ArticlePDF Available

Muscle activity in sprinting: a review

Sports Biomechanics

February 2017

DOI:10.1080/14763141.2016.1252790

Authors:

Róisín Howard

University of Limerick

Richard Conway

University of Limerick

Andrew Harrison

University of Limerick

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.

Flow chart outlining the inclusion criteria for articles reviewed.

…

. Participant information from the selected 18 review papers

…

. Study information from the selected 18 review papers

…

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.

…

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).

…

Figures - uploaded by Róisín Howard

Content may be subject to copyright.

Content uploaded by Róisín Howard

Content may be subject to copyright.

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).

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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.

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

Figure 1. Flow chart outlining the inclusion criteria for articles reviewed

Figure 2. The key phases of the running gait cycle

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

Male

Female

Total

Albertus-Kajee et

al.(2011)

12*

NS1

Middle

distance

Ball & Scurr

(2011)

NS1

Recreational

Ball & Scurr

(2008)

NS1

Bartlett et

al.(2013)

NS1

Recreational

Chumanov et

al.(2007)

NS1

Higashihara et

al.(2010)

8.50 ±0.14

Sprinters

Kuitunen et

al.(2002)

9.73

Sprinters

Kyröläinen et

al.(2005)

8.50 ±0.57

Middle

distance

Mastalerz et

al.(2012)

NS1

Sprinters

Mero & Komi

(1987)

190

10.16

±0.15

9.78 ±0.42

8.77 ±0.30

Sprinters

Nummela et

al.(1992)

8.17 ±0.31

Sprinters

Nummela et

al.(1994)

9.23 ±0.59

Sprinters

Nummela et

al.(2008)

7.61 ±0.44

Middle

distance

Pinniger et

al.(2000)

192

NS1

Footballers

Schache et

al.(2012)

8.95 ±0.70

Sprinters

Slawinski et

al.(2008)

7.56 ±0.41

Sprinters

Thelen et

al.(2005)

NS1

Yu et al.(2008)

140

7.77 ±0.11

Mixed

164

287

204

1077

8.50 ±0.89

*Gender of participants not disclosed.

1Not specified (NS) by the authors.

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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;

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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).

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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).

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

Ball et al.(2011, 2008)

1 kHz

>96 dB @ 60 Hz

> 100 MΩ

1000

20 - 450

Higashihara et al.(2010)

2 kHz

16-bit

50 - 500

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

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

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

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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.

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

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

http://dx.doi.org/10.1080/14763141.2016.1252790

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].

References

Albertus-Kajee, Y., Tucker, R., Derman, W., Lamberts, R. P., & Lambert M. I. (2011).

Alternative methods of normalising EMG during running. Journal of Electromyography and

Kinesiology, 21, 579-586. doi: 10.1016/j.jelekin.2011.03.009

Askling, C. M., Tengvar, M., Saartok, T., & Thorstensson, A., (2007). Acute first-time

hamstring strains during high-speed running: a longitudinal study including clinical and

magnetic resonance imaging findings. American Journal of Sports Medicine, 35, 197-206.

doi: 10.1177/0363546506294679

Ball, N., & Scurr, J. C. (2008). An assessment of the reliability and standardisation of tests

used to elicit reference muscular actions for electromyographical normalisation. Journal of

Electromyography and Kinesiology, 20, 81-88. doi: 10.1016/j.jelekin.2008.09.004

Ball, N., & Scurr, J. C. (2011). Efficacy of current and novel electromyographic

normalization methods for lower limb high-speed muscle actions. European Journal of

Sports Science, 11, 447-456. doi: 10.1080/17461391.2010.536583

Bartlett, J. L., Sumner, B., Ellis, R. G., & Kram, R. (2014). Activity and functions of the

human gluteal muscles in walking, running, sprinting, and climbing. American Journal of

Physical Anthropology, 153, 124-131. doi: 10.1002/Ajpa.22419

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

Baur, H., Hirschmuller, A., Muller, S., Gollhofer, A., & Mayer, F., (2007). Muscular activity

in treadmill and overground running. Isokinetics and Exercise Science 15, 165-171. doi:

10.12691/ajssm-2-4-8

Best, T. M., McElhaney, J. H., Garrett, W. E. Jr. & Myers, B. S., (1995). Axial strain

measurements in skeletal muscle at various strain rates. Journal of Biomechanical

Engineering, 117, 262-265.

