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Neuromechanical control of the forearm musclesduring gripping with sudden flexion and extensionwrist perturbationsMichael W.R. Holmesa, Jimmy Tatb & Peter J. Keirb

a Faculty of Health Sciences, University of Ontario Institute of Technology, 2000 SimcoeStreet North, Oshawa, ON, CanadaL1H 7K4b Department of Kinesiology, McMaster University, Ivor Wynne Centre, Room 212, 1280 MainStreet West, Hamilton, ON, CanadaL8S 4K1Published online: 06 Nov 2014.

To cite this article: Michael W.R. Holmes, Jimmy Tat & Peter J. Keir (2015) Neuromechanical control of the forearm musclesduring gripping with sudden flexion and extension wrist perturbations, Computer Methods in Biomechanics and BiomedicalEngineering, 18:16, 1826-1834, DOI: 10.1080/10255842.2014.976811

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Neuromechanical control of the forearm muscles during gripping with sudden flexion andextension wrist perturbations

Michael W.R. Holmesa1, Jimmy Tatb2 and Peter J. Keirb*aFaculty of Health Sciences, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, Canada L1H 7K4;

bDepartment of Kinesiology, McMaster University, Ivor Wynne Centre, Room 212, 1280 Main Street West, Hamilton, ON,Canada L8S 4K1

(Received 2 February 2014; accepted 12 October 2014)

The purpose of this study was to investigate how gripping modulates forearm muscle co-contraction prior to and duringsudden wrist perturbations. Ten males performed a sub-maximal gripping task (no grip, 5% and 10% of maximum) while aperturbation forced wrist flexion or extension. Wrist joint angles and activity from 11 muscles were used to determineforearm co-contraction and muscle contributions to wrist joint stiffness. Co-contraction increased in all pairs as grip forceincreased (from no grip to 10% grip), corresponding to a 36% increase in overall wrist joint stiffness. Inclusion of individualmuscle contributions to wrist joint stiffness enhanced the understanding of forearm co-contraction. The extensor carpiradialis longus (ECRL) and brevis had the largest stiffness contributions (34.5 ^ 1.3% and 20.5 ^ 2.3%, respectively), yetmuscle pairs including ECRL produced the lowest co-contraction. The muscles contributing most to wrist stiffness wereconsistent across conditions (ECRL for extensors; Flexor Digitorum Superficialis for flexors), suggesting enhancedcontributions rather than muscular redistribution. This work provides investigation of the neuromuscular response to wristperturbations and gripping demands by considering both co-contraction and muscle contributions to joint stiffness.Individual muscle stiffness contributions can be used to enhance the understanding of forearm muscle control duringcomplex tasks.

Keywords: forearm; wrist; gripping; co-contraction; biomechanical modeling

1. Introduction

The forearm and hand represent a redundant musculoske-

letal system with a complex arrangement of muscles that

elegantly coordinate movements to perform daily tasks.

To interact with our environment, forearmmuscles transfer

loads across the wrist joint and balance moments created by

the finger flexor and extensor muscles. Uncoordinated

muscle actions may lead to wrist joint instability and/or

injury. Quantifying co-contraction and stabilizing contri-

butions from individual muscles would enhance our

knowledge of neuromechanical control and improve our

understanding of wrist joint loading and injury risk.

Muscle co-contraction is closely linked to joint

stiffness (De Serres and Milner 1991; Cholewicki and

McGill 1996). Co-contraction of the wrist extensors has

been observed during gripping (Mogk and Keir 2003) to

help stiffen and stabilize the wrist joint (De Serres and

Milner 1991). Hand posture and grip force have large

effects on forearm muscle activity (Mogk and Keir 2003),

yet there is limited information on forearm muscle activity

prior to and during sudden externally applied loads as most

forearm experiments have used isometric gripping and

static postures. In addition to the co-contraction required

during gripping, perturbing the system allows a window

into how the forearm muscles resist wrist rotation.

Potvin and Brown (2005) demonstrated that individual

muscle contributions to resist joint rotation can be

determined knowing muscle geometry, force and stiffness.

