ARTICLE IN PRESS
Journal of Biomechanics 40 (2007) 3013–3022
www.elsevier.com/locate/jbiomech
www.JBiomech.com
The contribution of the wrist, elbow and shoulder joints to
single-finger tapping
Jack T. Dennerleina,, Idsart Kingmab, Bart Visserb, Jaap H. van Dieënb
a
Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02139, USA
Institute for Fundamental and Clinical Human Movement Sciences, Faculty of Human Movement Sciences, VU University Amsterdam, Van der
Boechorststraat 9, Amsterdam 1081 BT, The Netherlands
b
Accepted 30 January 2007
Abstract
We aimed to determine the role of the wrist, elbow and shoulder joints to single-finger tapping. Six human subjects tapped with their
index finger at a rate of 3 taps/s on a keyswitch across five conditions, one freestyle (FS) and four instructed tapping strategies. The four
instructed conditions were to tap on a keyswitch using the finger joint only (FO), the wrist joint only (WO), the elbow joint only (EO),
and the shoulder joint only (SO). A single-axis force plate measured the fingertip force. An infra-red active-marker three-dimensional
motion analysis system measured the movement of the fingertip, hand, forearm, upper arm and trunk. Inverse dynamics estimated joint
torques for the metacarpal–phalangeal (MCP), wrist, elbow, and shoulder joints. For FS tapping 27%, 56%, and 18% of the vertical
fingertip movement were a result of flexion of the MCP joint and wrist joint and extension of the elbow joint, respectively. During the FS
movements the net joint powers between the MCP, wrist and elbow were positively correlated (correlation coefficients between 0.46 and
0.76) suggesting synergistic efforts. For the instructed tapping strategies (FO, WO, EO, and SO), correlations decreased to values below
0.35 suggesting relatively independent control of the different joints. For FS tapping, the kinematic and kinetic data indicate that the
wrist and elbow contribute significantly, working in synergy with the finger joints to create the fingertip tapping task.
r 2007 Published by Elsevier Ltd.
Keywords: Tapping; Finger; Hand; Wrist; Forearm; Elbow; Shoulder; Upper extremity; Motor control; Joint power; Computer keyswitch
1. Introduction
Computer work has long been associated with musculoskeletal disorders (MSDs) of the upper extremity
(Faucett and Rempel, 1994; Bergqvist et al., 1995; Gerr
et al., 2002). These disorders span the entire upper
extremity including the hand, wrist, forearm, elbow,
shoulder and neck. Static loading and postures are possible
risk factors (e.g. Marcus et al., 2002). However; when force
and movement are combined, risk is usually multiplicative
suggesting that dynamic loading may play an important
role (Silverstein et al., 1986; Marras, 1992). For example,
Schoenmarklin et al. (1994) illustrated that acceleration in
the flexion/extension plane of the wrist provided the best
discrimination between groups with low and high rates of
Corresponding author. Tel.: +1 617 432 2028; fax: +1 617 432 3468.
E-mail address: jax@hsph.harvard.edu (J.T. Dennerlein).
0021-9290/$ - see front matter r 2007 Published by Elsevier Ltd.
doi:10.1016/j.jbiomech.2007.01.025
MSDs. The high prevalence of MSDs among sign language
interpreters also indicates that such dynamic loads even
without the presence of external force may be an important
factor (Smith et al., 2000; Scheuerle et al., 2000).
While disorders affect the various parts of the upper
extremity, dynamic analysis for keyboard work has been
limited to distal joints of the upper extremity, with static
postural analysis has been applied to the more proximal
joints. Both Dennerlein et al. (1999) and Harding et al.
(1993) reported that forearm muscles that articulate the
finger were often loaded more than the fingertip, while no
effect of the impact forces in the proximal tissues was
found (Dennerlein et al., 1999). Serina et al. (1999)
measured and reported that wrist velocities and accelerations are high, similar to those reported for general
industrial tasks associated with MSDs; however, they did
not examine forces and torques at the joint. For the more
proximal joints there are both laboratory and field studies
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for the upper arm and neck posture that suggest maintaining specific static postures may be risk factors for the upper
extremity MSDs (Ortiz et al., 1997; Marcus et al., 2002;
Zennaro et al., 2004).