Chumanov, E. S., Heiderscheit, B. C., & Thelen, D. G. (2007). The effect of speed and

influence of individual muscles on hamstring mechanics during the swing phase of sprinting.

Journal of Biomechanics, 40, 3555-3562. doi: 10.1016/j.jbiomech.2007.05.026

Ditroilo, M., Watsford, M., Fernandez-Pena, E., D'Amen, G., Lucertini, F., & De Vito, G.

(2011). Effects of Fatigue on Muscle Stiffness and Intermittent Sprinting during Cycling.

Medicine and Science in Sports and Exercise, 43, 837-845. doi:

10.1249/Mss.0b013e3182012261

Higashihara, A., Ono, T., Kubota, J., Okuwaki, T., & Fukubayashi, T. (2010). Functional

differences in the activity of the hamstring muscles with increasing running speed. Journal of

Sports Sciences, 28, 1085-1092. doi: 10.1080/02640414.2010.494308

Howard, R. M., Conway, R., & Harrison A. J. (2016) A survey of sensor devices: use in

sports biomechanics, Sports Biomechanics, X, 1 – 12. doi:10.1080/14763141.2016.1174289

Koulouris, G., Connell, D. A., Brukner, P., & Schneider-Kolsky, M., (2007). Magnetic

resonance imaging parameters for assessing risk of recurrent hamstring injuries in elite

athletes. American Journal of Sports Medicine, 9, 1500-1506. doi:

10.1177/0363546507301258

Kuitunen, S., Komi, P. V., & Kyröläinen, H. (2002). Knee and ankle joint stiffness in sprint

running. Medicine & Science in Sports & Exercise, 34, 166-173.

Kyröläinen, H., Avela, J., & Komi, P. V. (2005). Changes in muscle activity with increasing

running speed. Journal of Sports Sciences, 23, 1101-1109. doi: 10.1080/02640410400021575

Mastalerz, A., Gwarek, L., Sadowski, J., & Szczepanski, T. (2012). The influence of the run

intensity on bioelectrical activity of selected human leg muscles. Acta of Bioengineering and

Biomechanics, 14, 101-107. doi: 10.5277/abb120213

Mero, A., & Komi, P. V. (1987). Electromyographic activity in sprinting at speeds ranging

from sub-maximal to supra-maximal. Medicine & Science in Sports & Exercise, 19, 266 -

274.

Novacheck, T. F. (1998). The biomechanics of running. Gait & Posture, 7, 77-95. doi:

10.1016/S0966-6362(97)00038-6

Nummela, A. T., Heath, K. A., Paavolainen, L. M., Lambert, M. I., St Clair Gibson, A.,

Rusko, H. K., & Noakes, T. D. (2008). Fatigue during a 5-km running time trial. Sports

Medicine, 29, 738-745. doi: 10.1055/s-2007-989404

SPORTS BIOMECHANICS, 2016

http://dx.doi.org/10.1080/14763141.2016.1252790

Nummela, A., Rusko, H., & Mero, A. (1994). EMG activities and ground reaction forces

during fatigued and nonfatigued sprinting. Medicine and Science in Sports and Exercise, 26,

605-609.