Thus, despite the relationship between co-contraction and

stiffness, evaluating just co-contraction may not be

sufficient to interpret joint stiffness. Used in combination

with co-contraction measures, evaluating stiffness should

enhance our understanding of how the neuromuscular

system prepares for sudden joint loading. This joint

stiffness approach is closely related to dynamic stability

using Lyapunov analyses of kinematic data in the spine

(Graham and Brown 2012). The equation of Potvin and

Brown (2005) is particularly appropriate for analyzing

static pre-perturbation states in which the stretch reflex can

significantly modify mechanical joint impedance (Halaki

et al. 2012). Recently, Pfeifer et al. (2012) validated a

similar approach using an electromyography (EMG)-

assisted model to estimate knee joint stiffness. This

equation has been used to calculate individual muscle

contributions at the knee (Cort and Potvin 2012), spine

(Howarth et al. 2008) and elbow (Holmes and Keir 2013).

Wrist stiffness is typically evaluated at the endpoint of a

perturbation or movement with little evaluation of how

individual muscles regulate stiffness (Franklin et al. 2003;

Krutky et al. 2010). There are few experimental models

q 2014 Taylor & Francis

*Corresponding author. Email: pjkeir@mcmaster.ca

Computer Methods in Biomechanics and Biomedical Engineering, 2015

Vol. 18, No. 16, 1826–1834, http://dx.doi.org/10.1080/10255842.2014.976811

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that consider both stiffness and co-contraction; thus, this

approach can provide insight into the neuromechanical

processes that govern forearm muscle co-contraction

(Pfeifer et al. 2012). The purpose of this study was to

quantify forearm muscle co-contraction with sudden

perturbations of wrist flexion and extension under different

preload conditions. Individual muscle contributions were

quantified and used to enhance the link between forearm

muscle co-contraction and the neuromechanical control of

wrist joint stiffness.

2. Methods

2.1 Participants

Ten right-handed healthy male volunteers participated.

Participant age, height, mass, arm length, and maximum

grip force are found in Table 1. This study was approved by

the McMaster University Human Research Ethics board.

Each participant provided written informed consent.

2.2 Experimental procedures

Participants performed a sub-maximal gripping task while

a pneumatic perturbation device delivered a push force

causing wrist rotation. Participants stood next to a waist-

high table with feet shoulder width apart. The right arm

was positioned above the table surface with 908 elbow

flexion, 08 shoulder flexion and abduction, forearm mid-

prone, and neutral wrist. Participants held a grip

dynamometer (MIE Medical Research Ltd, Leeds, UK;

mass ¼ 450 g) attached to a Plexiglase apparatus

(mass ¼ 210 g) which provided a rigid target for the

perturbation (Figure 1).

The perturbation device rested against the grip

apparatus and was positioned to deliver a push force that

caused wrist flexion or extension. The forearm rested on a

padded surface that extended to the wrist, and the hand and

gripping apparatus were unsupported. Restraints main-

tained forearm alignment. The device was adjusted to

apply the perturbation in the same location for each

participant (volar aspect just proximal to the third

metacarpal).

Prior to each perturbation, participants performed one

of three gripping tasks while holding the dynamometer: (i)

no grip requirement, (ii) maintaining 5% maximum grip

force, and (iii) maintaining 10% maximum grip force. The

dynamometer had a fixed grip span of 5 cm. Grip precision

was set to ^1.5% of maximum using visual feedback

(LabView 8.5, National Instruments, Austin, TX, USA).

Trials were repeated if the grip level was not achieved.

Perturbations were applied with known and unknown

timing. For known timing, participants self-initiated the

perturbation using a manual trigger with their left hand.

For unknown timing, the perturbation occurred randomly

within 10 s of the experimenter signaling the start of the

trial. Perturbation direction was performed in a semi-

random order. All gripping and timing conditions were

completed in one direction before the second direction.

Three trials were performed for each combination of

perturbation direction, grip, and timing condition with 30 s

rest between trials.

Table 1. Mean participant anthropometrics and maximum gripstrength (SD).

Anthropometrics Mean ^ SD

Age (years) 22.7 ^ 2.7Height (m) 1.78 ^ 0.06Mass (kg) 77.0 ^ 11.3Forearm length (cm) 28.1 ^ 1.4Hand length (cm) 23.6 ^ 8.4Max grip (N) 502.2 ^ 88.2

Figure 1. Participant preparing for a perturbation. The perturbation device (and load cell) could be adjusted to impact the same locationfor each participant.