Typing style may play a role in the development of
computer work-related MSDs. Pascarelli and Kella (1993)
described seven computer work styles that they felt had
biomechanical risks for typist including the infamous hunt
and peck style or two finger typing (Baker and Redfern,
2005) and forceful typing. In addition, instructions for
movement strategies are often used for specific interventions. However, differences in typing style have been
quantified with biomechanical measures in only a single
study of a single joint where the PIP joint flexion was found
to be greater among touch typists (Preite and Haslegrave,
2003).
Compared to typing, tapping is a simple and cyclic task
that has often been used for examination of the dynamics
of the finger for keyboard tasks (Kuo et al., 2006; Jindrich
et al., 2004) and the dynamics of the upper extremity (Aoki
et al., 2001; Aoki and Kinoshita, 2001; Zennaro et al.,
2004; Schnoz et al., 2000). Tapping provides a first step in
understanding a fundamental component of the typing
action (Flanders and Soechting, 1992).
Since dynamic analysis has focused on the distal joints
during tapping and typing, our goal was to determine the
dynamic role of the finger, wrist, elbow and shoulder joints
as well as the kinematics of the hand, forearm and upper
arm segments during single-finger tapping. Specifically we
quantified the dynamic contribution of each joint to the
fingertip movement, while subjects tapped at 3 Hz on a
keyswitch. In addition, subjects also tapped using different
instructed tapping strategies attempting to isolate movement to a single joint: the metacarpal joint for the index
finger, the wrist, the elbow and the shoulder joint
emulating exaggerated typing styles. Based on observations
of people tapping and the fact that previous studies have
emphasized the dynamic nature of the distal joints and the
static for the proximal joints we expect that the vertical
movement of the fingertip and work done by the joints
during tapping are generated by a combination of finger
joint and wrist movements only. Since typing styles are
defined by different motions observed we also expect that
the contribution of the individual joints to the fingertip
motion and joint work will differ across the different
instructed tapping strategies, while the fingertip force
characteristics do not differ.
2. Methods
A total of 6 human subjects (2 female, 4 male) ranging in age from 30 to
41 participated in the study. The instrumentation and protocols were
approved by the Human Subjects Committee at the Vrije Universiteit,
Amsterdam. Participants tapped with the right index finger across five
different conditions, while sitting in a chair adjusted such that the elbow
and tapping surfaces were at the same height. No forearm support was
provided. Participants were instructed to minimize contact time with the
tapping surface and to tap in synchrony with an electronic metronome at 3
taps/s. Subjects were also instructed to keep the middle, ring, and little
fingers in a similar posture as the index finger emulating their expected
posture during touch-typing.
The five tapping conditions consisted of one freestyle (FS) and four
instructed tapping strategies. The FS condition was tapping on a
keyswitch. The four instructed strategies were tapping on the keyswitch
with the instruction to use only finger flexion–extension (FO), tapping on a
keyswitch with the instruction to use only wrist flexion–extension (WO),
tapping on a keyswitch with the instruction to use only elbow
flexion–extension (EO) and tapping on a keyswitch using only shoulder
motion (SO). Subjects were specifically instructed to tap using the specific
joint of interest, while keeping the other joints as fixed as possible. The key
switch used was harvested from an Apple Extended II keyboard with an
activation force of 0.62 N and force–displacement characteristics described
by Rempel et al. (1994). Subjects practiced the movement before data
collection. Since we wanted the FS condition not to be influenced by the
other four conditions, the FS condition was run first. The instructed
conditions were then run in a distal to proximal joint order.
Three z-bar strain-gauge force transducers (Futek, model FP1146300533-B, 0.5 kg, Irvine, CA, USA) mounted between an aluminum base
plate and a lightweight stiff foam-shell plate (Kappas Plast, Alcan
Composites, /www.alcancomposites.comS) measured the fingertip tapping forces. The base of the keyswitch was mounted with double-sided
tape on top of the foam-shell plate. Amplified signals from each of the
three transducers were digitally recorded at 200 samples/s. The system
output was linear with an RMS error of 0.10 N between 0 and 5.4 N. The
force signal was low pass filtered digitally using a fourth order Butterworth filter with a cutoff frequency of 20 Hz and zero phase shift.
