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The use of electromyography (EMG) is widely recognised as a valuable tool for enhancing the understanding of performance drivers and potential injury risk in sprinting. The timings of muscle activations relative to running gait cycle phases and the technology used to obtain muscle activation data during sprinting are of particular interest to scientists and coaches. This review examined the main muscles being analysed by surface EMG (sEMG), their activations and timing, and the technologies used to gather sEMG during sprinting. Electronic databases were searched using ‘Electromyography’ OR ‘EMG’ AND ‘running’ OR ‘sprinting’. Based on inclusion criteria, 18 articles were selected for review. While sEMG is widely used in biomechanics, relatively few studies have used sEMG in sprinting due to system constraints. The results demonstrated a focus on the leg muscles, with over 70% of the muscles analysed in the upper leg. This is consistent with the use of tethered and data logging EMG systems and many sprints being performed on treadmills. Through the recent advances in wireless EMG technology, an increase in the studies on high velocity movements such as sprinting is expected and this should allow practitioners to perform the analysis in an ecologically valid environment.
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SPORTS BIOMECHANICS, 2016
http://dx.doi.org/10.1080/14763141.2016.1252790
Muscle Activity in Sprinting: A Review
Róisín M. Howard12, Richard Conway1, and Andrew J. Harrison2
1Department of Electronic & Computer Engineering, University of Limerick, Limerick,
Ireland; 2Biomechanics Research Unit, University of Limerick, Limerick, Ireland
Email: roisin.howard@ul.ie
Abstract
The use of electromyography (EMG) is widely recognised as a valuable tool for enhancing
the understanding of performance drivers and potential injury risk in sprinting. The timings of
muscle activations relative to running gait cycle phases and the technology used to obtain
muscle activation data during sprinting are of particular interest to scientists and coaches.
This review examined, the main muscles being analysed by surface EMG (sEMG), their
activations and timing, and the technologies used to gather sEMG during sprinting.
Electronic databases were searched using ‘Electromyography’ OR ‘EMG’ AND ‘running’
OR ‘sprinting’. Based on inclusion criteria, 18 articles were selected for review. While sEMG
is widely used in biomechanics, relatively few studies have used sEMG in sprinting due to
system constraints. The results demonstrated a focus on the leg muscles, with over 70% of
the muscles analysed in the upper leg. This is consistent with the use of tethered and data
logging EMG systems and many sprints being performed on treadmills. Through the recent
advances in wireless EMG technology an increase in the studies on high velocity movements
such as sprinting is expected and this should allow practitioners to perform the analysis in an
ecologically valid environment.
Keywords Gait/Locomotion, Running, EMG, Track Events, Injury
Introduction
In sports biomechanics, EMG analysis provides important information on muscle activity
which may be useful in optimising performance or reducing the likelihood of sports injuries
(Ditroilo et al., 2011; Nummela, Rusko, & Mero, 1994; Paul & Wood, 2002). This is crucial
for athletes such as sprinters, since the likelihood of injury increases with running speed
(Higashihara, Ono, Kubota, Okuwaki & Fukubayashi, 2010; Schache, Dorn, Blanch, Brown
& Pandy, 2012; Yu et al., 2008). Sports performance monitoring for injury prevention is
very important for athletes and their coaches as potentially the risk of injury may be increased
with an increase in speed and due to muscle fatigue. Identification of the specific effects of
fatigue on muscle activation may provide important insights about specific injury
mechanisms in sprinting (Thelen, Chumanov, Best, Swanson, & Heiderscheit, 2005; Yu et
al., 2008). Utilising EMG to provide information on muscle activity can be useful in
examining changes across increases in speed or muscle fatigue. Many features of the EMG
signal have been associated with fatigue or speed, especially the amplitude of the EMG
signal. Of particular importance are the average EMG (AEMG) which calculates the average
amplitude of the rectified EMG signal and the integrated EMG (iEMG) which calculates the
total accumulated activity of the muscle. An increase in either the AEMG or iEMG has been
reported to be associated with an increase in muscular fatigue (Nummela, Vuorimaa &
Rusko, 1992; Nummela et al., 1994), while also having a positive association with increasing
running speeds (Chumanov, Heiderscheit & Thelen, 2007; Higashihara et al., 2010).
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While many studies have examined applications of EMG in gait, relatively few have
examined muscle activity in sprinting. This could be due to the many challenges associated
with gathering accurate EMG data in sprinting. The demands of sprinting require EMG data
to be acquired in an unobtrusive way, therefore the EMG sensor design needs to minimise
encumbrances on the athlete during sprinting. Any change in the way in which an athlete
normally performs a sprint could result in unreliable data being gathered. To reduce
discomfort and avoid invasive procedures, the majority of dynamic movements are analysed
using sEMG. With advances in technology, sEMG measurements have evolved from
tethered systems to data loggers (wireless telemetry) and more recently, to fully wireless
systems. For the analysis of sprinting, wireless systems are particularly useful since they do
not constrain the movement and facilitate ecologically valid data capture, such as the athlete
sprinting on a track rather than on a treadmill in a laboratory setting (Baur, Hirschmuller,
Muller, Gollhofer, & Mayer, 2007; Savelberg, Vorstenbosch, Kamman, van de Weijer, &
Schambardt, 1998; Van Caekenberghe, Segers, Willems, et al., 2013).
To advance technical knowledge of coaches and athletes, there is a need to understand
muscle activations sequences and timing in sprinting, and wireless EMG data could augment
understanding of sprinting together with the existing kinematics and kinetic analyses of
sprinting derived from many studies. Since the muscles generate the forces required for
running there is a particular need to gain knowledge of the timings and sequencing of muscle
activity in unrestricted sprinting across the phases of the running gait cycle. With the advent
of wireless technology, an increase in studies using sEMG in overground sprinting is
expected. Therefore a review of existing knowledge of EMG in sprinting is necessary to
determine the patterns of muscle activations during sprinting as it is vitally important to
understand the muscles involved and how they act to produce an effective sprint running
action since a full understanding of the biomechanics of sprinting requires analysis of
movement, force generation and muscle action. A review of sEMG technologies and their
applications in sprint analysis is also important and could highlight how the current
knowledge base can be used most effectively in new sEMG studies of sprinting to identify
specific areas for future research. Consequently, the primary aim of this study was to
examine the various muscles analysed during sprinting highlighting where the focus has
been, which muscles are important for sprinting in terms of sequencing and timings of
activations and the changes in muscle activity levels as a function of running speed. The
secondary aim was to understand the various technologies used for sEMG in sprinting, to
identify the key features of these systems and examine their relative merits and limitations in
the analysis of sprinting.
Methods
This review was limited to articles where sEMG data was collected on participants
performing maximal sprint trials. Sprinting was defined as any distance up to and including
400 m, with only the maximal velocity part of sprinting being included in analysis (speeds
above 7 m/s). Scopus, ScienceDirect, and Web of Science were searched to identify studies
which utilised surface Electromyography in sprinting. The following keywords/combinations
were used in searches: (1) ‘Electromyography’ OR ‘EMG’ AND (2) ‘running’ OR
‘sprinting’. After the initial search results returned over 1200 citations the advanced search
option was used. The inclusion criteria was defined as (1) articles written in English, (2) the
source types were journals with books and conference proceedings being excluded, (3) the
articles were published in the period from January 2000 to December 2014 and (4) the paper
type was an article (review papers were excluded). A final search of ‘surface EMG’ was
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performed on the results and this identified 418 articles. The titles of the articles were
subsequently reviewed with the inclusion criteria: (1) surface EMG measurements were
acquired, (2) sprinting was performed and, (3) participants were human. Duplicates acquired
from multiple databases were also excluded and this identified 36 articles. The reference lists
of these articles were examined to identify any important articles not found in the previous
search (28 extra articles were identified) and finally the full papers were examined of all
remaining articles. Articles needed to include surface EMG measurements on participants
while they were performing maximal sprints, those which did not meet the inclusion criteria
were excluded. Articles on the sprint start were excluded because these articles focused only
the start and acceleration phases and therefore the athletes would not have been sprinting at
maximum velocity. On completion of this process, a total of 18 articles were identified
which met all inclusion criteria. Additional databases such as Google Scholar, PubMed and
Research Gate were examined under the same search criteria. The first 50 results were
examined and no new papers satisfying the above criteria were found. A flow chart outlining
selection and exclusion of articles is provided in Figure 1.