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2.3 Data collection and instrumentation

Surface EMG was collected from 11 muscles of the right

upper extremity: triceps brachii lateral head, biceps brachii

long head, brachialis, brachioradialis, flexor carpi radialis

(FCR), flexor carpi ulnaris (FCU), flexor digitorum

superficialis (FDS), extensor carpi radialis longus

(ECRL), extensor carpi radialis brevis (ECRB), extensor

carpi ulnaris (ECU), and extensor digitorum communis

(ED). Electrode placements were determined by guide-

lines recommended by Perotto (2005). Following shaving

and scrubbing with alcohol, disposable bipolar Ag–AgCl

surface electrodes (MediTrace 130, Kendall, Mansfield,

MA, USA) were placed over each muscle belly in line with

muscle fiber orientation (inter-electrode distance of

2.5 cm). EMG signals were band-pass filtered (10–

1000Hz) and differentially amplified (CMRR . 115 dB

at 60Hz; input impedance ,10GV; AMT-8, Bortec

Biomedical Ltd, Calgary, AB, Canada).

A quiet EMG trial was collected and maximal

voluntary excitations (MVE) were determined using

muscle specific maximal voluntary isometric contractions

(MVC), each held for 3 s. Forearm MVCs consisted of

multiple tests involving manually resisted, isometric wrist

flexion, extension, and deviation, both with and without

simultaneously producing a maximal grip. This ensured

that both the wrist and finger flexors and extensors were

activated. MVCs were performed twice for each muscle

and maximal grip force, with a minimum 30-s rest between

exertions. The MVE and maximal grip force were defined

as the peak from all trials.

The perturbation device consisted of a metal rod

(1.0 cm diameter, 20 cm long) housed in an aluminum

cylinder that would thrust the rod outward to deliver a push

force when filled with compressed air. Perturbation force

was measured by load cell (MPL-50, Transducer

Techniques, Temecula, CA, USA). EMG, grip force, and

load cell data were sampled at 2048Hz (USB-6229 BNC,

National Instruments). Hand posture was collected using

an electromagnetic system (FASTRAKw, Polhemus Ltd,

Colchester, VT, USA) with a sensor attached to the mid-

point of the dorsal aspect of the third metacarpal

(Wigderowitz et al. 2007) and calibrated in the neutral

starting posture. Kinematics were sampled at 75Hz and

synchronized with EMG and force data.

2.4 Data analysis

The bias (from quiet trials) was removed from each EMG

channel prior to full-wave rectifying and low-pass filtering

(3Hz, second order, single pass Butterworth). Signals

were then normalized to MVE for each muscle. Load cell

and wrist motion data were dual low-pass filtered at 10Hz

(second order Butterworth). A pressure sensor in the

perturbation device indicated the start of a perturbation.

All data were examined during three time periods: (i)

baseline (150–100ms pre-perturbation), (ii) anticipatory

(15–0ms pre-perturbation), and (iii) reflex (25–150ms

post-perturbation).

Following EMG processing, all 11 muscles listed

above were paired together for a muscle co-contraction

index (CCI), resulting in 55 muscle pairs (Lewek et al.

2004), using Equation (1).

CCI ¼XNi¼1

EMGlowðiÞEMGhighðiÞ

� �EMGlowðiÞ þ EMGhighðiÞ� �� �

;

ð1Þ

where N represents the number of data points in the

calculation, i is the sample, EMGlow, and EMGhigh

represent lowest and highest normalized EMG for the

muscle pair, respectively. The CCI was divided by time,

for each time period.

Using OpenSIM (Delp et al. 2007), an upper extremity

model (Holzbaur et al. 2005) was reduced to 26 muscles

that cross the wrist joint, then further reduced to include

only those muscles measured accurately with surface

EMG, leaving 13 muscles (Table 2). In the model, FDS

and ED cross the wrist joint as four tendons that attach to

the digits of the hand. We used FDS and ED activity from

the bulk of each muscle to drive each compartment.

Instantaneous muscle parameters and EMG were used to

evaluate muscle force using a Hill-type muscle model

(Delp and Loan 1995) with inputs of instantaneous length,

velocity, and moment arm derived from wrist and forearm

angles.