An active-marker infrared three-dimensional (3-D) motion analysis
system (Optotrak, Northern Digital, Ontario, CN) measured the upper
extremity kinematics. Four clusters of three markers secured on a flat plate
were mounted on the hand, forearm, upper arm and torso and a single
marker on the fingertip. 3-D locations of these markers were recorded with
a single three-camera unit at 200 samples/s. All kinematic data were
digitally low pass filtered with a fourth order Butterworth filter with a
cutoff frequency of 10 Hz and zero phase shift. Using the relationships to
the anatomical landmarks and anthropometrical data, the position of each
segment’s center of mass position, its inertia tensors and orientation were
calculated at each instant of time based upon the 3-D trajectories of the
markers (Cappozzo et al., 1995; McConville et al., 1980; Veldpaus et al.,
1988). Finger kinematics were estimated from the anatomical landmarks
of the MCP joints and the fingertip position using the assumption that the
DIP and PIP joint angles are dependent (Buchner et al., 1988; Buchholz
et al., 1992).
Estimates for the velocities and accelerations of center of mass of the
hand, forearm, and upper arm were obtained through digital differentiation using a Lanczos five-point differentiator. The angular velocities and
accelerations were obtained from segment orientation matrices according
to Berme et al. (1990). A 3-D multi-segment inverse dynamic model
(Kingma et al., 1996; Kuo et al., 2006) calculated net reactive joint forces
and moments for the MCP, wrist, elbow and shoulder joints. The unused
fingers were assumed to be fixed to the hand and their inertia properties
were included in the hand segment. The potential and kinetic energy were
calculated for each segment. Based on the joint torque and relative joint
movements, the joint power and the mechanical work done at the joints
were calculated for each joint (Zajac et al., 2002). Work done by the
fingertip on the keyswitch and work done on the shoulder were also
calculated to determine the total work done on the upper extremity.
Data for the last 16 taps collected over the 10 s of data collection were
averaged within each condition for each subject. The kinematic and kinetic
data were aligned at the maximum tapping force (assigned time ¼ 0).
From the average fingertip motion and force data the beginning of
downward finger movement, the beginning and end of contact, and the
end of the upward finger movement were identified. Summary statistics
(mean, standard deviation, maxmin) for the kinematic and kinetic
parameters were then calculated over the appropriate portions of the tap.
To test our first hypothesis that the vertical movement of the fingertip is
generated by a combination of finger and wrist joint movements only, we
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experimental conditions as the independent variables (FS, FO, WO, EO,
SO). When the ANOVA demonstrated significance (p-values less than
0.05), planned comparisons (paired contrasts) were made only between the
FS keyswitch and each of the four instructed conditions.
compared three joint parameters across the different joints within the FS
condition. Using a 1-way repeated measures analysis of variance
(ANOVA) with joint as the independent variable (MCP, wrist, elbow
and shoulder), we compared joint flexion/extension excursion, joint’s
contribution to the vertical fingertip movement, and work done by the
joint. Since the parameters violated the sphericity assumption the degrees
of freedom of the ANOVA were adjusted with Huynh–Feldt epsilon
corrections. When the ANOVA demonstrated significance (p-values less
than 0.05), Tukey post-hoc comparisons were made to determine
differences in the parameters between the different joints. To explore
differences between the instructed conditions and the FS tapping
condition, tap summary statistics were analyzed using a 1-way repeated
measures ANOVA (with Huynh–Feldt epsilon corrections) with the five
3. Results
While the finger and wrist joints demonstrated the
largest range of motion during the FS tapping condition,
the elbow joint contributed substantially to the joint
powers and to a lesser extent to the actual movement of
FS
0.5
Angle (radians)
Wrist
A
B
C
D
E
Elbow
0
Shoulder
MCP
-0.5
-0.2
-0.1
0
0.1
Time(s)
0.2
WO
FO
0.5
0.5
A
B
C
D
E
Elbow
0
Wrist A
Angle (radians)
Angle (radians)
Wrist
Shoulder
MCP
B
D
E
Elbow
Shoulder
0
MCP
-0.5
-0.5
-0.2
-0.1
0
0.1
Time(s)
-0.2
0.2
-0.1
0
0.1
Time(s)
EO
0.2
SO
0.5
0.5
B C
D
E
Elbow
Shoulder
0
MCP
-0.5
A
Wrist
Angle (radians)
A
Wrist
Angle (radians)
C
B
C
D
E
Elbow
0
Shoulder
MCP
-0.5
-0.2
-0.1
0
0.1
Time(s)
0.2
-0.2
-0.1
0
0.1
Time(s)
0.2
Fig. 1. Average joint angles across subjects (N ¼ 6) for the five experimental conditions. Solid blue is MCP flexion, dashed green is wrist flexion, dotted
red is elbow extension (neutral 901), and dash–dot black is shoulder extension. Taps were aligned at the maximum force (time ¼ 0, point C). The five
highlighted points during the tap cycle are: (A) the start of the down stroke, (B) the beginning of contact, (C) maximum tip force, (D) end of contact, and
(E) the beginning of next downswing. The five experimental tapping conditions were free style (FS), finger only (FO), wrist only (WO), elbow only (EO)
and shoulder only (SO).