The key phases of the running gait cycle, adapted from (Novacheck, 1998; Nummela, et al.,
1994; Pinniger, Steele, & Groeller, 2000; Yu et al., 2008) are defined as follows for this study
(see Figure 2):
1. The Early Stance (Braking) Phase: This phase begins as the foot makes initial
contact (IC) and ends at the mid-stance phase, estimated at 0 15% of the cycle.
2. The Late Stance (Propulsion) Phase: This phase begins at the mid-stance phase
and ends at the toe off (TO), estimated at 15 30% of the cycle.
3. The Early & Middle Swing (Recovery) Phase: This phase begins at TO and ends
roughly two thirds of the way through the swing phase, estimated at 30 77% of
the cycle.
4. The Late Swing (Pre-activation) Phase: This phase begins roughly two thirds of
the way through the swing phase and ends at the IC, estimated at 77 100% of the
cycle.
The 18 articles were examined under two headings: (1) Muscle activations and timings in
sprinting and (2) EMG systems and specifications. The muscles activation timings were
compared across the key phases of running gait as defined above. The review papers were
analysed to compare and contrast the timings (Chumanov et al., 2007; Higashihara et al.,
2010; Kuitunen, Komi, & Kyröläinen, 2002; Kyröläinen, Avela, & Komi, 2005; Mero &
Komi, 1987; Pinniger et al., 2000; Thelen et al., 2005; Yu et al, 2008), EMG timings from the
review paper on the biomechanics of running (Novacheck, 1998) were also included to
provide more detailed results on timings of muscle activation. Ensemble means of the
muscle activation timings were derived and these were used to create a prolife of the phasic
muscle activity across the running gait cycle. Muscle groups included in the profile were
based on the muscle groups where clear data was given in the papers reviewed and only
muscles which had timings across the entire gait cycle were included.
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Figure 1. Flow chart outlining the inclusion criteria for articles reviewed
Figure 2. The key phases of the running gait cycle
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Results
Study design and sample
Within the 18 selected articles, 204 participants (73 sprinters, 47 distance runners, 26
recreational runners, 12 footballers and 46 mixed sports or unknown) were tested with 11 ±5
participants per study. On average 5 ±3 trials of EMG data gathered during sprinting were
performed by each participant in each study, with a total of 60 ±55 sprinting trials completed
by all the participants in each study. A total of 1107 trials were therefore examined over all
studies. The mean maximum sprint velocity across all articles was 8.50 ± 0.89 m/s. Table 1
provides a complete summary of these data. Further information on the purpose and
outcomes of each of the studies is summarised in Table 2.
Table 1. Participant information from the selected 18 review papers
Number of participants per
study
Number of trials
per participant
Total number of
trials per study
Sprint
speed
(m/s)
Participants
sport
Female
Total
Albertus-Kajee et
al.(2011)
12*
2
24
NS1
Middle
distance
Ball & Scurr
(2011)
16
3
48
NS1
Recreational
Ball & Scurr
(2008)
16
3
48
NS1
NS1
Bartlett et
al.(2013)
5
10
5
50
NS1
Recreational
Chumanov et
al.(2007)
5*
5
25
NS1
NS1
Higashihara et
al.(2010)
8
4
32
8.50 ±0.14
Sprinters
Kuitunen et
al.(2002)
10
4
40
9.73
Sprinters
Kyröläinen et
al.(2005)
17
3
51
8.50 ±0.57
Middle
distance
Mastalerz et
al.(2012)
4
4
16
NS1
Sprinters
Mero & Komi
(1987)
8
19
10
190
10.16
±0.15
9.78 ±0.42
8.77 ±0.30
Sprinters
Nummela et
al.(1992)
6
5
30
8.17 ±0.31
Sprinters
Nummela et
al.(1994)
10
6
60
9.23 ±0.59
Sprinters
Nummela et
al.(2008)
18
5
90
7.61 ±0.44
Middle
distance
Pinniger et
al.(2000)
12
16
192
NS1
Footballers
Schache et
al.(2012)
2
7
1
7
8.95 ±0.70
Sprinters
Slawinski et
al.(2008)
8
9
1
9
7.56 ±0.41
Sprinters
Thelen et
al.(2005)
5
5
25
NS1
NS1
Yu et al.(2008)
20
7
140
7.77 ±0.11
Mixed
23
204
89
1077
8.50 ±0.89
*Gender of participants not disclosed.
1Not specified (NS) by the authors.
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Table 2. Study information from the selected 18 review papers
Participant EMG
characteristics
Purpose of study
Sprint trial description
Outcome measures
Results of study
Albertus-Kajee
et al.(2011)
EMG of right leg
Normalisation methods
(3 Days) 2 x 20m max sprint: 140m
indoor track*
RMS and peak
EMG
Sprint and MVC most repeatable methods
Ball & Scurr
(2011)
EMG of dominant
leg
Normalisation methods for
20m sprint
(3 Days) 3 x 20m max sprint: indoor
sports hall*
RMS & peak EMG
Normalise to peak in sprint or squat jump
Ball & Scurr
(2008)
Unilateral EMG
measures
Reliability and standardisation
of normalisation methods
(3 Days) 3 x 20m max sprint: indoor
sports hall*
RMS and peak
EMG
Sprint and squat jump methods
Bartlett et
al.(2013)
EMG of right leg
Activity of gluteal muscles in
walk, run, sprint & climb
5 x 30m max sprint:30m runway*
RMS & peak EMG
Gluteal activity changes with increased speed
Chumanov et
al.(2007)
EMG of right leg
Effects of speed on hamstring
muscle mechanics
80%, 85%, 90%, 95% & 100% of max
velocity: treadmill
Linear Envelope
Increase in peak hamstring activity with increase
in speed
Higashihara et
al.(2010)
Unilateral EMG
measures
Hamstring muscle activity at
different running speeds
50%, 75%, 85% & 95% of max velocity:
high speed treadmill
RMS & peak time
of maximum
activity
Significant difference in activation patterns as
speed increases
Kuitunen et
al.(2002)
EMG of right leg
muscles
Examine ankle and knee joint
stiffness during sprinting
70% - 100% (4 sprints) of max velocity,
accelerate to photocells (10m apart)
Smoothed EMG (15
point average) &
Average EMG
Ankle stiffness remained constant, knee joint
stiffness increased with running speed
Kyröläinen et
al.(2005)
Unilateral EMG
measures
Changes in muscle activations
as speed increases
5 submaximal sprints & 3 x 30m max:
200m indoor track*
Average EMG
Increase in activity of all muscles with increase in
speed
Mastalerz et
al.(2012)
EMG of right &
left legs
Represent fatigue in EMG
profile across different run
intensities
4 x 400m (90s, 70s, 60s & max): tartan
athletics track
MPF & FFT
Greater fatigue in left leg compared to right
Mero & Komi
(1987)
Unilateral EMG
measures
Find relationship between
EMG and contact forces in
sprinting
2 runs x 5 speeds: indoor hall*
iEMG & peak EMG
Peak activity was shown in all muscles except the
RF at braking phase of ipsi-lateral contact
Nummela et
al.(1992)
EMG of right leg
Neural activation changes
across speed in 400m sprint
(iEMG)
(2 Days) 20m max sprint & 400m &
200m (Day 1) & 100m & 300m (Day 2):
indoor running track (flying start for all
runs)*
iEMG
Fatigue in 400m running is mainly due to skeletal
muscles rather than the central nervous system
Nummela et
al.(1994)
EMG of right leg
EMG activities in fatigued and