Muscle specific, three-dimensional anatomical coordi-

nates (representing origin, insertion, and node/wrap

points) and calculated muscle forces were extracted from

Table 2. List of muscles included in the model that cross thewrist joint.

Muscle Abbreviation

Extensor carpi radialis longus ECRLExtensor carpi radialis brevis ECRBExtensor carpi ulnaris ECUExtensor digitorum communisa

Digit 2 ED2Digit 3 ED3Digit 4 ED4Digit 5 ED5

Flexor carpi radialis FCRFlexor carpi ulnaris FCUFlexor digitorum superficialisb

Digit 2 FDS2Digit 3 FDS3Digit 4 FDS4Digit 5 FDS5

a Indicates that one activation drives four modeled ED compartments.b Indicates that one activation drives four modeled FDS compartments.2 ¼ index finger; 3 ¼ middle finger; 4 ¼ ring finger; 5 ¼ little finger.

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OpenSIM and used to calculate joint stiffness (Equation

(2)) (Matlab, R2008a, The Mathworks, Inc., Natick, MA,

USA).

JRSðmÞz ¼ FAxBx þ AyBy 2 r2z

lþ qr 2z

L

� �; ð2Þ

where JRS is the joint rotational stiffness contribution of

each muscle (m) about the z axis (flexion–extension) of

the wrist joint. F is the calculated muscle force, l is the

length of the muscle vector crossing the wrist joint, L

represents the length of the entire muscle, r is the moment

arm, Ax, and Ay represent origin coordinates of the muscle,

Bx and By represent insertion/node coordinates with

respect to the wrist joint. The constant, q, relates muscle

force and length to muscle stiffness and was set to 10

(Potvin and Brown 2005).

For each trial, muscle contributions were summed to

represent total joint rotational stiffness (JRST). Each

muscle contribution was normalized to JRST at each time

point to represent its relative contribution. Individual

muscle contributions (expressed as %JRST) were calcu-

lated for the two time periods immediately prior to the

perturbation (baseline and anticipatory) for rotations about

all axes. JRST was also expressed as a percentage of the

theoretical maximum JRS (MJRS), calculated as the

stiffness associated with maximal forearm extensor

activity and the required forearm flexor activity to

maintain static equilibrium of the wrist joint in the neutral

posture. Each JRST was normalized as a percent of MJRS.

2.5 Statistical analysis

Data were averaged across the three trials for each

condition. A 2 £ 2 £ 3 £ 3 repeated measures ANOVA

evaluated the effects of perturbation timing knowledge

(known, unknown), perturbation direction (flexion, exten-

sion), grip level (no grip, 5% MVC, 10% MVC), and time

period (baseline, anticipatory, reflex). Dependent variables

included grip force, push force, wrist angle, and CCI for

each muscle combination. A 2 £ 2 £ 3 £ 2 repeated

measures ANOVA evaluated the effects of timing

knowledge, direction, and grip level on JRST and MJRS

in the pre-perturbation time periods. Significant effects

were compared with Tukey’s HSD test. An alpha level of

0.05 was used for all analyses (SPSS v13.0, IBM

Corporation, Somers, NY, USA).

3. Results

3.1 Perturbation push force, grip force, and wristkinematics

There were no significant differences in perturbation push

force across trials (mean ^ SD, 15.8 ^ 2.6N). There was

a significant effect of grip level (F2,18 . 299.6, p , 0.000)

with all three tasks differing significantly. The mean grip

force recorded during the baseline time period with no grip

requirement, 5% MVC and 10% MVC trials were

4.2 ^ 0.3%, 7.3 ^ 0.2%, and 11.5 ^ 0.4%MVC, respect-

ively. There were no differences in grip force due to

perturbation direction or time period. Peak wrist flexion

and extension angles were consistent when averaged

across all conditions (timing knowledge and grip level),

with flexion producing 33.2 ^ 9.18 and extension

32.8 ^ 5.88. There was a significant timing knowledge

£ time period interaction for wrist angle ( p ¼ 0.001).

During the reflex period, peak wrist flexion was 6.28greater during the known conditions than the

unknown conditions (36.3 ^ 7.18 and 30.1 ^ 9.88, for

known and unknown conditions, respectively). Peak wrist

extension was 2.48 greater during the known conditions

than the unknown conditions (34.0 ^ 5.38 and

31.6 ^ 6.18, for known and unknown conditions, respect-

ively). Ensemble averages of the wrist kinematics can be

found in Figure 2.

