Balance of Upper Limb Muscle Activation and Aerodynamics for Cycling

Posture Optimization

Xiangru Li

, Peng Zhou

and Xin Zhang

Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water

Bay, Kowloon, Hong Kong SAR, China

Keywords:

Aerodynamics, Cycling Postures, Electromyography, Performance Optimization.

Abstract:

Cycling postures signiﬁcantly inﬂuence aerodynamic performance in competitive cycling, yet aggressive pos-

tures may increase muscle activation and lead to adverse physiological effects. This study evaluated the

aerodynamic drag area (C

A) of various cycling postures in a wind tunnel, revealing a strong correlation with

decreasing forearm angles. Surface electromyography (sEMG) tests were conducted to assess upper limb

muscle activation across postures, identifying the triceps brachii (TB) as dominant in maintaining both hoods

and drops positions (46.9% and 40.2% of total activation, respectively). Additionally, this study explores the

trade-off between aerodynamic gains and muscle activation by examining the relationship between C

A and

composite EMG. A Pareto front analysis identiﬁed locally optimal postures that balance these factors, poten-

tially enhancing overall cyclist performance.

1 INTRODUCTION

Cycling performance is inﬂuenced by a multitude

of factors, including psychological, physiological,

biomechanical, and environmental conditions (Berry

et al., 1994; Ghasemi et al., 2022; Arpinar-Avsar

et al., 2013). A key determinant of cycling perfor-

mance, cycling speed, depends on the cyclist’s power

output, aerodynamic drag, and environmental factors

such as wind and terrain (Martin et al., 2006). Ap-

proximately 90% of the total resistance experienced

during cycling at a speed over 30 km/h on ﬂat road

arises from the combined aerodynamic drag of the cy-

clist and their equipment (Faria et al., 2005). While

innovative equipment—such as aerodynamic helmets,

aero handlebars, and skinsuits—can reduce drag to

some extent, cycling postures remain a critical factor

in reﬁning the human-equipment interaction. Short-

distance sprint events often prioritize power output,

whereas endurance time trials (TTs) require a balance

between physiological efﬁciency and aerodynamics

(Faulkner et al., 2024; Fintelman et al., 2014).

Altering cycling postures primarily involves ad-

justments of torso and hip angles (Fintelman et al.,

2014, 2015). Aerodynamically favorable positions

typically require cyclists to adopt a crouched posture,

https://orcid.org/0009-0003-1601-2751

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https://orcid.org/0000-0001-9322-4115

with the forearm and trunk positioned nearly paral-

lel to the ground. However, such positions can com-

promise critical power output. For instance, transi-

tioning from the hoods of the handlebar to a TT po-

sition has been shown to reduce critical power (Ko-

rdi et al., 2019). Furthermore, aggressive postures

may adversely affect physiological metrics, including

oxygen consumption, muscle activation, muscle pain

and injury risk, and overall cycling economy (Turpin

et al., 2017; Faulkner and Jobling, 2020; Brand et al.,

2020). Muscle activity, commonly measured using

surface electromyography (EMG), is associated with

both fatigue, pain, and injury (Streisfeld et al., 2017)

and power production. For instance, a lower hip an-

gle can reduce muscle activation, thereby decreasing

lower limb power output (Kordi et al., 2019; Moura

et al., 2017). However, the effects of posture alter-

ations on muscle activity remain equivocal. Bini et al.

(2019) assessed the inﬂuence of different hip ﬂexion

angles on muscle forces, reporting that changes in up-

per body position by varying hip angles altered con-

tributions of the knee and hip joints without impact on

peak muscle forces. In contrast, Dorel et al. (2009)

found that aero position signiﬁcantly increased EMG

activity level of gluteus maxiums and vastus medialis,

compared with upright posture, while gas-exchange

variables presented minor differences.

Maintaining an aerodynamic position without spe-

cialized equipment, such as aero handlebar, is widely

recognized as challenging and often leads to exces-

Li, X., Zhou, P. and Zhang, X.

Balance of Upper Limb Muscle Activation and Aerodynamics for Cycling Posture Optimization.