non-fatigued sprinting
(2 Days) 20m max sprint (40m flying
start) & 400m time trial (Day 1) & 3/4
submaximal 20m (Day 2): outdoor
running track
Average EMG
The increased neural activation was due to
muscular fatigue
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Nummela et
al.(2008)
EMG of right leg
Fatigue induced changes
3-5x 20m max sprints (15m running
start): indoor running track*
Average EMG
Fatigue in 5km running at maximum effort was
related to sprint performance
Pinniger et
al.(2000)
Unilateral EMG
measures
Effects of hamstring fatigue
induced by maximum effort
during maximum sprint
3 x 40m max sprint (non-fatigued); 10
maximal 40m sprints hamstring fatigue
task; 3 x40m max sprint (fatigued)*
Linear Envelope
Increased duration of hamstring activity and
earlier offset of RF during swing phase
Schache et
al.(2012)
Unilateral EMG
measures
Differences in each hamstring
muscle during sprint
20m sprint: 110m indoor synthetic
running track
Average EMG
Peak musculotendon force and strain for the
hamstrings occurred around the same time as
terminal swing, this may be when hamstrings are
at greatest risk of injury
Slawinski et
al.(2008)
Unilateral EMG
measures
Muscle activity during
inclined and level training
300m max sprint: indoor/outdoor running
track*
RMS & iEMG
A lower velocity in the inclined sprinting results in
a decrease in hamstring activity
Thelen et
al.(2005)
Unilateral EMG
measures
Mechanics of hamstring
during swing phase of
sprinting
80% - 100% of max velocity: treadmill
Linear Envelope
Increase in excitation of BF at 70 80% of
running gait cycle until the end of the swing phase
Yu et al.(2008)
EMG of dominant
leg
Mechanics of hamstring
muscle strain injuries during
overground sprinting
7 sprint trails with a 10m run up to
calibration zone
Linear envelope
across running gait
cycle
Hamstrings were active during entire running
cycle, maximum activations occurred during the
early stance phase and late swing phase.
*Partial study information; only the maximum sprint trials are accounted for
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Muscle activations and timings in sprinting
Muscles analysed
The results demonstrated a focus on the hamstrings and quadriceps muscle groups in the
papers reviewed (see Table 3). 14 of the 18 articles analysed the biceps femoris (BF), seven
analysed the medial hamstrings. 12 of the 18 articles analysed the rectus femoris (RF), 10
analysed the vastus lateralis (VL) and five analysed the vastus medialis (VM). Two of the
18 articles analysed the gastrocnemius (GA), however 10 specifically analysed the medial
gastrocnemius (MG) and three analysed the lateral gastrocnemius (LG). Of the 18 articles,
five analysed the gluteus maximus (GMAX) and one analysed the gluteus medialis (GMED).
Four of the 18 articles analysed the soleus (SOL) and the tibialis anterior (TA). 77 muscles
were analysed in total across all the articles reviewed. Of these, 35% of the 77 muscles
analysed were quadriceps; 27% were hamstrings, 25% were calves, 8% were gluteal muscles
and 5% were TA. Over 70% of the 77 muscles analysed were the upper leg muscles with less
than 30% of those analysed being from the lower leg muscles.
Table 3. Muscles studied during sprinting using sEMG
Muscles
Biceps Femoris (BF)
Gastrocnemius (GA)
Gluteus Maximus (GMAX)
Gluteus Medius (GMED)
Medial Hamstrings (MH) ST & SM
Lateral Gastrocnemius (LG)
Medial Gastrocnemius (MG)
Rectus Femoris (RF)
Soleus (SOL)
Tibialis Anterior (TA)
Vastus Lateralis (VL)
Vastus Medialis (VM)
Bartlett et al.(2013)
Mastalerz et al.(2012)
Schache et al.(2012)
Albertus-Kajee et
al.(2011)
Ball & Scurr (2011, 2008)
Higashihara et al.(2010)
Nummela et al.(2008,
1994, 1992)
Slawinski et al.(2008)
Yu et al.(2008)
Chumanov et al.(2007)
Kyröläinen et al.(2005)
Thelen et al.(2005)
Kuitunen et al.(2002)
Pinniger et al.(2000)
Mero & Komi (1987)
Muscles activation timings
The muscle activation timings of the lower limbs are presented in Figure 3. The periods of
muscle activity were identified using the timings gathered from the review papers which gave
timing details (Chumanov et al., 2007; Higashihara et al., 2010; Kuitunen et al., 2002;
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Kyröläinen et al., 2005; Mero & Komi, 1987; Pinniger et al., 2000; Thelen et al., 2005; Yu et
al, 2008) and the biomechanics of running paper by Novacheck (1998).
Figure 3. The muscle activation timings of the lower limbs during sprinting across the gait cycle as a percentage of time.
Timings gathered from Chumanov et al. (2007), Higashihara et al. (2010), Kuitunen et al. (2002), Kyröläinen et al. (2005),
Mero and Komi (1987), Novacheck (1998), Pinniger et al. (2000), Thelen et al. (2005), and Yu et al. (2008). The light grey
areas represent periods where there is muscle activity. The error bars in the plot represent the SD of the mean onset and
termination times which were gathered.
Muscle activation timings in the stance phase
Figure 3 shows that the hamstrings were active through the stance phase (Higashihara et al.,
2010; Pinniger et al., 2000; Yu et al, 2008). An earlier peak activation of the BF than the ST
during the stance phase was found (Higashihara et al., 2010). The quadriceps muscle group
were also active in the stance phase, which was consistent with Pinniger et al. (2000). Peak
activity of the gluteus maximus (GMAX) was found at foot strike, with activity in the early
stance phase (Bartlett, Sumner, Ellis, & Kram, 2013; Kyröläinen et al., 2005). It can also be
observed in Figure 3 that the GA was active in stance phase (Kuitunen et al, 2002;
Kyröläinen et al., 2005; Mero & Komi, 1987; Pinniger et al., 2000) and the SOL was active
in the braking (early stance) phase, with the peak activity occurring after the initial contact
(Kuitunen et al, 2002). The TA also produced activity in the early stance phase in Figure 3
(Kuitunen et al., 2002; Kyröläinen et al., 2005; Mero & Komi, 1987).
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Muscle activation timings in the swing phase
Figure 3 also shows the hamstrings are active in the late swing phase (Chumanov et al., 2007;
Higashihara et al., 2010; Pinniger et al., 2000; Thelen et al., 2005; Yu et al, 2008). It can be
observed from Figure 3 that the RF had two clear bursts of activity, one in the early swing
phase and a second in late swing phase. The VL was also active in the late swing phase
(Pinniger et al.; 2000). Muscle activity was observed in the GMAX in the late swing phase
(Kyröläinen et al.; 2005) and as outlined in Figure 3 the GA and the SOL were active in the
pre-activation (late swing) phase (Kuitunen et al, 2002; Kyröläinen et al., 2005; Mero &
Komi, 1987). Figure 3 showed activity beginning in the mid-swing phase for the TA
(Kuitunen et al., 2002; Kyröläinen et al., 2005; Mero & Komi, 1987).