3.2 Muscle co-contraction

Five muscle combinations are highlighted to represent

forearm muscle co-contraction (ECRL–FCR, ED–FDS,

ECU–FCU, ECRL–ED, FCR–FCU). Three of the muscle

pairings (ECRL–ED, ED–FDS, and ECRL-FCR) demon-

strated a significant direction £ grip interaction (all

F2,18 . 3.62, all p , 0.048). During trials with no

required grip force, wrist flexion perturbations produced

an ED–FDS CCI 1.5 times less than wrist extension, with

no differences for the other two grip levels (Figure 3). For

ECRL–ED, the CCI during no grip requirement, 5% and

10% MVC trials was ,1.5 times larger with flexion than

extension. For extension, the ECU–FCU CCI for no grip,

5%, and 10% MVC trials were 2.6, 2.0, and 1.8 times

larger than flexion, respectively.

Time period had a significant effect on CCI for all five

pairings, increasing from baseline to the anticipatory

period (all F2,18 . 6.96, all p , 0.006; Figure 4). During

the reflex period, CCI was greater than baseline for all

pairings and greater than the anticipatory period for

ECRL–ED, ECU–FCU and ECRL–FCR. During the

reflex and anticipatory periods, mean CCI were 2.8 and 2.3

times larger than baseline, respectively, for all five

pairings.

Grip had a significant effect on CCI for all muscle

pairings (all F2,18 . 8.05, all p , 0.003). ECRL–FCR

CCI increased from no grip requirement to the 5%

grip. For other pairings, CCI was greater with 10% grip

than 5% and no required grip. ECU–FCU CCI was

significantly greater with unknown (5.79 ^ 2.59) versus

known timing (4.24 ^ 2.05) (F1,9 ¼ 18.4, p ¼ 0.002).

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3.3 Maximum joint rotational stiffness

MJRS was greatest for the flexion–extension axis,

followed by pronation/supination and radial/ulnar devi-

ation (18.5, 11.8, and 10.9Nm/rad, respectively). Only the

flexion–extension axis will be presented. A significant

direction £ grip interaction (F2,18 ¼ 6.7, p ¼ 0.007) was

found for MJRS. During flexion perturbations, the no grip

required condition was 10.5 ^ 1.9% MJRS while the 10%

MVC grip was 14.4 ^ 2.6% MJRS, a 36.4% increase

(Figure 5). This effect was smaller during extension

perturbations, with a 24.8% increase in MJRS. Time

period significantly affected stiffness (F1,9 ¼ 44.2,

p ¼ 0.0001) with the anticipatory period being 35%

greater than baseline (13.2 ^ 2.2% versus 9.7 ^ 1.6%

MJRS, respectively).

3.4 Individual muscle contributions to JRS

The ranked order of relative muscle contributions

remained consistent, regardless of experimental condition

as seen in Figure 6 for baseline. At baseline, the largest

contributions were ECRL (34.5 ^ 1.3% JRST) and ECRB

(20.5 ^ 2.3% JRST). The four compartments of ED (ED2,

3, 4, 5) combined to contribute 13.0 ^ 0.5% JRST, while

FDS (FDS2, 3, 4, 5) contributed 16.7 ^ 3.9% JRST. FDS2

and FDS3 contributed most of the FDS stiffness at

Figure 2. Ensemble average of wrist kinematics during known and unknown timing perturbations. Dotted lines represent SD. Negativerepresents wrist extension; positive, wrist flexion. (A and D) No grip; (B and E) 5% MVC grip; (C and F) 10% MVC grip. Perturbationoccurred at 300ms.

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7.0 ^ 0.9% JRST and 8.0 ^ 0.9% JRST, respectively,

while ED2 contributed the most to ED stiffness

(3.8 ^ 0.4% JRST; Figure 7).

4. Discussion

This study developed a gripping paradigm to modulate

muscle preload to assess muscular contributions to wrist

joint stiffness prior to and during sudden perturbations.