DOI: 10.5220/0013677000003988

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2025), pages 50-56

ISBN: 978-989-758-771-9; ISSN: 2184-3201

sive upper body muscle activation, potentially caus-

ing pain and fatigue (Turpin et al., 2017; Brand et al.,

2020). This discomfort typically stems from pro-

longed cervical spine extension and lumbar spine hy-

perﬂexion, which impose high loads and compres-

sion on surrounding muscles during forward leaning

(Dettori and Norvell, 2006; Schwellnus and Derman,

2005). Identifying an optimal cycling posture remains

a complex challenge, as it requires balancing power

output, aerodynamics, and comfort (Savelberg et al.,

2003; Umberger et al., 1998; Brand et al., 2020).

Most previous studies on the impact of posture on per-

formance have focused on professional cyclists or the

lower limbs (Berry et al., 1994, 2000; So et al., 2005).

Moreover, adaptations to specialized equipment and

aggressively crouched positions through regular train-

ing may not generalize to recreational cyclists, high-

lighting the need for further research on upper body

and recreational population (Ashe et al., 2003; Brand

et al., 2020; Chapman et al., 2008; Savelberg et al.,

2003).

Despite these insights, there remains a scarcity

of research addressing the balance between aerody-

namic optimization and physiological cost. Faulkner

et al. (2020) introduced the concept of aerodynamic-

physiological economy (APE), which integrates

metabolic costs, aerodynamic positioning, and their

interaction with TT performance, providing valuable

predictions of cycling efﬁciency across varying upper

body postures . However, their drag area (C

A) es-

timates relied on anthropometric data and measured

frontal area, which may lack precision. Giljarhus

et al. (2020) explored the drag effects of adjusted

arm positions using computational simulations, but

such methods may overlook complex real-world ﬂow

conditions requiring further experimental validation.

Faulkner et al. (2024) later advanced aerodynamic

measurement by integrating a commercial device on

the base bar of a bicycle during TT experiments .

However, this approach may not fully capture the

aerodynamics of the entire bike-rider system, under-

scoring the need for more accurate drag measure-

ments.

This study aims to investigate the effects of cy-

cling posture alterations on aerodynamic drag and up-

per limb muscle activity in recreational cyclists. It

ﬁrst examines the relationship between muscle activa-

tion contributions, forearm angles across posture vari-

ations, and aerodynamic drag, and further analyzed

data using Pareto optimization in search of optimal

postures. We hypothesize that the biceps brachii and

triceps brachii muscles will contribute the majority of

activation among the measured upper limb muscles,

and that aerodynamic drag-muscle activity will mani-

fest a linear relationship.

2 MATERIALS AND METHODS

2.1 Participants

Nine young male cyclists (age = 27.1 ± 2.0 years,

height = 180.0±6.3 cm, weight = 77.0±5.7 kg, mean

± SD) from the Hong Kong University of Science

and Technology (HKUST) voluntarily agreed to par-

ticipate in this study. A priori sample size calcula-

tion (G*Power, version 3.1.9.7, Kiel, Germany) de-

termined that 9 participants would provide 80% (α =

0.05) to achieve an effect size = 0.8 in muscle acti-

vation. Inclusion criteria required participants to be

competent in riding a bicycle without prior or cur-

rent professional training. Exclusion criteria included

any chronic or acute muscle injuries or mental disor-

ders that could adversely affect cycling performance.

This study was approved by the Human and Artefacts

Research Ethics Committee of HKUST (HREP-2023-

0145) and conducted in accordance with the ethical

principles of the Declaration of Helsinki. Written

informed consent was obtained from all participants

prior to the study.

2.2 Protocol

All subjects were invited to attend three experimental

sessions. Session I measured aerodynamic drag in a

wind tunnel (see Sec.2.3), while session II and III as-

sessed muscle activity (see Sec.2.4) on a bike trainer

(Wahoo Kickr Bike, Atlanta, USA) in the hoods and

drops hand position, respectively. The bike trainer,

located in an air-conditioned room of 23

◦

C, has a

power accuracy of ±1%.

In session I, all participants were asked to wear

cycling skinsuits, cycling shoes and road bike hel-

mets that ﬁtted to their individual body sizes to mini-

mize the aerodynamic inﬂuence of personal garments.