Muscle activity levels
Seven articles found increases in muscle activity with increases in speed (Albertus-Kajee,
Tucker, Derman, Lamberts & Lambert, 2011; Bartlett et al., 2013; Higashihara et al., 2010;
Kuitunen et al., 2002; Kyröläinen et al., 2005; Mastalerz, Gwarek, Sadowski, & Szczepanski,
2012; Nummela et al., 1994). The maximum activations of the BF and semimembranosus
(SM) were found in the late swing and early stance phases, with the activation in the late
swing phase being two to three times greater than the late stance and early swing (Yu et al,
2008). Similarly, Kuitunen et al. (2002) found the highest EMG activity of the BF in the pre-
activation (late swing) phase. The ST showed greater activity than the BF during the mid-
swing phase, with the earlier peak activation of the ST than the BF during the late swing
phase (Higashihara et al., 2010). In an inclined sprint, the root mean square (RMS) of the BF
and ST was decreased compared to level sprinting during the early stance phase (Slawinski et
al., 2008). Slawinski et al. (2008) found the RMS of the VL and the SOL was also lower in
inclined sprinting compared to level sprinting. Ball and Scurr (2011) showed higher RMS
EMG in Medial Gastrocnemius (MG) and SOL compared to Lateral Gastrocnemius (LG).
Mastalerz et al. (2012) found greater fatigue in the left BF to the right BF on bend running.
EMG systems and specifications
The range of EMG systems used within the studies included in the review is described in
Table 4. Of the 18 articles reviewed, 14 used telemetry (of which four were also data logging
systems), two used data logging and two used wired systems. Four of the articles mentioned
the use of transmitter devices attached to the participants back, either strapped (Albertus-
Kajee et al.,2011; Pinniger et al. 2000) or attached to a belt (Nummela, et al., 1992;
Nummela, et al., 1994; Nummela, et al., 2008). Two articles mention taping cables back to
avoid motion artefacts (Higashihara et al., 2010; Thelen et al., 2005). Similar specifications
were seen between each of the systems used (see Table 5). Typically a 12 16 bit Analog to
Digital Converter, and a gain of 500 1000 was used. The most common sampling
frequency was 1000 Hz. The bandwidth was generally from 10 to 500 Hz and the input
impedance was set below 100 MΩ. Five articles also mentioned the use of a ground or
reference electrode attached to the wrist (Ball & Scurr, 2008; Ball & Scurr 2011; Thelen et
al., 2005) or the tibia (Pinniger et al., 2000; Schache et al., 2012; Yu et al., 2008).
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Table 4. The frequency of use of the EMG systems from the 18 review papers
EMG system
Bangoli-
16 DelSys
Biometrics
Datalog EMG
System
Glonner
Biomes
2000
ME3000
Mega
Electronics
ME6000
Mega
Electronics
Telemyo
EMG
System
Noraxon
Medinik
AB (IC-
600-G)
Bartlett et
al.(2013)
Mastalerz et
al.(2012)
Schache et
al.(2012)
Albertus-Kajee
et al.(2011)
Ball & Scurr
(2011, 2008)
Higashihara et
al.(2010)
Nummela et
al.(2008, 1994,
1992)
Slawinski et
al.(2008)
Yu et al.(2008)
Chumanov et
al.(2007)
Kyröläinen et
al.(2005)
Thelen et
al.(2005)
Kuitunen et
al.(2002)
Pinniger et
al.(2000)
Mero & Komi
(1987)
Table 5. Specifications of Electromyography systems reviewed
EMG devices specifications
Sampling
Rate
Analog to
Digital
Converter
(ADC)
Common Mode
Rejection Ratio
(CMRR)
Input
Impedance
Gain
Bandwidth
(BW)
Bartlett et al.(2013)
1 kHz
16-bit
>100 dB
>100 MΩ
1700
Mastalerz et al.(2012)
1 kHz
14-bit
>130 dB
1000
2 - 500 Hz
Schache et al.(2012)
1.5 kHz
16-bit
>100 dB
>100 MΩ
500
Albertus-Kajee et al.(2011)
2 kHz
16-bit
>100 dB
>100 MΩ
1000
10 - 500
Hz
Ball et al.(2011, 2008)
1 kHz
>96 dB @ 60 Hz
> 100 MΩ
1000
20 - 450
Hz
Higashihara et al.(2010)
2 kHz
16-bit
50 - 500
Hz
Nummela et al.(2008, 1994, 1992)
1 kHz
1000
Slawinski et al.(2008)
1 k Hz
375
8 -500 Hz
Yu et al.(2008)
2.4 kHz
16-bit
>100 dB
>100 MΩ
1000
10- 800 Hz
Chumanov et al.(2007)
2 kHz
12-bit
> 84 dB @ 60 Hz
> 100 MΩ
20 - 450
Hz
Kyröläinen et al.(2005)
1 kHz
500
0-360 Hz
Thelen et al.(2005)
2 kHz
12-bit
> 84 dB @ 60 Hz
> 100 MΩ
20 - 450
Hz
Kuitunen et al.(2002)
833 Hz
Pinniger et al.(2000)
1 kHz
16-bit
>100 dB
>100 MΩ
1000
0 - 340 Hz
Mero et al.(1987)
1 kHz
1000
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Discussion & Implications
The primary aim of this study was to examine the various muscles analysed during sprinting,
highlighting where the focus has been, which muscles were important for sprinting in
sequencing and timings of activations and the changes in muscle activity levels as a function
of running speed. Analysis of the hamstring muscle mechanics and fatigue during sprinting
were two of the most common themes emerging from this review. This focus on hamstring
muscle sEMG in many of the studies reviewed is reasonable given the important role that the
hamstrings play in generating forward ground reaction forces during the propulsive part of
stance in sprinting. This muscle group is also the most commonly injured during sprinting
(Chumanov et al., 2007; Thelen et al., 2005) which emphasises the importance of evaluating
the hamstring muscle activity during sprinting. Yu et al., (2008) examined the kinematics
and activations of the hamstrings during over-ground sprinting using sEMG wireless
telemetry. Differences in running biomechanics and onset times of muscle activations have
been observed between treadmill and overground running (Baur et al., 2007; Wank et al.,
1998), since treadmills have limited ecological validity and therefore analysis of over-ground
sprinting is more appropriate and valid (Van Caekenberghe, Segers, Willems, et al., 2013).
Higashihara et al. (2010) and Schache et al. (2012) analysed the BF and ST and compared
their muscle activity over trials of increased running speed, a potentially greater risk of
hamstring strain as sprint speed increased was proposed, however it must be noted that
although the authors suggest an increased risk it was not directly observed or measured.
Understanding the specific muscle activations of the hamstring and gluteal muscle groups is
useful for coaches and practitioners as this knowledge may provide vital insights on injury
risk factors and muscle loadings during the various phases of the sprint action. Hamstring
strain injuries are likely to occur at the muscle belly during the late swing phase (Best,
McElhaney, Garrett, & Myers, 1995; Yu et al., 2008). Yu et al. (2008) observed that the peak
eccentric contraction speeds of the hamstring muscle were significantly greater during the
late swing phase than the late stance phase, which could explain why 90% of hamstring strain
injuries occur in the muscle belly (Askling, Tengvar, Saartok, & Thorstensson, 2007;
Koulouris, Connell, Brukner, & Schneider-Kolsky, 2007). Early identification of injury
risks in athletes will highlight the possibility of muscle imbalances or incorrect running
biomechanics. This in turn, may help prevent the risk of a more serious injury or
reoccurrence due to non-optimal running biomechanics or training methods.