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Table 1
Joint parameters for the joints of the upper extremity within the freestyle condition
Parameter
ANOVA resultsa
MCP
Joint excursion (rad)
F(2.6, 13.2) ¼ 17.1
p ¼ 0.000
0.11 (0.01)A
Resulting finger tip movement (mm)
F(1.3, 6.5) ¼ 6.8
p ¼ 0.033
Work done (mJ)b
F(1.1, 5.7) ¼ 11.45
p ¼ 0.01
Wrist
Elbow
0.07 (0.01)A
5.3 (1.7)A,B
0.01 (0.01)B
9.9 (1.7)A
1.5 (4.9)A
22.9 (4.9)B
Shoulder
0.00 (0.01)B
3.5 (1.7)A,B
0.0 (1.7)B
34.2 (4.9)B
2.9 (4.9)A
the fingertip (Fig. 1 and Table 1). The range of motion of
the finger MCP joint and wrist joints were 0.11 and
0.07 rad, respectively, whereas the elbow’s excursion was
on average 0.01 rad. However, the range of motion of the
elbow contributed 18% of the 2.07 cm of vertical fingertip
movement and 56% of the total work done on the upper
extremity. The finger contributed 27% of the movement
and only 2% of the work done. The wrist contributed 43%
of the movement and 32% of the work.
During the FS tap, the wrist and the finger joints moved
together except during the contact period (Fig. 1) where the
MCP extended during the loading portion of contact (B–C)
then flexed during the unloading portion of contact (C–D).
The correlation coefficients of movement between the two
joints calculated from the continuous data of the 16 FS
taps for each subject ranged from 0.05 to 0.51 (across
subject averages are presented in Table 3). The flexion–
extension movement of the elbow joint was very small,
however, it did follow a similar pattern to the wrist angle
and was correlated with the wrist and to a lesser extent with
the MCP joint. The net joint power for the finger joint was
correlated with wrist (range: 0.24–0.67) and elbow (range:
0.33–0.68). The wrist and elbow demonstrated the highest
correlation with coefficients ranging from 0.51 to 0.89. The
movement of the shoulder was not large enough to be
observed visually and its relative contribution to motion
and power was much smaller than of the other joints
(Figs. 2 and 6). There was hardly any correlation between
the shoulder and the MCP joint for either flexion–extension or for joint power (Table 3). These general patterns
were observed across all subjects.
The fingertip force parameters for the four instructed
tapping strategies (FO, WO, EO, and SO), did not
significantly vary from the FS condition except for the
duration of contact, which was significantly shorter for the
EO and SO conditions compared to the FS condition
(Table 2). In contrast, the kinematics and the kinetics for
the four instructed tapping strategies differed from the FS
condition. As subjects were instructed to use specific
motor strategies, the range of motion for the specified
joint significantly increased relative to the FS condition
Percentage of vertical finger movement
Mean (standard error) values are presented. The superscript letter attached to the values reports the results from the Tukey post-hoc analysis. Values with
the same letter denotes group without significant differences. Values with different letters are ranked such that A4B4C.
a
1-way repeated measures ANOVA with Huynh–Feldt epsilon correction. Bold values indicate statistically significant results.
b
Work done was calculated between points A and C in Figs. 1, 4 and 5.