They exercised on a ﬁxed road bike positioned in

the wind tunnel, pedaling to drive the rear wheel at

a speed matching the wind tunnel’s airﬂow. Partic-

ipants maintained 10 distinct postures with varying

forearm angles in the hoods hand position, each held

for 30 s, with 30 s rest intervals between randomly se-

quenced postures. The same procedure was then re-

peated for 10 postures in the drops hand position. Fig-

ure 1 shows the postures adopted in the tests in hoods

and drops position, respectively. Posture 1 presents

a maximum forearm angle, equivalent to an upright

posture without arm bending. Posture 10 presents a

Balance of Upper Limb Muscle Activation and Aerodynamics for Cycling Posture Optimization

minimum forearm angle. The forearm angle is de-

ﬁned as the acute angle between a subject’s forearm

and a horizontal line, as shown in Fig.1a.

In Session II, participants replicated the 10 hoods

postures from the wind tunnel test in a randomized

order to reduce order effect. They performed these

postures on the bike trainer at a constant power output

of 100 W, pedaling at 60 rpm, a low exercise intensity

intended to reduce the impact of lower limb fatigue on

RPE evaluation. To mitigate fatigue effects on subse-

quent trials, a stop criterion was applied: participants

could end a trial either after maintaining a posture for

2 min or upon self-reporting a rated perceived exer-

tion (RPE) score of around 14 (equivalent to ”some-

what hard” to ”hard” on the Borg scale (Borg, 1982)).

Each trial was separated by a 3 min rest period. Ses-

sion III followed the same protocol as session II, but

with the drops hand position. For both session II and

III, participants were instructed to refrain from high-

intensity exercise for at least 24 hours prior to labora-

tory trials. Session II and III were conducted separat-

edly by at least 48 hours and were randomly assigned

to each participant.

2.3 Aerodynamic Force Measurement

Aerodynamic drag was measured in a low-speed wind

tunnel at the Aerodynamics and Acoustics Facility

(AAF), HKUST. The wind tunnel featured a closed

test section measuring 14 m × 2.5 m × 2 m. All mea-

surements were conducted at a constant wind speed

of 8 m/s, representative of typical leisure cycling

speeds. A road bike was mounted on a cycling aero-

dynamic test rig installed underneath the wind tunnel

ﬂoor for force measurements. Loads were recorded

using a six-component force balance with a measure-

ment accuracy within ±0.2 N (Mao et al., 2024).

Each force measurement was sampled 2 000 Hz for

a duration of 30 s.

2.4 EMG Recording

Muscle activity is measured using a surface EMG de-

vice (Cometa Systems PicoX and miniX, Bareggio,

Italy) with a sampling frequency of 2 000 Hz per

channel. EMG was recorded for six muscles on the

participants’ dominant side of the body: ﬂexor carpi

radialis (FCR), biceps brachii (BB), medial triceps

brachii (TB), anterior deltoids (AD), upper trapezius

(UP) and lumbar erector spinae (ES). Prior to elec-

trode application, the skin was shaved and cleaned

with alcoholic pads. A bilateral Ag/AgCl monitor-

ing electrode (3M Red Dot, MN, USA) was placed in

the middle of the muscle belly according to the sEMG

(a)

(b)

(c)

(d)

Figure 1: Postures in hoods and drops position.

icSPORTS 2025 - 13th International Conference on Sport Sciences Research and Technology Support

application standards (Stegeman and Hermens, 2007).

2.5 EMG Data Processing

Raw EMG signals were band-pass ﬁltered using a

-order Butterworth ﬁlter between 20 and 500 Hz,

followed by full-wave rectiﬁcation and root mean

square (rms) calculation with a window size of 0.5s,

as shown in Fig. 2. For each participant and for each

muscle, rms EMG was further normalized by its dy-

namic peak to obtain EMG linear envelope (Burden

and Bartlett, 1999). The dynamic peak was deter-

mined in a separate trial, where participants crouched

their upper body to the lowest position, maximally

contracted the target muscle, and pedaled at full

power for 10 s, this process was repeated three times,

and the average of the three trials was used as the dy-

namic peak value.

Figure 2: Sample EMG of TB muscle from a participant

in drops posture 7. Processed EMG refers to the rectiﬁed

EMG before performing rms calculation.