The effects of fatigue on muscle activation can also provide vital insights about specific
injuries during sprinting (Thelen et al., 2005; Yu et al., 2008). These studies noted that there
was increased muscle activation due to muscle fatigue in submaximal conditions. Fatigue in
the muscles was also correlated with an increase in the duration of the muscle activation, an
increase in the AEMG or an increase in the iEMG. For coaches and practitioners, there is a
need for early recognition of the onset of fatigue levels that may precede injury and therefore
place an athlete at risk. Recognising the onset of fatigue through EMG monitoring during
sprinting may be helpful in providing early warnings of elevated injury risk.
Pinniger et al. (2000) noted that in a fatigued sprint, the duration of the muscle activation in
the ST muscle increased significantly with an earlier onset and a later termination of the
activation. Similarly, there was a difference in the RF in the fatigued condition: the first burst
of activity terminated significantly earlier and the second burst turned on significantly earlier
(Pinniger et al., 2000). Pinniger et al. (2000) also observed that fatigue measured during the
20 m sprint and during the maximum voluntary contraction (MVC) measurement was not
related to the fatigue which caused the decrease in velocity during the endurance task.
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Longer, endurance sprints, such as the 400 m, were performed in some studies to consider the
effects of fatigue (Mastalerz et al., 2012; Nummela et al., 1994; Nummela et al., 1992;
Slawinski et al., 2008). These studies observed that EMG activity increased as the sprint
progressed. Increased contact times in the latter half of the run could be as a result of the
increasing number of slow-twitch fibres involved as the fast-twitch fibres fatigued (Nummela
et al., 1992). The left limb had a greater fatigue compared to the right limb due to a
considerable load on the BF of the inner leg, which could be caused from the curve on the
track (Mastalerz et al., 2012). Understanding the differences in fatigue between long and
short sprinters is very important to allow coaches observe the signs of fatigue in their athletes
during speed or endurance specific training session.
Several studies observed changes in the EMG data across various running speeds, which
showed that the activity of the muscles increased with an increase in speed (Nummela et al.,
1994; Kuitunen et al., 2002; Bartlett et al., 2013; Mastalerz et al., 2012; Albertus-Kajee et al.,
2011; Kyröläinen et al., 2005; Higashihara et al., 2010). Nummela et al. (1994) observed a
significant difference in the RF in the braking (early stance) phase; this was most likely due
to the important role the RF plays in tolerating impact loads. Kuitunen et al. (2002)
examined a variety of speeds as a percentage of maximum speed which showed that there
was an increase in muscle activation of the plantar flexors (TA) and the knee extensors (RF)
in the pre-activation (late swing) phase as the speed increased, the VM showed earlier peak
activation in the late swing phase in higher speeds and there was significant differences found
in the BF with increased speeds. Another study found a large increase in EMG amplitude in
sprinting compared to the walking condition, the RMS mean normalised to walking showed a
significant difference of four to seven times greater during sprinting (Bartlett et al., 2013).
The greatest changes in muscle activity were found in the BF and RF as speed increased
(Albertus-Kajee et al., 2011). Kyröläinen et al. (2005) found that the MVC is not a good
indicator of the activation potential, since some muscles recorded amplitudes greater than the
MVC recorded.
The timings of muscle activations provide important insight into the functions the muscles
perform throughout the gait running cycle. Figure 3 shows that during the braking (early
stance) phase, agonistic and antagonistic muscles co-contract to facilitate stabilisation. It can
be observed from Figure 3 that there are temporal overlaps of muscle activity in agonist and
antagonist groups. For example, the calves and the hamstrings in the braking (early stance)
phase contract simultaneously with the TA and RF respectively. During the flight phase
when the knee is in a flexed position there is minimal activity observed in the hamstrings and
the calves. Figure 3 shows RF is active in the early swing phase and contracts eccentrically
for hip extension and knee flexion. There is no activation of the RF during the concentric
contraction in the forward flexion of the thigh, however in the late swing phase there is
activation in the RF as the leg extends in preparation for the ground contact (see Figure 3).
Mero and Komi (1987) concluded that the RF had a more important role as a hip flexor than a
knee extensor. The TA is also active earlier in the swing phase to keep the foot in a
dorsiflexed position throughout mid swing to late swing phase. It is then activating in
preparation for the ground contact when it takes on a stabilisation role alongside the calves
muscle group in the braking phase. All of the muscle groups shown are active in the late
swing phase in preparation for ground contact and then in the early stance phase in a
stabilisation role.
The secondary aim was to understand the various technologies used for sEMG in sprinting, to
identify the key features of these systems and examine their relative merits and limitations in
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the analysis of sprinting. Examinations of EMG systems and their specifications in sprinting
show that the main issues were with the data transmission rather than the specification of the
acquisition features. System specifications were very similar across the devices, with data
acquisition and analysis steps performed to similar standards. Many of the systems used
were data logger technologies, which required long wires when evaluating sEMG on distal
body segments were required. The quadriceps and hamstring muscles are prime movers in
sprinting. Logically the majority of papers analysed these muscles, however the convenience
in measuring the upper leg muscles (see Table 3) may have also been a factor resulting in
these muscles being the most analysed in sprinting, results show over 70% of the muscles
analysed were the upper leg muscles. Less emphasis has been placed on the analysis of the
lower leg muscles. Less than 30% of those analysed were the lower leg muscles which may
be due to technology constraints. Longer wires would cause increased noise artefact or
movement encumbrance if the data logger was mounted on the distal segment. Clearly, there
is a bias on the muscles analysed which may be a consideration due to the limitations of
devices. The use of fully wireless sEMG systems could facilitate the effective analysis of a
wider range of muscles used in sprinting (Howard, Conway & Harrison, 2016).
There appears to be a historic trend which dominates sEMG measurements. Several studies
in this review reported the use of a tethered sEMG system for analysing gait and running
performance. However, these studies all involved the athlete running or walking on a
treadmill (Chumanov et al., 2007; Higashihara et al., 2010; Thelen et al., 2005). Very few
treadmills allow athletes to reach maximum sprint speed and this limits the ecological
validity of treadmill running since sprinting or jogging on a treadmill is not identical with
overground sprinting or jogging (Baur et al., 2007; Van Caekenberghe, Segers, Willems, et
al., 2013; Wank et al., 1998). There may also be potential changes in the muscle activation
timings and magnitudes as motorised treadmills also contribute to hip extension, as the belt
moves the foot of the participant backwards (Van Caekenberghe, Segers, Aerts, Willems, &
De Clercq, 2013). As a result a tethered sEMG system is likely to cause the athlete to
moderate the way they run due to the fact sprints need to be performed on a treadmill. The
use of data loggers and telemetry also required the participants to wear a transceiver pack
connected via wires to the electrodes while sprinting which could cause changes in the sprint
movement pattern.
For coaches, monitoring sports performance it is important that the results accurately reflect
the activity in an ecologically valid environment. Technologies were initially quite bulky and
limited the amount of data that could be captured. The majority of sampling rates of the
systems in the papers reviewed were 1 kHz which is an appropriate sampling rate (SENIAM).
More recently higher sampling rates are being used (Albertus-Kajee et al., 2011; Higashihara
et al., 2010; Schache et al., 2012; Thelen et al., 2005; Yu et al., 2008). The issues associated
with tethered systems highlight the need for wireless based sEMG devices. The system
selected for analysis of sprinting needs to ensure many of the same specifications necessary
for any other application, while also allowing real time data streaming. By exploiting
wireless technology the data gathering process will be simplified for the practitioner (Howard
et al., 2016). Advances in technology have facilitated smaller wireless devices which can
sample at higher rates and stream large data sets wirelessly across a long distance.
