Force was measured using a "Kistler" force plate (1000 Hz). Kinematic data were recorded using two high-speed (120 Hz) video cameras. Each athlete completed trials running at five different velocities (approx. 2.5, 3.5, 4.5, 5.5 and 6.5 m/s). Running velocity influences the leg spring stiffness, the effective vertical spring stiffness and the spring stiffness at the knee joint. The spring stiffness at the ankle joint showed no statistical difference (p < 0.05) for the five velocities. The theoretically calculated length change of the spring-mass model significantly (p < 0.05) overestimated the actual length change. For running velocities up to 6.5 m/s the leg spring stiffness is influenced mostly by changes in stiffness at the knee joint.