Across five forearm muscle pairs, co-contraction increased

with grip demand. Using a 10% grip resulted in a 36.4%

increase in total joint stiffness over trials with no grip

requirement, confirming that a slight increase in grip force

greatly increases wrist joint stiffness prior to a sudden

flexion perturbation. While grip force did not change

between the baseline and anticipatory time periods, wrist

joint stiffness increased with an increase in co-contraction

in the muscle pairs tested. Individual muscle contributions

to joint stiffness provided further insight distinct from

common co-contraction measures. We found that the

greatest contributors to stiffness were consistent across

conditions, representing an up-regulation of stiffness with

grip force rather than a redistribution of muscle

requirements. The interpretation of ECRL’s role in wrist

stiffness was most influenced by using both methods. CCI

was lowest for pairs that included ECRL, yet ECRL

provided the greatest joint stiffness contribution. Further-

more, despite large CCI magnitudes involving FCR, the

stiffness approach demonstrated that it is a poor wrist

stabilizer.

We found a neuromuscular response that stiffened the

wrist joint by 35% between the baseline and anticipatory

Figure 3. Mean muscle CCI (with SD) demonstrating the effects of grip and perturbation direction. NG, no grip; 5%, 5% MVC grip;10%, 10% MVC grip. See text for muscle abbreviations.

Figure 4. Mean muscle CCI (with SD) during the three time periods. These data were averaged across flexion and extension conditions.Significance is indicated for individual time period comparisons, ‘*’ p , 0.05. See text for muscle abbreviations.

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periods that was independent of grip force. In addition,

wrist stiffness was enhanced regardless of knowing when

the perturbation would arrive. By modulating the pre-

perturbation grip force, the level of forearm co-contraction

prior to each perturbation was also increased, which differs

from previous studies that typically have no enhanced

muscle pre-activation (Milner 2002; Krutky et al. 2010).

We found the five CCIs to increase but each pair by a

different magnitude. By interpreting with stiffness,

individual muscles contributing greatly (ECRL, ECRB,

and FDS) were implicated as most responsible in the CCI.

We demonstrated that an increase in grip force, leads to

forearm co-contraction and enhanced wrist stiffness prior

to sudden perturbations. The JRS technique was applied to

the pre-perturbation phases of our movement and

evaluating wrist kinematics during the post-perturbation

phase provides another assessment technique, in addition

to co-contraction, on how the forearm muscles resist wrist

motion. There was significantly less wrist flexion and

extension during the unknown timing perturbations when

compared to the known timing perturbations (Figure 2).

Participants did increase co-contraction during unknown

perturbations; however, this was not significant. Despite

being a metabolically inefficient strategy (Hogan 1984),

this co-contraction provides enhanced stiffness and likely

contributed to the reduced peak wrist angles found in our

kinematic data.

Our findings enhance the interpretation of how the

neuromuscular system modulates stiffness by inducing

background muscle activity prior to sudden perturbations

by evaluating both individual muscle stiffness and co-

contraction. We used low grip forces, yet stiffness, during

the flexion perturbations increased from 10.5% MJRS

during no required grip force to 14.4%MJRS at 10%MVC

(Figure 5). The larger MJRS change was found during

flexion perturbations and this appears to support the wrist

kinematics. On average, peak wrist flexion was 2.18 lessduring the 10% MVC conditions than during the no

required grip conditions, while there was only a 0.48reduction in peak wrist extension due to the 10% MVC

grip. While it has been documented that co-contraction is

related to joint stiffness, previous evaluations quantified

endpoint stiffness (De Serres and Milner 1991; Franklin

et al. 2003). Averaged across all trials, ECRL and ECRB

were the largest contributors to wrist stiffness (Figure 6),

partly due to having the largest physiological cross

sectional areas. However, muscle pairs that included

ECRL produced the lowest CCI (Figure 2). Individual

stiffness calculations highlight the importance of ECRL to

overall wrist joint stiffness and this large contribution

(34.5 ^ 1.3% JRST) was inadequately represented by the

CCI. Previously, we demonstrated that ECR also

helps resist elbow joint perturbations (Holmes and Keir

Figure 5. Muscle contribution to JRS normalized to themaximum potential for our wrist model during the flexion/extension axis (%MJRS ^ SD). The effects of grip level andperturbation direction are highlighted.

Figure 6. Mean muscle contribution to JRS (% JRST ^ SD) for all muscles during the baseline time period, averaged across allexperimental conditions. See Table 2 for muscle abbreviations.