Companies have invested in low power wireless technology with a huge emphasis on
wearable wireless sensor technologies. Technologies from other sectors can be easily
transferred into the area of sports performance and sprinting aiding the analysis of sprint
performance for both the coach and practitioner.
SPORTS BIOMECHANICS, 2016
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Conclusion
This review presented information on muscle activations during maximal sprinting such as
timings and activity levels across the running gait cycle. The composite of muscle activity
timings across the running gait cycle provides a summary of timings from previous research
and could aid future researchers. It is important that more research is done in the area of
injury prevention utilising data from muscle activations during sprinting, allowing a greater
insight into the causes of injury and the times at which athletes are at a greater risk. This will
aid coaches and facilitate more analysis in the area of sports performance for practitioners.
This review also highlights the current technologies used in the analysis of sEMG in sprinting
and will provide a useful reference for future studies. Due to the limitations of sEMG
devices, there are relatively few articles on sprinting using sEMG. EMG systems used
throughout these studies tended to be tethered or data logging systems giving a bias to the
muscles analysed and the way in which sprints were performed. There is a need to utilise
wireless technology to facilitate the analysis of all lower limb muscles during sprinting and
allow practitioners to perform the analysis in an ecological valid environment.
Disclosure statement
The authors declare that there is no conflict of interest.
Funding
Funding for this research was provided by the Irish Research Council [grant number
EPSPG/2013/587, Analog Devices Inc [grant number EPSPG/2013/587].
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... On the other hand, there were no differences found in the thighs and calves groups for any variable. The reason why the only thermal asymmetry that in uenced performance were hamstrings can be due to its importance in sprinting, as they play a crucial role generating force in the propulsive part of the sprint 34 , and are clearly the most frequently injured muscle during sprinting 5 , a proof of the effort these muscles do in the sprint is that an increase in running speed of 80-100% is linked with an increase in net hamstring muscle force and energy absorption of 1.4 and 1.9 times respectively 35 , however, this study took the data from athletes running on a treadmill, and the mechanical properties of treadmill surfaces are different to the surfaces where athletes train and compete usually (e.g. arti cial turf or athletic track) 36 . ...
... So, in concordance with this results, high thermal asymmetry hamstrings group performed similar to the low asymmetry hamstring group before de RSA, and then decreased it performance in the CMJ Post-RSA. This can be an indicative of fatigue, as a decreased average CMJ jump height can indicate neuromuscular fatigue 37 , the reason why only signi cant differences are found in the high asymmetry hamstring groups (and not in calves and thighs) maybe is because hamstrings are the most implicated and frequently injured group in sprinting 34 , while calves and thighs seems to play a less crucial role in sprinting, so it asymmetry takes longer to turn into fatigue when doing an RSA test, as they are less implicated in this activity. ...
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Enhancing the functionality of the hamstring is an important matter for sprinters in improving their performance. Sprinters show almost the highest incidences of hamstring injuries as compared with other athletes. For sprinters and their coaches, prevention of hamstring injury is a prime concern along with improved their performance. To prevent hamstring injuries in sprinters, injury, incidence, mechanisms, and risk factors need to be taken into consideration, and a strategic program based on evidence needs to be implemented. A combination of three factors: agility, strength, and flexibility, is a good contributor to preventing muscle injuries in sprinters. Simultaneously, the training programs need to take into consideration the conditioning for muscle fatigue depending on a sprinter’s abilities. It may be important for coaches, trainers, and sports doctors to encourage sprinters for stopping training to monitor the degree of fatigue objectively and subjectively and to avoid the risk of injury. Future establishment of a hamstring injury-prevention program will be achieved by building a support system for programs with tactics and strategies. These programs are based on the accumulation of data via cooperation among coaches, researchers, trainers, and sports doctors.
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... The key phases of the running gait cycle are usually divided into contact (from early to late stance) and flight (from early to late swing) phases (54). The kinematic variables traditionally assessed in sprint efforts are contact time (CT), stride length (SL) and frequency (SF), flight time (FT), contact distance, trunk angle, and joint angular kinematics. ...
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... The stance phase is marked by the foot's initial contact with the ground (foot strike), mid stance through toe-off and takeoff. The swing phase begins with the float as the toe off, which morphs into the forward swing or swing reversal, and finishes with the landing or absorption (Howard, Conway et al. 2018). From initial-stance to mid-stance phase, the quadriceps contract eccentric eccentrically to attenuate the GRF within store elastic energy to be used later for propulsion moment. ...
Thesis
The main objective of this thesis was to determine the influence of hamstring and quadriceps neuromuscular capacities on explosive performance and the risk of lower limb injuries in soccer players. The first study of this thesis examined the relationship between the isokinetic force capacity of the knee muscles and the deceleration performance in professional female soccer players. The results revealed the importance of the eccentric force of the knee extensors contributing to the production of braking force during the linear deceleration test. The second study of this thesis was interested in the influence of the direction of jump on the dynamic postural stability during an unipodal landing and the importance of the hamstring / quadriceps co-activation in the stabilization capacity of the legs unipodal supports. The main finding is that the dominant leg showed better dynamic postural stability during the jump landing, associated with higher H / Q co-activation in the first milliseconds of the contact phase. The third study of this thesis explores the influence of fatigue generated by maximal isokinetic contractions on the capacity for rapid strength of the hamstrings and quadriceps in footballers. The results showed that the functional and conventional ratios measured during the preseason testing are not sensitive to fatigue. In contrast, the rapid hamstring / quadriceps torque ratio is more affected by fatigue. In summary, this work has shown that the evaluation of the explosive strength and fatigability capacities of the extensor and flexor muscles of the knee on an isokinetic ergometer remains a central subject for the improvement of explosive performance and the reduction of the risk of injury in the soccer player and footballer. In addition, the results of this work show the value of systematically associating the analysis of the EMG signal with the evaluation of the isokinetic force capacities, sprint performance and stability of the supports in male and female soccer players.
... Therefore, the purpose of this study was to analyze changes in normalized sEMG signals of selected lower limb muscles (gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, vastus lateralis) between subsequent curved sections of the track during a 400 m indoor sprint. These muscles were selected because their activation is most often analyzed in sprint studies [16]. Based on previous findings, it was hypothesized that the normalized sEMG signals of the gastrocnemius medialis will be higher for the left (inside) than for the right leg (outside) while running curved sections of the 400 m race, but the magnitude of this difference will decrease in successive curves. ...
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The purpose of this cross-sectional study was to analyze changes in normalized surface electromyography (sEMG) signals for the gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, and vastus lateralis muscles occurring during a 400 m indoor sprint between subsequent curved sections of the track. Ten well-trained female sprinters (age: 21 ± 4 years; body mass: 47 ± 5 kg; body height: 161 ± 7 cm; 400 m personal best: 52.4 ± 1.1 s) performed an all-out 400 m indoor sprint. Normalized sEMG signals were recorded bilaterally from the selected lower limb muscles. The two-way ANOVA (curve × side) revealed no statistically significant interaction. However, the main effect analysis showed that normalized sEMG signals significantly increased in subsequent curves run for all the studied muscles: gastrocnemius medialis (p = 0.003), biceps femoris (p < 0.0001), gluteus maximus (p = 0.044), tibialis anterior (p = 0.001), and vastus lateralis (p = 0.023), but differences between limbs were significant only for the gastrocnemius medialis (p = 0.012). The results suggest that the normalized sEMG signals for the lower limb muscles increased in successive curves during the 400 m indoor sprint. Moreover, the gastrocnemius medialis of the inner leg is highly activated while running curves; therefore, it should be properly prepared for high demands, and attention should be paid to the possibility of the occurrence of a negative adaptation, such as asymmetries.