Nummela, A., Vuorimaa, T., & Rusko, H. (1992). Changes in force production, blood lactate

and EMG activity in the 400-m sprint. Journal of Sports Sciences, 10, 217-228.

doi:10.1080/02640419208729920

Paul, L., & Wood, L. (2002). Skeletal muscle fatigue. Physical Therapy Reviews, 7, 123-132.

doi: 10.1179/108331902125001815

Pinniger, G. J., Steele, J. R., & Groeller, H. (2000). Does fatigue induced by repeated

dynamic efforts affect hamstring muscle function? Official Journal of the American College

of Sports Medicine, 32, 647-653. doi: 10.1097/00005768-200003000-00015

Savelberg, H. H. C. M., Vorstenbosch, M. A. T. M., Kamman, E. H., van de Weijer, J. G. W.,

& Schambardt, H. C. (1998). Intra-stride belt-speed variation affects treadmill locomotion.

Gait & Posture, 7, 26-34. doi: 10.1016/S0966-6362(97)00023-4

Schache, A. G, Dorn, T. W., Blanch, P. D., Brown, N. A., & Pandy, M. G. (2012). Mechanics

of the human hamstring muscles during sprinting. Medicine and Science in Sports and

Exercise, 44, 647-658. doi: 10.1249/MSS.0b013e318236a3d2

Slawinski, J., Dorel, S., Hug, F., Couturier, A., Fournel, V., Morin, J., & Hanon, C. (2008).

Elite long sprint running: a comparison between incline and level training sessions. Medicine

& Science in Sports & Exercise, 40, 1155-1162. doi: 10.1249/MSS.0b013e3181676681

Thelen, D. G., Chumanov, E. S., Best, T. M., Swanson, S. C, & Heiderscheit, B. C. (2005).

Simulation of biceps femoris musculotendon mechanics during the swing phase of sprinting.

Medicine & Science in Sports & Exercise, 37, 1931-1938.

Van Caekenberghe, I., Segers, V., Aerts, P., Willems, P., & De Clercq, D. (2013). Joint

kinematics and kinetics of overground accelerated running versus running on an accelerated

treadmill. Journal of the Royal Society Interface, 10, 20130222. doi: 10.1098/rsif.2013.0222

Van Caekenberghe, I., Segers, V., Willems, P., Gosseye, T., Aerts, P., & De Clercq, D.

(2013). Mechanics of overground accelerated running vs. running on an accelerated treadmill.

Gait & Posture, 38, 125-131. doi: 10.1016/j.gaitpost.2012.10.022

Wank, V., Frick, U., & Schmidtbleicher, D. (1998). Kinematics and electromyography of

lower limb muscles in overground and treadmill running. International Journal of Sports

Medicine 19, 455-461. doi: 10.1055/s-2007-971944

Yu, B., Queen, R. M., Abbey, A. N., Lui, Y., Moorman, C. T., & Garrett, W. E. (2008).

Hamstring muscle kinematics and activation during overground sprinting. Journal of

Biomechanics, 41, 3121-3126. doi: 10.1016/j.jbiomech.2008.09.005

Upper Leg Muscular Co-Contraction During Maximal-Speed Sprinting in Male Club Ice Hockey Athletes

Article

Full-text available

Jan 2025

Background Little is known about the lower extremity muscle co-contraction patterns during sprinting and its relation to running velocity (i.e., performance). Therefore, we compared lower extremity muscular activation patterns during sprinting between slower and faster collegiate club hockey athletes. We hypothesized that faster athletes would have lower EMG-assessed co-contraction index (CCI) values in the lower extremities during over-ground sprinting. Methods Twenty-two males (age = 21 [1] yrs (median[IQR]); body mass = 77.1 ± 8.6 kg (mean ± SD)) completed two 20-m over-ground sprints with concomitant EMG and asynchronous force plate testing over four days in Tallahassee, Florida, USA. We split participants using median running velocity (FAST: 8.5 ± 0.3 vs. SLOW: 7.7 ± 0.3 m/s, p < 0.001). Results Faster athletes had lower CCI between the rectus femoris and biceps femoris (group: p = 0.05), particularly during the late swing phase of the gait cycle (post hoc p = 0.02). Early swing phase duration was moderately inversely related to Hip CCI in the stance phase (ρ=-0.58, p < 0.01) and weakly related to Knee CCI in the swing phase (ρ = 0.44, p = 0.046). Finally, swing phase duration was moderately inversely related to Hip CCI in the stance phase (ρ=-0.50, p = 0.02). Conclusions In agreement with our hypothesis, we found lower CCI values in the upper leg musculature during maximal-speed over-ground sprinting. These data from collegiate club hockey athletes corroborate other reports in clinical populations that the coordination between the rectus femoris and biceps femoris is associated with linear over-ground sprinting velocity.