To assess the variation in muscle contributions

supporting upper body postures, muscle activation

weights were calculated as follows:

Weight

rms EMG

∑

j=1

rms EMG

, (1)

where Weight

represents the contribution of the i

muscle, and the denominator is the sum of root mean

square EMG values across all measured muscles.

The composite sum of the EMG signals was com-

puted to estimate overall muscle activity across the

measured muscle (Ingraham et al., 2019), deﬁned as:

Composite rms EMG =

∑

i=1

rms EMG

, (2)

where i denotes i

muscle ranging from 1 to 6 (corre-

sponding to the six muscles recorded).

2.6 Pareto Optimality Analysis

Pareto optimality, a foundational concept in eco-

nomics and management science, was introduced by

Pareto (1919). In multi-criteria decision-making, a

solution is considered Pareto optimal if no other so-

lution is at least as good across all criteria and better

in at least one criterion (Ehrgott, 2005). This is math-

ematically expressed as:

min{ f

(y), f

(y),..., f

(y), f

(y)}, (3)

where y = (y

,...,y

) represents the decision vari-

ables, f

denotes the objective functions, 1 ≤ m ≤ M

and M ≥ 2. In this context, M = 2, as two objective

functions were considered: drag area (C

A) and the

composite sum of EMG.

3 RESULTS AND DISCUSSION

3.1 Aerodynamic Drag and Postures

Aerodynamic drag measurement were performed at

wind speed of 8m/s. The drag area C

A is deﬁned as

A =

ρU

, (4)

where F

is the measured mean drag force, ρ is the

air density and U

denotes the ﬂow speed at the wind

tunnel outlet.

Figure 3 illustrates the variation in C

A across de-

scending forearm angles. The shaded regions repre-

sent 95% conﬁdence intervals, while data of all partic-

ipants are shown as dots. Both hoods and drops posi-

tions exhibited a strong correlation between C

A and

decreasing forearm angles. This indicates that lower-

ing the forearm angle and adopting a more crouched

upper body posture signiﬁcantly reduces drag area,

thereby enhancing cycling performance. The consis-

tent correlation across positions suggests that posture

optimization is a critical factor in aerodynamic efﬁ-

ciency.

3.2 Muscle Activation

Figure 4 illustrates the changes in muscle activation

weights for each measured muscle across postures,

with posture indices 1 to 10 corresponding to a shift

from an upright posture (maximum forearm angle) to

an aerodynamic posture (minimum forearm angle).

Error bars represent 95% conﬁdence intervals. The

results reveal that the TB muscle was the primary

contributor to overall muscle activity, accounting for

Balance of Upper Limb Muscle Activation and Aerodynamics for Cycling Posture Optimization

Figure 3: Drag area (C

A) versus forearm angles in in-

creased crouching down. Data of all participants are shown

as dots.

46.9% and 40.2% of activation in the hoods and drops

positions, respectively. In the hoods position, TB ac-

tivation typically increased as postures became more

aerodynamic, despite minor ﬂuctuations. Conversely,

in the drops position, TB activation decreased after

posture 7, possibly due to accelerated fatigue from

increased torso forward tilt, which heightens upper

arm loading to support body weight. Moreover, it is

observed that arm and chest muscles (FCR, BB, and

AD) were more recruited in drops, whereas shoulder

and back muscles (UT, and ES) showed greater acti-

vation in hoods.

3.3 Drag-Muscle Activity Relationship

Figure 5 displays the relationship between aerody-

namic drag and muscle activity, with data averaged

across participants for each posture. The composite

sum of EMG, representing total upper body muscle

activity, is plotted against C

A. Figure 5a shows the

Pareto front for individual participant (in colored dash

line) and the group trend (in red solid line). The ﬁnd-

ings suggest that drops postures generally provide a

better balance between aerodynamic drag and muscle

activity in a crouched position, compared to hoods

within the measured forearm angle range. On the

other hand, as the upper body shifts to a more up-

right posture, hoods postures’ Pareto points predomi-

nate, indicating that hoods may offer a more effective

trade-off.