100
MCP
Wrist
Elbow
Shoulder FE
Shoulder Mov
f
80
e
e
60
40
w
w
20
w
0
FS
-20
s
f
FO
WO
EO
*
s
f
SO
*
-40
Fig. 2. The contribution of the joint movements to the vertical fingertip
motion during the down swing (points A–C in Fig. 1) across the five
experimental conditions. Both the wrist and the elbow contribute to the
fingertip movement during free-style tapping on a keyswitch (FS). When
more proximal joints are used the finger joint extends during contact
demonstrated by the negative contribution to the fingertip movement. The
shoulder joint contributes little to fingertip motion and subjects increased
both rotation and translation of the shoulder joint to create the vertical
movement in the shoulder only condition (SO). Across subject mean
values with standard errors are presented. ANOVA and planned
comparisons indicated that statistically significant differences were
observed across conditions with reference to the free-style condition
(FS) for the MCP denoted with the letter f (F(2.0, 9.8) ¼ 25.6, p ¼ 0.000),
wrist denoted with the letter w (F(2.7, 13.3) ¼ 24.8, p ¼ 0.000), elbow
denoted by the letter e (F(2.7, 13.5) ¼ 31.6, p ¼ 0.000) and the translation
of the shoulder denoted with the letter s (F(1.1, 5.3) ¼ 9.3, p ¼ 0.026).
(Fig. 1; Table 2). For example, when subjects were
instructed to create the tapping motion with only the
finger joints (FO), the range of motion of the finger joint
significantly increased nearly three-fold compared to the
FS condition, whereas the wrist and elbow range of motion
decreased though not significantly. For the WO condition,
the excursion of the wrist angle increased significantly
nearly three-fold relative to the FS condition. The subjects
did not use pure joint rotation of the shoulder when
instructed to create the tapping motion with the shoulder
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Table 2
Tap fingertip force, joint excursion and joint torque mean values (standard deviation) across the five instructed tapping conditionsa
Fingertip force
Average (N)
Maximum (N)
Duration (s)
Vertical travel (cm)
Joint excursionsc
MCP (rad)
Wrist (rad)
Elbow (rad)
Shoulder (rad)
Average joint torques
MCP (Nmm)
Peak to peak MCP (Nmm)
Wrist (Nm)
Elbow (Nm)
Shoulder (Nm)
ANOVA valuesb
Freestyle (FS) Finger only (FO) Wrist only (WO) Elbow only (EO) Shoulder only (SO)
F(1.9,
F(2.3,
F(4.0,
F(1.5,
9.53) ¼ 3.3, p ¼ 0.083
11.4) ¼ 2.7, p ¼ 0.109
20.0) ¼ 7.3, p ¼ 0.001
7.4) ¼ 3.5, p ¼ 0.093
0.54
1.11
0.15
2.07
(0.13)
(0.29)
(0.04)
(0.87)
0.58
1.21
0.15
2.30
(0.19)
(0.46)
(0.04)
(0.87)
0.52
1.17
0.12
3.54
(0.15)
(0.43)
(0.03)
(0.87)
0.59
1.27
0.11
4.28
(0.20)
(0.45)
(0.03)
(0.87)
0.80
1.65
0.11
5.23
(0.33)
(0.66)
(0.03)
(0.87)
F(2.5,
F(4.0,
F(1.2,
F(1.1,
12.4) ¼ 5.2, p ¼ 0.018
20.0) ¼ 19.8, p ¼ 0.000
5.9) ¼ 10.4, p ¼ 0.017
5.4) ¼ 12.0, p ¼ 0.018
0.11
0.07
0.01
0.00
(0.04)
(0.04)
(0.00)
(0.00)
0.24
0.03
0.01
0.00
(0.099)d
(0.01)
(0.01)
(0.00)
0.13
0.18
0.01
0.00
(0.10)
(0.07)
(0.01)
(0.00)
0.07
0.09
0.07
0.01
(0.04)
(0.06)
(0.03)
(0.00)
0.09
0.04
0.08
0.04
(0.02)
(0.03)
(0.06)
(0.03)
F(2.5,
F(2.3,
F(3.8,
F(3.4,
F(2.5,
12.2) ¼ 2.7,
11.7) ¼ 3.2,
18.9) ¼ 1.7,
17.1) ¼ 1.6,
12.3) ¼ 0.8,
3
84
0.4
3.8
4.6
(10)
(16)
(0.1)
(0.6)
(1.7)
5
93
0.4
3.9
4.3
(7)
(42)
(0.1)
(0.5)
(1.8)
10
92
0.4
3.9
4.4
(13)
(33)
(0.1)
(0.6)
(1.8)
7
103
0.4
3.8
4.7
(8)
(51)
(0.1)
(0.6)
(1.5)
3
135
0.4
3.8
4.3
(9)
(61)
(0.1)
(0.5)
(1.6)
p ¼ 0.096
p ¼ 0.072
p ¼ 0.190
p ¼ 0.213
p ¼ 0.507
a
Bold values were statistically different than the freestyle condition (FS) in the planned comparison.