The Effects of High-Intensity Interval Training on Basketball Players: A Systematic Review and Meta-Analysis

Article

Dec 2024
J SPORT SCI MED

This review aims to evaluate the effectiveness of HIIT on basketball players' physical fitness and skill-related performance. This study adhered to the PRISMA guidelines and included randomized controlled trials (RCTs) that investigated the effects of HIIT on basketball players. The databases searched included Web of Science, Scopus, PubMed, and SPORTDiscus (up to 4 March 2024). The meta-analysis used a random-effects model, with effect sizes (ES) calculated for various performance outcomes. A total of 15 studies, with a low risk of bias or some concerns of bias, including 369 players (130 females, 239 males) at the developmental level, national level, and international level, were included in the systematic review, with 7 of these included in the meta-analysis. The systematic review indicated that HIIT significantly improved cardiovascular endurance, power, change of direction (COD) ability, linear sprint, and basketball skill-related performance. However, the effects on certain physical aspects such as VO2max, the Yo-Yo intermittent recovery test level 1 (Yo-Yo IR 1), jump tests, ball throw test, 20-m COD sprint test, T-test, 20-m linear sprint, and basketball-specific skills such as shooting accuracy and passing were inconsistent. The meta-analysis revealed a very large effect on the Yo-Yo IR 1 (ES = 2.32; p = 0.000), a moderate effect on VO2max (ES = 0.90; p = 0.000), T-test performance (ES = 0.91; p = 0.000), and CMJ height (ES = 0.76; p = 0.000), and a small effect on the 20-m sprint test (ES = 0.59; p = 0.006). HIIT appears to be an effective training method for improving general physical fitness and certain basketball-specific skills, particularly endurance, power, and agility. However, its impact on more skill-specific aspects, such as shooting accuracy and passing, requires further investigation. Coaches should consider supplementing HIIT with targeted skill training and carefully plan its timing, ideally incorporating HIIT during pre-season or off-season periods for optimal effectiveness. Further research is needed to explore the differential effects of HIIT across various age groups and playing levels.

The Effect of the Nordic Hamstring Exercise on Hamstring Muscle Activity Distribution During High-Speed Running Estimated Using Multichannel Electromyography: A Pragmatic Randomized Controlled Trial

Article

Full-text available

Nov 2024
CLIN J SPORT MED

Objective To evaluate the effect of the Nordic hamstring exercise on normalized muscle activity and relative contribution of the biceps femoris long head, semitendinosus, and semimembranosus through multichannel electromyography in the late-swing phase of high-speed running. Design A pragmatic, 2-arm, single-center randomized controlled trial. Participants were randomly assigned to a Nordic group or control group. Setting Dutch male basketball. Participants Twenty injury-free players (mean age 18 ± 3 years). Intervention A 12-week Nordic hamstring exercise intervention. Main Outcome Measures Level of normalized muscle activity (percentage maximal voluntary isometric contraction [%MVIC]) and relative contribution (%con) of hamstring muscles for 12 weeks. Results The Nordic hamstring exercise intervention did not result in significant changes for 12 weeks. For normalized muscle activity, between-group differences (compared with the control group) for 12 weeks were 11.4 %MVIC (95% confidence interval [95% CI]: −11.0, 33.8) for the biceps femoris long head, −9.4 %MVIC (95% CI: −23.3, 5.2) for the semitendinosus, and −2.7 %MVIC (95% CI: −15.8, 10.3) for the semimembranosus, P = 0.151. For relative contribution, between-group differences for 12 weeks were −6.1 %con (95% CI: −2.4, 14.6) for the biceps femoris long head, −7.0 %con (95% CI: −13.6, −0.4) for the semitendinosus, and 0.9 %con (95% CI: −9.2, 11.0) for the semimembranosus P = 0.187. Positive values are in favor of the Nordic group. Conclusions A 12-week Nordic hamstring exercise intervention did not affect the level of muscle activity and relative contribution of hamstring muscles in the late-swing phase of high-speed running. Because of the low amount of data sets, results should be interpreted cautiously.