Figure 5b highlights a strong negative linear re-

lationship between C

A and composite EMG, with

Pearson correlation coefﬁcients of r = −0.96 for

hoods and r = −0.94 for drops, indicating robust neg-

ative linearity. A linear regression model ﬁtted to

the averaged data across participants produced coef-

ﬁcients of determination (R

) of 0.93 for hoods and

(a)

(b)

Figure 4: Muscle activation contributions across different

postures in (a) hoods and (b) drops position.

0.91 for drops. These high R

values conﬁrm that

the regression model effectively captures the linear

relationship, indicating that as aerodynamic drag de-

creases in more aerodynamic postures, muscle activ-

ity increases. This trade-off highlights the importance

of balancing aerodynamic advantages with physiolog-

ical demands, as excessive muscle activation in ag-

gressive postures may lead to fatigue. The strong lin-

earity supports the hypothesis of a coupled relation-

ship between drag and muscle activity.

4 CONCLUSIONS

This study explored the relationship between aerody-

namic drag and upper limb muscle activity to identify

optimal cycling postures that balance aerodynamic ef-

ﬁciency with physiological costs. Aerodynamic drag

was measured in a controlled wind tunnel across var-

ious postures, while upper limb muscle activity was

assessed using surface EMG on a bike trainer, repli-

icSPORTS 2025 - 13th International Conference on Sport Sciences Research and Technology Support

(a)

(b)

Figure 5: Drag-muscle activity relationship in analysis of

(a) Pareto optimality and (b) linear regression for the group.

Each color (other than red) in (a) represents data from a

participant. Red color denotes group averaged data.

cating the wind tunnel postures. Pareto optimality and

linear regression analyses were used to examine the

trade-offs between drag and muscle activation.

Drag measurements showed a strong negative cor-

relation between drag area (C

A) and forearm an-

gle, conﬁrming that more aerodynamic postures sig-

niﬁcantly reduce drag. However, this beneﬁt comes

with increased muscle activation, as demonstrated

by EMG data. The triceps brachii was the primary

contributor to muscle activation across all postures

in both hoods and drops positions. Pareto optimal-

ity analysis indicated that drops positions optimize

crouched postures, while hoods positions better bal-

ance drag and muscle activation in upright postures.

This is consistent with linear regression intercepts:

in low-drag regions, drops require less muscle acti-

vation, but beyond the intersection point, hoods are

more efﬁcient for upright postures.

Despite these insights, the study has some limita-

tions. The participant group consisted solely of right-

handed male cyclists, and the small sample size and

limited number of muscles analyzed restrict the gen-

eralizability of the ﬁndings. Additionally, individual

EMG responses exhibited signiﬁcant variability, re-

ﬂecting diverse muscle activation patterns. Pareto op-

timality analysis further suggested that optimal hand

positions vary by individual, supporting the need for

personalized bike ﬁtting, as corroborated by Faulkner

et al. (Faulkner et al., 2024). Emerging research

highlights bidirectional and ipsilateral coupling be-

tween upper and lower limb muscle activation in cy-

cling, with greater variability in upper limb activation

(Huang and Ferris, 2009; Cartier et al., 2022). Future

studies could investigate upper and lower limb coordi-

nation to elucidate their combined impact on cycling

performance.

The ﬁndings offer practical guidance for cyclists

and coaches. For time trials prioritizing aerodynamic

efﬁciency, a drops position with a lower forearm an-

gle minimizes drag but increases activation of arm and

chest muscles (FCR, BB, AD), necessitating targeted

endurance training. For endurance rides prioritizing

comfort, a hoods position with a more upright pos-

ture reduces activation of shoulder and back muscles

(UT, ES), enhancing sustainability. Coaches can use

these insights to customize bike ﬁtting and training

regimens, balancing aerodynamic beneﬁts with phys-

iological demands. Regular EMG monitoring during

training can help identify fatigue thresholds and opti-

mize posture adjustments.

ACKNOWLEDGEMENTS

This work is partially supported by the Hong

Kong Innovation and Technology Commission

(No.ITS/101/23FP). The author would like to thank

HKUST for PhD sponsorship. This work was per-

formed in the Aerodynamics and Acoustics Facility

at HKUST (http://aaf.ust.hk).

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