1-way repeated measures ANOVA with Huynh–Feldt epsilon correction. Bold values indicate statistically significant results.
c
Excursions were the difference between the maximum and minimum joint angles within the average tap cycle.
d
This value was marginally insignificantly different than the free-style (FS) condition with a comparison p-value of 0.057.
b
Table 3
Across subject averaged (and across subject standard deviation) cross-correlation coefficients
Freestyle (FS)
Finger only (FO)
Wrist only (WO)
Elbow only (EO)
Shoulder (SO)
Cross-correlation coefficients for joint angles
MCP with wrist
0.33 (0.17)
MCP with elbow
0.16 (0.30)
MCP with shoulder
0.00 (0.17)b
0.27 (0.41)
0.00 (0.35)a
0.00 (0.05)c
0.28 (0.41)
0.01 (0.30)a
0.14 (0.25)a
0.16 (0.40)
0.50 (0.34)b
0.35 (0.20)
0.04 (0.47)
0.40 (0.38)
0.06 (0.40)a
Wrist with elbow
Wrist with shoulder
0.30 (0.35)
0.02 (0.08)b
0.02 (0.43)a
0.02 (0.34)
0.16 (0.42)
0.20 (0.23)
0.46 (0.32)c
0.05 (0.38)a
0.19 (0.45)
0.13 (0.31)
Elbow with shoulder
0.25 (0.17)
0.21 (0.32)
0.23 (0.18)a
0.22 (0.16)
0.03 (0.58)a
Cross-correlation coefficients for joint power
MCP with wrist
0.53 (0.15)
MCP with elbow
0.46 (0.12)
MCP with shoulder
0.01 (0.05)b
0.35 (0.35)
0.15 (0.28)
0.03 (0.17)b
0.30 (0.40)a
0.15 (0.13)a
0.00 (0.32)b
0.07 (0.21)a
0.04 (0.23)
0.20 (0.13)
0.03 (0.27)a
0.04 (0.17)
0.14 (0.11)a
Wrist with elbow
Wrist with shoulder
0.72 (0.08)
0.08 (0.19)
0.43 (0.08)
0.03 (0.27)
0.30 (0.08)
0.40 (0.34)a
0.84 (0.04)
0.19 (0.27)
0.76 (0.08)a
0.06 (0.44)a
Elbow with shoulder
0.09 (0.15)
0.26 (0.32)
0.20 (0.37)a
0.06 (0.29)a
0.17 (0.53)a
Each subject’s coefficient was calculated over the continuous data spanning the 16 taps for each condition.
a
One of the six subjects did not demonstrate a significant (i.e. p40.05) correlation.
b
Two of the six subjects did not demonstrate a significant (i.e. p40.05) correlation.
c
Three of the six subjects did not demonstrate a significant (i.e. p40.05) correlation.
joint. Rather subjects often included translation of their
shoulder joint to move the entire limb (Fig. 2). Correlations
between joint angles and net joint powers patterns were less
strong for the instructed tapping strategies (FO, WO, EO,
and SO) compared to the FS condition (Table 3).
The relative finger joint motion behaved differently for
the proximal joint movement strategies in that it extended
during loading. When the fingertip was not in contact with
the keyswitch the MCP joint was relatively motionless;
however, during the contact portion the MCP joint
extended during loading and then flexed during the
unloading section (Fig. 1). This is reflected in the negative
contribution to the fingertip motion (Fig. 2), which was
significantly different than the FS condition.