Gastrocnemius Neuromuscular Activation During Standing Explosive Acceleration

Article

Full-text available

Oct 2024

The gastrocnemius muscle plays a crucial role in transmitting and generating energy during standing explosive accelerations, and as a consequence, is a muscle with high injury prevalence, especially the medial gastrocnemius (MG). This study aimed to compare the neuromuscular activation of the lateral gastrocnemius (LG) and MG during one of the most common standing explosive accelerations performed in team sports—the false start that occurs in jumps where the leg steps back before moving forward. Forty-two physically active participants (34 males: age = 24 ± 5 years, body mass = 73 ± 10.4 kg; and 8 females: age = 26 ± 5 years, body mass = 57.1 ± 6.8 kg) underwent electromyography analysis of the MG and LG in the four first foot contacts of standing explosive acceleration. The results showed that the third contact differed significantly from others (LG vs. MG: 76.48 ± 3.10 vs. 66.91 ± 2.25, p = 0.01, ES = 0.5), with the LG exhibiting earlier activation and higher peak sEMG activity compared to the MG (LG vs. MG: 0.12 ± 0.01 vs. 0.13 ± 0.01, p = 0.02, ES = 0.4). Additionally, the MG displayed longer duration contractions in all the foot contacts except the third foot contact. In conclusion, the MG showed an earlier activation timing and a longer duration of contraction than the LG in the first foot contact. Additionally, the third foot contact showed a different pattern of neuromuscular activation between the MG and LG compared to the rest of the foot contacts.

Cloud and IoT based Smart Agent-driven Simulation of Human Gait for Detecting Muscles Disorder

Preprint

Full-text available

Aug 2024

Motion disorders pose a significant global health concern and are often managed with pharmacological treatments that may lead to undesirable long-term effects. Current therapeutic strategies lack differentiation between healthy and unhealthy muscles in a patient, necessitating a targeted approach to distinguish between musculature. There is still no motion analyzer application for this purpose. Additionally, there is a deep gap in motion analysis software as some studies prioritize simulation, neglecting software needs, while others concentrate on computational aspects, disregarding simulation nuances. We introduce a comprehensive five-phase methodology to analyze the neuromuscular system of the lower body during gait. The first phase employs an innovative IoT-based method for motion signal capture. The second and third phases involve an agent-driven biomechanical model of the lower body skeleton and a model of human voluntary muscle. Thus, using an agent-driven approach, motion-captured signals can be converted to neural stimuli. The simulation results are then analyzed by our proposed ensemble neural network framework in the fourth step in order to detect abnormal motion in each joint. Finally, the results are shown by a userfriendly graphical interface which promotes the usability of the method. Utilizing the developed application, we simulate the neuromusculoskeletal system of some patients during the gait cycle, enabling the classification of healthy and pathological muscle activity through joint-based analysis. This study leverages cloud computing to create an infrastructure-independent application which is globally accessible. The proposed application enables experts to differentiate between healthy and unhealthy muscles in a patient by simulating his gait.