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2012, 2014) suggesting the forearm extensors contribute to

both elbow and wrist stiffness.

In an attempt to better understand muscle recruitment

strategies, we also considered synergistic contractions.

The FCR–FCU pairing demonstrated a large CCI and

considering only the co-contraction approach, we could

conclude that the pairing provides substantial wrist joint

stiffness; however, the stiffness evaluation suggests

otherwise. FCR made only a small contribution to wrist

stiffness, likely due to its small force generating capacity

(Gonzalez et al. 1997) and little need for FCR activity at

10% MVC grip. Claudon (1998) found that the forearm

flexors are preferentially activated at high force levels,

while Mogk and Keir (2003) demonstrated much greater

extensor versus flexor (relative) activity during a 5%

grip. Additionally, it has been suggested that ECU and

FCU co-contraction increases with increasing load

instability, thereby increasing wrist stiffness (De Serres

and Milner 1991; Milner 2002). Our CCI for ECU–FCU

increased with grip but the increase was similar to other

pairings. This further emphasizes our hypothesis that both

CCI and stiffness are needed to understand neuromech-

anical forearm control. Of further interest, De Serres and

Milner (1991) found that FCR activity remained

unchanged by co-contraction of the wrist extensors,

whereas FCU activity increased substantially. The FCR–

FCU pairing produced the largest CCI increase of all

pairings from the baseline to anticipatory time period

(Figure 2). Despite this increase (CCI of 7.5–18.0), FCU

and FCR contributed little to wrist stiffness (5.9 ^ 0.7%

and 0.6 ^ 0.01% JRST, respectively). This suggests that

for a low grip force task, FCR is not an important stabilizer

of the wrist due to its anatomical geometry (independent of

muscle force). At low force levels, the wrist extensors

balance moments and forces generated by the finger

flexors, resulting in only a small force contribution from

FCR and a minimal stabilizing requirement.

There are a few limitations that should be considered.

While crosstalk is always a concern when investigating

forearm muscles, care was taken to ensure accurate

electrode placement. Crosstalk can be minimized with

proper electrode configuration (Mogk and Keir 2003).

Yung and Wells (2013) examined crosstalk using

ultrasound to map muscles and their data support the

electrode placements used in this study. Due to surface

EMG limitations, deep forearm muscles were not

monitored or included in our calculations. Buchanan

et al. (1993) found that many of the muscles omitted from

our model generate minimal wrist moments and are likely

not large contributors to wrist joint stiffness. The joint

stiffness approach was used to avoid the complexity

associated with other stability calculations (Cholewicki

and McGill 1996; Granata and England 2006) yet still

incorporate damping through muscle modeling. Unlike

endpoint stiffness and joint impedance calculations

(Hogan, 1984; Franklin et al. 2003), our approach does

not directly incorporate inertia of the hand. However, the

equation used allows for individual muscle interpretations

rather than overall joint parameters.

5. Conclusion

This study found that muscular contributions increased

wrist joint stiffness immediately prior to a sudden

perturbation without concomitant increase in grip force.

For a relatively low-level gripping task, forearm muscle

co-contraction resulted in a 36.4% increase in wrist joint

stiffness. This study documented individual forearm

muscle contributions to wrist joint stiffness and found

that the ECR had the largest contributions while the

superficial finger flexors had the largest flexor contri-

butions. By quantifying stiffness and co-contraction

indices, we are better able to comprehend how the

Figure 7. Mean muscle contribution to JRS (% JRST ^ SD) for each muscle compartment that was summated in Figure 5 to representED and FDS, averaged across all experimental conditions for the baseline time period. See Table 2 for muscle abbreviations.

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neuromuscular system selects muscles to stiffen a joint, and

provide a margin of safety during sudden loading with

different grip force requirements. We found that individual

muscle stiffness and CCImethods were complementary with

situations where stiffness enhanced interpretation of the

control of the forearm muscles during wrist perturbations.

Acknowledgement

Special thanks to Dr Jim Potvin.

Funding

Fundedby anNSERCDiscoveryGrant [grant number #217382-09].

Notes

1. Email: Michael.Holmes@uoit.ca2. Email:tatj2@mcmaster.ca

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