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Forma de citar: Silhi-Vargas F, Bruneau-Chávez B, Rifo-Contreras V, Lagos-Hernández R. Diferencias de actividad electromiográfica abdominal en el entrenamiento del core. Salud UIS. 2022; e22008. doi: https://doi.org/10.18273/ saluduis.54.e:22009 Resumen Introducción: la musculatura del core involucra a todos los grupos musculares ubicados en la zona media del cuerpo cuya actividad puede ser medible usando electromiografía superficial (sEMG), metodología clínica para el registro y posterior análisis de las actividades bioeléctricas del músculo esquelético. Objetivo: el objetivo de esta investigación es determinar si existen diferencias de actividad electromiográfica en la musculatura del recto abdominal y oblicuo externo entre practicantes de musculación y practicantes de calistenia. Metodología: el estudio es de tipo comparativo, de diseño no experimental, transversal de enfoque cuantitativo en una muestra de 20 sujetos; 10 practicantes de calistenia y 10 que entrenan con ejercicios de musculación clásicos. Resultados: los ejercicios que tuvieron mayor respuesta electromiográfica fueron abdominal en suspensión y abdominal inclinado. Conclusiones: la calistenia es una alternativa viable para la activación muscular a nivel del Core. Abstract Introduction: The Core musculature involves all the muscle groups located in the middle area of the body whose activity can be measured using superficial electromyography (sEMG), a clinical methodology for recording and subsequent analysis of skeletal muscle bioelectric activities. Objectives: The objective of this research is to determine if there are differences in electromyographic activity in the muscles of the rectus abdominis and external oblique between bodybuilding practitioners and calisthenics practitioners. Methodology: A comparative study, non-experimental design, cross-sectional with a quantitative approach in a sample of 20 subjects; 10 calisthenics practitioners and 10 who train with classic bodybuilding exercises. Results: The exercises with the greatest electromyographic response were abdominal in suspension and abdominal incline. Conclusions: Calisthenics is a viable alternative for muscle activation at the Core level.
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Purpose: We aimed to examine the timing of electromyography activity of the rectus femoris (RF) and biceps femoris (BF) in both legs, as well as spatiotemporal variables (running speed (RS), step frequency (SF), step length (SL)) between the maximal speed (Max) phase (50-70 m) and the deceleration (Dec) phase (80-100 m) of the 100-m dash. Methods: Nine track and field athletes performed the 100-m dash with maximal effort. Spatiotemporal variables of each 10-m section were measured. A portable wireless data logger was attached to the subject's lower back to record electromyographies. We calculated onset/offset timing (%) of RF and BF in both legs using a Teager-Kaiser Energy Operator filter (e.g., ipsilateral leg RF onset is "iRF-onset," contralateral leg BF onset is "cBF-onset") in a running cycle. Results: The decreased RS in the Dec phase (P < 0.001) was due to a decreased SF (P < 0.001). Moreover, iRF-onset (P = 0.002), iRF-offset (P = 0.008), iBF-offset (P = 0.049), and cBF-offset (P = 0.017) in the Dec phase lagged in the running cycle as compared with the Max phase. Furthermore, the time difference between the swing leg RF activity (iRF-onset) and the contact leg BF activity (cBF-onset; "Scissors1") became bigger in the Dec phase (P = 0.041). Significant negative correlations were found between ΔiRF-onset and ΔSF (P = 0.045), and between ΔiBF-offset and ΔSF (P = 0.036). Conclusions: The decreased RS and SF in the Dec phase of the 100-m dash would be the delayed timing of the RF and BF activities in the same leg as well as the disturbed interleg muscular coordination.
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http://www.tandfonline.com/doi/ref/10.1080/14763141.2016.1174289 This paper examines the use of sensor devices in sports biomechanics, focusing on current frequency of use of Electromyography (EMG) device preferences. Researchers in the International Society of Biomechanics in Sports were invited to participate in an online survey. Responses on multiple sensor devices highlighting frequency of use, device features and improvements researchers sought in acquisition and analysis methods were obtained via an online questionnaire. Results of the investigation showed that the force platform is the most frequently used device, with inertial measurement units and EMG devices growing in popularity. Wireless functionality and ease of use for both the participant and the practitioner proved to be important features. The main findings of the survey demonstrated need for a simple, low power, multi-channel device which incorporates the various sensors into one single device. Biomechanists showed they were looking for more availability of wireless sensor devices with acquisition and analysis features. The study found there is a need to develop software analysis tools to accompany the multi-channel device, providing all the basic functions while maintaining compatibility with existing systems.
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The objective of this study is to compare the muscular activity of lower extremity muscles while running on treadmill and on overground surfaces. A total of 13 experienced heel-to-toe runners participated in the study. Electromyographic (EMG) data of four lower extremity muscles, including rectus femoris, tibialis anterior, biceps femoris, and gastrocnemius, were collected using the Noraxon EMG system while running on a treadmill and on overground surfaces at a running speed of 3.8 m/s. The obtained data were then analyzed. In this study, throughout the stance phase, the EMG values in the rectus femoris (P<0.01) and the biceps femoris (P<0.05) were higher while running on overground surfaces than those on a treadmill. The EMG values in the rectus femoris (P<0.05) and the biceps femoris (P<0.05) were also higher on concrete than those on grass in the stance phase. Results showed that the muscle activity was significantly different in treadmill running than in overground running. The difference in muscle activity while running on different overground surfaces was also found in this study. Kinematic adjustment of the lower extremity may explain the EMG difference while running on different surfaces.
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The aim of this study was to assess the efficacy of electromyography (EMG) normalization methods for a high-speed 20-m sprint. Comparisons were based on intra-individual reliability and magnitude of normalized EMG signals from three repeat sessions separated by 1 day (between days) and 1 week (between weeks) from the initial test. Surface EMGs were recorded (n=16) from the medial and lateral gastrocnemius and soleus during the normalization methods (isometric: maximum/sub-maximum/body weight; isotonic: maximum/sub-maximum/body weight; isokinetic: 1.05 rad · s–1, 1.31 rad · s–1, 1.83 rad · s–1; squat jump). The EMG data from the 20-m sprint were normalized using each method and using the within-sprint peak EMG (sprint peak). Intra-individual reliability of the EMG was assessed using typical error of measurement as a percentage of intra-individual coefficient of variance (TEMCV%). Sprint peak normalization improved intra-individual reliability of EMG (soleus: CV%; medial gastrocnemius: CV%; lateral gastrocnemius: CV%) compared with un-normalized EMG (soleus: CV%; medial gastrocnemius: CV%; lateral gastrocnemius: CV%) both between days and between weeks. Squat jump normalization improved the soleus (CV%) and medial gastrocnemius (CV%) reliability between days and weeks and provided a representative measure of triceps surae muscle activation. The intra-individual reliability of the medial gastrocnemius EMG data was improved both between days and weeks when using isotonic normalization. Isometric and isokinetic normalization showed no improvement in intra-individual reliability either between days or weeks for any muscle. The method of normalization influenced the between-stride muscle interaction during the 20-m sprint. The results of this study suggest that peak normalization can be used to normalize high-speed muscle actions, while normalizing EMG to a squat jump may provide an alternative method to represent relative muscle activation.