Associations between the isometric strength of selected muscle groups and the speed of linear and curvilinear sprints

Article

Jan 2025

The study was designed to investigate the associations between the isometric strength of selected muscle groups and the speed of linear and curvilinear sprints. Eighteen subjects (12 males (mean age: 24,8 ± 4,7 years) and 6 females (mean age: 20 ± 1,3 let)) participated in the study. Subjects performed linear and curvilinear sprints at the first visit and strength measurements at the second visit (knee extensors and flexors strength, hip adductors and abductors strength). The relationship between lower limb strength and maximum sprint speed was assessed using Pearson's correlation coefficient. Linear regression was performed to predict maximum speed in both linear and curvilinear sprints based on the strength variables. Knee flexors and extensors strength and hip adductors strength were in significant moderate to high correlation with the maximum speed of linear and curvilinear sprints (r = 0,57-0,76), while hip abductors strength were correlated just with maximum speed of curvilinear sprints. Selected isometric muscle strength variables explained 79,3 % of the variance in the maximum speed of linear sprint (right leg knee extensors, left leg hip adductors and right leg eccentric knee flexor strength). Right leg hip adductors and knee flexors strength explained 68,7 % of the variance in the maximum speed of the left curvilinear sprint and 68,0 % of the variance in the maximum speed of the right curvi-linear sprint. In general, high lower limb muscle strength shows correlations with better sprint times and higher developed speed during sprints.

THE EFFECTS OF RUNNING VELOCITY AND LOWER EXTREMITY LOADING ON BIARTICULAR LEG MUSCLES DURING TREADMILL RUNNING

Poster

Full-text available

Jul 2021

The purpose of this study was to investigate the changes in muscle activity levels in treadmill running while imposing in wearable loaded as a function of running speed. Nine recreational runner participated in this study; they were requested to perform the unloaded and loaded running at four discrete speeds ranging from 2.5 to 7.0 m/s on treadmill. The mass of wearable resistance was set at one third of the mass of the shank and foot of participant. Repeated-measures two-way ANOVA analysis was then used to explore loaded and speed effects. The data showed the shank loaded augmented the activation amplitude of the biceps femoris for the concentric action of hip extension followed the stretching of knee extension at high running speed. The loaded condition afforded the rectus femoris to be stretched and to diminish the disadvantage of active insufficiency for the hip flexor.

Einfluss der isometrischen und dynamischen Maximalkraft auf die Sprint- und Richtungswechselleistung bei jugendlichen Basketballern

Article

Oct 2024

Viele Studien haben den Zusammenhang der Maximalkraft zur Sprint- und Richtungswechselleistung untersucht, wobei nur eine Studie den Einfluss verschiedener Kraftqualitäten ermittelte. Das Ziel dieser Studie war es einen möglichen Zusammenhang zwischen der isometrischen oder dynamischen Maximalkraft und der Richtungswechsel- sowie Sprintleistung in jugendlichen Basketballern herauszustellen. Die dynamische und isometrische Relativkraft korrelieren signifikant negativ mit der Richtungswechselleistung, aber nicht dem Sprint. Dabei war die Korrelation der dynamischen Relativkraft und der Richtungswechselleistung stärker. Die Ergebnisse der Studie empfehlen eine Verbesserung der dynamischen Maximalkraft zur Verbesserung der Richtungswechselleistung.

Changes in hamstrings’ active stiffness during fatigue tasks are modulated by contraction duration rather than intensity