The system and joint kinetics varied across the conditions as seen in the system kinetic energy (Table 4, Fig. 3),
the change in joint torques (Fig. 4) and the joint powers
(Table 2, Fig. 5). As more proximal joint strategies were
employed, the maximum kinetic energy, the change in
potential energy and work done on the upper extremity by
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Table 4
Upper extremity kinetic energy, potential energy, and work donea
ANOVA valuesb
Max kinetic energy (mJ)c
Change kinetic energy (mJ)d
Change potential energy (mJ)d
Joint work done (mJ)d
Freestyle (FS) Finger only (FO) Wrist only (WO) Elbow only (EO) Shoulder (SO)
F(1.1, 5.4) ¼ 5.0, p ¼ 0.071
6.4 (6.4)
Ff(1.1, 5.4) ¼ 1.8, p ¼ 0.238 0.32 (0.72)
F(1.0, 5.2) ¼ 7.9, p ¼ 0.036 62 (19)
F(1.1, 5.4) ¼ 10.0, p ¼ 0.022 63 (21)
4.2
0.28
34
31
(4.1)
(0.45)
(24)
(16)
19
0.22
64
74
(13)
(1.3)
(27)
(49)
53
0.70
348
352
(44)
(2.03)
(190)
(192)
104
6.8
697
626
(1 1 0)
(11.7)
(588)
(469)
Mean and (standard deviation) values are presented.
a
Bold values were statistically different than the freestyle condition (FS) in the planned comparison.
b
1-way repeated measures ANOVA with Huynh–Feldt epsilon correction. Bold values indicate statistically significant results.
c
Maximum kinetic energy observed across the average tap cycle, points A–E in Figs. 1, 4 and 5.
d
Change in energy and work done was calculated between points A and C in Figs. 1, 4 and 5.
Finger
Hand
Forearm
Upper Arm
Percentage of total kinetic energy
70
60
50
h
h
a
40
30
a
20
u
f
10
f
0
FS
FO
WO
EO
SO
Fig. 3. The average contribution of each segment to the overall kinetic
energy of the upper extremity during the tap cycle (A–E). Kinetic energy
of the hand dominates the kinetic energy of the whole arm. Across subject
mean values with standard errors are presented. Absolute kinetic energy
values are presented in Table 2. ANOVA and planned comparisons
indicated that statistically significant differences were observed across
conditions with reference to the freestyle condition (FS) for the finger
denoted with the letter f (F(1.2, 5.7) ¼ 9.3, p ¼ 0.022), hand denoted with
the letter h (F(3.9, 19.2) ¼ 13.4, p ¼ 0.000), forearm denoted by the letter a
(F(3.8, 18.8) ¼ 27.6, p ¼ 0.000), and the upper arm denoted with the letter
u (F(1.9, 9.6) ¼ 23.0, p ¼ 0.000).
the joints increased. This increase was statistically significant for the change in potential energy as well as for the
joint work. The work done on the upper extremity was very
similar to the changes in potential and kinetic energy
(Table 4).
4. Discussion
The goal of this study was to determine and characterize
the dynamic repetitive loading of the upper extremity
during a single-finger tapping task and to investigate
specific instructed tapping strategies that reflect to some
extent different typing styles and potential interventions.
The results suggest that tapping is a complicated movement
of the entire upper extremity that involves not only the
finger and wrist joint, but dynamic loading of the elbow
and to a lesser extent the shoulder. Hence, the data reject
our hypothesis that only the wrist and finger joints
contribute to the fingertip motion. Furthermore, more
proximal movement strategies required more joint power
than distal joint strategies without drastically changing the
tapping task performance.
We see that the finger, wrist and elbow joints work
together to create the fingertip tapping motion, whereas the
shoulder appears to be stationary during the tapping
exercise. For the FS keyswitch tapping condition, the
elbow, wrist and finger joint power productions are
correlated, whereas the shoulder has very small correlations with the other joints (Fig. 5, Table 2). Since many of
the muscles for the fingers crossing the wrist have the same
function (i.e. finger flexors act as wrist flexors and finger
extensors act as wrist extensors) one can expect that these
two joints will work in a synergistic fashion.
The elbow, while having a very small excursion but large
moment arm relative to the fingertip position, also acts
with the finger and wrist joint to create the tapping motion.
This was not expected. The muscles that articulate the
finger and wrist originate at the epicondyles; however, they
have little to no moment arms at the elbow (Murray et al.,
1995). Pronation and supination muscles such as the
pronator teres do cross the elbow (Murray et al., 1995).
Tapping with the index finger may produce some torque
about the pronation/supination axis and therefore muscles
that cross the elbow may work in synergy with the muscles
that cross the wrist.