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Literature shows that running on an accelerated motorized treadmill is mechanically different from accelerated running overground. Overground, the subject has to enlarge the net anterior-posterior force impulse proportional to acceleration in order to overcome linear whole body inertia, whereas on a treadmill, this force impulse remains zero, regardless of belt acceleration. Therefore, it can be expected that changes in kinematics and joint kinetics of the human body also are proportional to acceleration overground, whereas no changes according to belt acceleration are expected on a treadmill. This study documents kinematics and joint kinetics of accelerated running overground and running on an accelerated motorized treadmill belt for 10 young healthy subjects. When accelerating overground, ground reaction forces are characterized by less braking and more propulsion, generating a more forward-oriented ground reaction force vector and a more forwardly inclined body compared with steady-state running. This change in body orientation as such is partly responsible for the changed force direction. Besides this, more pronounced hip and knee flexion at initial contact, a larger hip extension velocity, smaller knee flexion velocity and smaller initial plantarflexion velocity are associated with less braking. A larger knee extension and plantarflexion velocity result in larger propulsion. Altogether, during stance, joint moments are not significantly influenced by acceleration overground. Therefore, we suggest that the overall behaviour of the musculoskeletal system (in terms of kinematics and joint moments) during acceleration at a certain speed remains essentially identical to steady-state running at the same speed, yet acting in a different orientation. However, because acceleration implies extra mechanical work to increase the running speed, muscular effort done (in terms of power output) must be larger. This is confirmed by larger joint power generation at the level of the hip and lower power absorption at the knee as the result of subtle differences in joint velocity. On a treadmill, ground reaction forces are not influenced by acceleration and, compared with overground, virtually no kinesiological adaptations to an accelerating belt are observed. Consequently, adaptations to acceleration during running differ from treadmill to overground and should be studied in the condition of interest.
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The purpose of this work was to investigate the electromyographic (EMG) fatigue representations in muscles of male runners during run at different level of intensity. In this study, the EMG signals for the rectus femoris and biceps femoris (long head) were collected by bipolar electrodes from the left and right lower extremities. EMG measurements were recorded during the run on tartan athletic track. Four professional athletes had to run a 400 m distance with a different intensity. The first distance of 400 m took 90 s; the second, 70 s; the third, 60 s; and the last one was covered with a maximal velocity until exhaustion. Power spectral analysis of EMG signals was carried out to calculate MPF. The results of our study revealed the efforts of different intensity for each muscle individually. The effect of fatigue was observed only in the case of running with the highest velocity. The biggest changes in MPF were observed for BF (23.6%) and RF (19.5%) muscles of the left leg and then for BF (17.5%) and RF (12.5%) ones of the right leg. We supposed that those differences between the right and left legs were mainly due to the curve of the track where those muscles are differently loaded.
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It still remains unclear whether muscular activity on the treadmill (T) differs compared to overground (O) running. The purpose of this study was therefore to examine possible differences in muscular activation between T and O. 14 healthy runners were analyzed in a neutral running shoe at 12 km-h(-1) on a treadmill and in a field test. Muscular activity (EMG) of the tibialis anterior, peroneus longus, and soleus were measured. Time and amplitude quantities were assessed during the gait cycle. The EMG of the peroneus longus exhibited a later onset, a later maximum and shorter total time of activation (p < 0.05) in O. The soleus showed a higher amplitudes in O during the push-off phase (p < 0.05). Altered peroneus longus activity may indicate its role as an ankle stabilizer and demonstrates a compensatory response due to changing mechanical conditions. Weaker amplitudes of the soleus in the push-off during T suggest adaptation to the movement of the treadmill belt, and/or changes in load receptor input. Differences in muscle activity between T and O running must thus be taken into consideration in studies of neuromuscular control of movement.
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For overground and treadmill locomotion to be mechanically similar, it is required that the belt speed of the treadmill is constant and the same to that of overground locomotion. Variation of the belt speed during a stride causes exchange of energy between the subject and the treadmill. This might be the cause of different kinematic patterns between overground and treadmill locomotion, which have been reported in literature. The aim of this study was to investigate whether the intra-stride belt-speed is variable, and whether differences in kinematic patterns can be attributed to these variations. Nine subjects walked and ran overground and on two treadmills that were differently susceptible to subjects' braking and accelerating forces. It was found that the speed variations during treadmill locomotion affect the kinematic parameters significantly. The amount of intra-stride belt-speed variation was found to depend on the power of the treadmill and the mass of the subject.
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It has been suggested that the uniquely large gluteus maximus (GMAX) muscles were an important adaptation during hominin evolution based on numerous anatomical differences between humans and extant apes. GMAX electromyographic (EMG) signals have been quantified for numerous individual movements, but not across the range of locomotor gaits and speeds for the same subjects. Thus, comparing relative EMG amplitudes between these activities has not been possible. We assessed the EMG activity of the gluteal muscles during walking, running, sprinting, and climbing. To gain further insight into the function of the gluteal muscles during locomotion, we measured muscle activity during walking and running with external devices that increased or decreased the need to control either forward or backward trunk pitch. We hypothesized that 1) GMAX EMG activity would be greatest during sprinting and climbing and 2) GMAX EMG activity would be modulated in response to altered forward trunk pitch demands during running. We found that GMAX activity in running was greater than walking and similar to climbing. However, the activity during sprinting was much greater than during running. Further, only the inferior portion of the GMAX had a significant change with altered trunk pitch demands, suggesting that the hip extensors have a limited contribution to the control of trunk pitch movements during running. Overall, our data suggest that the large size of the GMAX reflects its multifaceted role during rapid and powerful movements rather than as a specific adaptation for a single submaximal task such as endurance running. Am J Phys Anthropol, 2013. © 2013 Wiley Periodicals, Inc.
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Unsteady state gait involving net accelerations has been studied overground and on a treadmill. Yet it has never been tested if and to what extent both set-ups are mechanically equal. This study documents the differences in ground reaction forces for accelerated running on an instrumented runway and running on an accelerating treadmill by building a theoretical framework which is experimentally put to the test. It is demonstrated that, in contrast to overground, no mean fore-after force impulse should be generated to follow an accelerating treadmill due to the absence of linear whole body acceleration. Accordingly, the adaptations in the braking phase (less braking) and propulsive phase (more propulsion) to accelerate overground are not present to follow an accelerating treadmill. It can be concluded that running on an accelerating treadmill is mechanically different from accelerated running overground.
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An understanding of hamstring mechanics during sprinting is important for elucidating why these muscles are so vulnerable to acute strain-type injury. The purpose of this study was twofold: first, to quantify the biomechanical load (specifically, musculotendon strain, velocity, force, power, and work) experienced by the hamstrings across a full stride cycle; and second, to determine how these parameters differ for each hamstring muscle (i.e., semimembranosus (SM), semitendinosus (ST), biceps femoris long head (BF), biceps femoris short head (BF)). Full-body kinematics and ground reaction force data were recorded simultaneously from seven subjects while sprinting on an indoor running track. Experimental data were integrated with a three-dimensional musculoskeletal computer model comprised of 12 body segments and 92 musculotendon structures. The model was used in conjunction with an optimization algorithm to calculate musculotendon strain, velocity, force, power, and work for the hamstrings. SM, ST, and BF all reached peak strain, produced peak force, and formed much negative work (energy absorption) during terminal swing. The biomechanical load differed for each hamstring muscle: BF exhibited the largest peak strain, ST displayed the greatest lengthening velocity, and SM produced the highest peak force, absorbed and generated the most power, and performed the largest amount of positive and negative work. As peak musculotendon force and strain for BF, ST, and SM occurred around the same time during terminal swing, it is suggested that this period in the stride cycle may be when the biarticular hamstrings are at greatest injury risk. On this basis, hamstring injury prevention or rehabilitation programs should preferentially target strengthening exercises that involve eccentric contractions performed with high loads at longer musculotendon lengths.