Article

Full-text available

Oct 2024

Despite the increase in research of hamstring stiffness through the use of ultrasound-based shear wave elastography, the active stiffness of biceps femoris long head (BFlh) and semitendinosus (ST) muscles under fatigue conditions at various contraction intensities has not been sufficiently explored. This study aimed to compare the effects of knee flexor’s isometric contraction until exhaustion performed at 20% vs. 40% of maximal voluntary isometric contraction (MVIC), on the active stiffness responses of BFlh and ST. Eighteen recreationally active males performed two experimental sessions. The knee flexors’ MVIC was assessed before the fatiguing task, which involved a submaximal isometric contraction until failure at 20% or 40% of MVIC. Active muscle stiffness of the BFlh and ST was assessed using shear wave elastography. BFlh active stiffness remained relatively unaltered at 20% of MVIC, while ST active stiffness decreased from ≅ 91% contraction time (55.79 to 44.52 kPa; p < 0.001). No intramuscular stiffness changes were noted in BFlh (36.02 to 41.36 kPa; p > 0.05) or ST (63.62 to 53.54 kPa; p > 0.05) at 40% of MVIC session. Intermuscular active stiffness at 20% of MVIC differed until 64% contraction time (p < 0.05) whereas, at 40% of MVIC, differences were observed until 33% contraction time (p < 0.05). BFlh/ST ratios were not different between intensities (20%=0.75 ± 0.24 ratio vs. 40%=0.72 ± 0.32 ratio; p > 0.05), but a steeper increase in BFlh/ST ratio was found for 20% (0.004 ± 0.003 ratio/%) compared to 40% (0.001 ± 0.003 ratio/%) of MVIC (p = 0.003). These results suggest that contraction duration could play a major role in inducing changes in hamstrings’ mechanical properties during fatigue tasks compared to contraction intensity.

Simulations reveal how touchdown kinematic variables affect top sprinting speed: implications for coaching

Preprint

Full-text available

Oct 2024

Sprint performance is a priority for coaches and athletes. Several kinematic variables, including horizontal touchdown distance (HTD) and inter-knee touchdown distance (IKTD), are targeted by coaches to increase top sprinting speed. However, the results of past research are conflicting, potentially due to the use of experimental inter-athlete study designs where it is not possible to establish cause-effect relationships. In this study, we used a predictive simulation approach to assess cause-effect relationships between HTD and IKTD and sprinting speed. We scaled a three-dimensional musculoskeletal model to match the anthropometry of an international caliber male sprinter, and generated predictive simulations of a single symmetric step of top-speed sprinting using a direct collocation optimal control framework. We first used our simulation framework to establish the model's top speed with minimal constraints on touchdown kinematics (the optimal simulation). Then, in additional simulations we enforced specific HTD or IKTD values (± 2, 4 and 6 cm compared to optimal). The model achieved a top speed of 11.85 m/s in the optimal simulation. Shortening HTD by 6 cm reduced speed by 7.3%, while lengthening HTD by 6 cm had a smaller impact on speed, with a 1.6% reduction. Speed in the simulation was insensitive to the IKTD changes we tested. The results of our simulations indicate there is an optimal HTD to maximize sprinting speed, providing support for coaches and athletes to adjust this technique variable. Conversely, our results do not provide evidence to support utilizing IKTD as a key technique variable for speed enhancement. We share the simulation framework so researchers can explore the effects of additional modifications on sprinting performance (https://github.com/nicos1993/Pred_Sim_Sprinting).

A survey of sensor devices: use in sports biomechanics

Article

Full-text available

May 2016

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.

Muscular Activity of Lower Extremity Muscles Running on Treadmill Compared with Different Overground Surfaces

Article

Full-text available

Jul 2014

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.

Efficacy of current and novel electromyographic normalization methods for lower limb high-speed muscle actions

Article

Full-text available

Nov 2011

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.

Joint kinematics and kinetics of overground accelerated running versus running on an accelerated treadmill

Article

Full-text available

Jul 2013
J R SOC INTERFACE

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.

The influence of the run intensity on bioelectrical activity of selected human leg muscles

Article

Full-text available

Jul 2012
Acta Bioeng Biomech

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.

Muscular activity in treadmill and overground running

Article

Aug 2007

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.

Intra-stride belt-speed variation affects treadmill locomotion

Article

Jan 1998
GAIT POSTURE

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.

Activity and Functions of the Human Gluteal Muscles in Walking, Running, Sprinting, and Climbing

Article

Jan 2014
AM J PHYS ANTHROPOL

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.

Mechanics of overground accelerated running vs. running on an accelerated treadmill

Article

Dec 2012

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.

Mechanics of the Human Hamstring Muscles during Sprinting

Article

Sep 2011
MED SCI SPORT EXER

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.