The shoulder joint, whose movement and power poorly
correlated with the elbow and the wrist, may be acting as a
mechanical base for the kinematic chain. As such the
shoulder may act in a passive manner being perturbed by
the dynamics of the distal links rather than actively
contributing to the fingertip movement. As a base, the
shoulder has to maintain stability to counteract the
perturbations from the dynamics of the distal links of
the chain. As a result there may be a high level of muscle
coactivity at the shoulder (and neck). However it is
impossible to infer the specific level of (co-)activity from
measurements of net joint torques.
Comparing the FS movement of tapping on a keyswitch
(FS) and the four instructed conditions (FO, WO, EO, and
ARTICLE IN PRESS
J.T. Dennerlein et al. / Journal of Biomechanics 40 (2007) 3013–3022
3019
FS
0.5
Torque N-m
A
B
C
D
E
Wrist
MCP
0
Shoulder
Elbow
-0.5
-0.2
-0.1
0
0.1
Time (s)
0.2
FO
WO
0.5
A
B
C
0.5
E
D
A
B C
D
Wrist
E
Torque N-m
Torque N-m
MCP
Wrist
MCP
0
0
-0.5
Elbow
Elbow
Shoulder
Shoulder
-0.5
-1
-0.2
-0.1
0
0.1
Time (s)
0.2
-0.2
-0.1
0
0.1
Time (s)
EO
2
A
B C
SO
D
2
E
1
A
B
C
D
E
1
MCP
MCP
0
Torque N-m
Torque N-m
0.2
Wrist
-1
-2
0
Wrist
-1
Elbow
-2
Elbow
-3
Shoulder
-4
Shoulder
-3
-4
-0.2
-0.1
0
0.1
Time (s)
0.2
-0.2
-0.1
0
0.1
Time (s)
0.2
Fig. 4. Average joint torque patterns across the taps: solid blue line is MCP flexion, dashed green is wrist extension, dotted red is elbow extension (neutral
901), and dash–dot black is shoulder extension. For the wrist, elbow and shoulder the maximum absolute torques were subtracted from the torque to plot
the patterns on similar scales. Different vertical scales are used for each plot above. The average absolute torques are presented in Table 1. Taps were
aligned at the maximum force (time ¼ 0). The five points of the tap cycle are: (A) the start of the down stroke, (B) the beginning of contact, (C) maximum
tip force, (D) end of contact, and (E) the beginning of next downswing.
SO) further suggests that the FS movement consists of
finger, wrist and elbow efforts. The instructed tapping
strategies provided a basis of joint movements that make
up the FS movements. For example, in Fig. 6 the
proportion of work done by the wrist joint is between the
FO and the WO and the work done by the elbow is
between the FO and the EO conditions.
The more proximal movement strategies caused increasing dynamic loads on the proximal joints on top of their
static postural loads, which can be attributed to the fact
that these strategies required movement of more inertia.
These proximal strategies, however, did not decrease the
torques at the distal joints nor the fingertip loading
parameters (Table 1). The increased loading on the
proximal joints suggests that for unsupported work more
proximal strategies for computer input devices may
increase risk for discomfort. While the more proximal
muscles are stronger and therefore will likely have larger
tolerances to these dynamic loading they still have to
maintain larger static loads associated with the unsupported kinematic chain. On the other hand, the proximal
strategies do add larger variations onto the static postural
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3020
FS
0.5
Power N-m/s
Elbow
Shoulder
0
MCP
A
Wrist
B C
D
E
0
0.1
Time (s)
0.2
-0.5
-0.2
-0.1
FO
WO
0.5
1
Power N-m/s
Power N-m/s
Elbow
MCP
0
Wrist
A
B
Shoulder
C
0.5
MCP
Wrist
0
Elbow
Shoulder
-0.5
D
A
E
-0.5
B C
D
E
-1
-0.2
-0.1
0
0.1
Time (s)
0.2
-0.2
-0.1
0
0.1
Time (s)
EO
0.2
SO
5
5
Power N-m/s
Power N-m/s
Wrist
MCP
0
Shoulder
MCP
0
Shoulder
Elbow
Wrist
Elbow
A
B C
D
E
A
B
C
D
E
-5
-5
-0.2
-0.1
0
0.1
Time (s)
0.2
-0.2
-0.1
0
0.1
Time (s)
0.2
Fig. 5. Average three-dimensional joint powers across subjects (N ¼ 6 solid blue is MCP flexion, dashed green is wrist extension, dotted red is elbow
extension (neutral 901), and dash–dot black is shoulder extension. Taps were aligned at the maximum force (time ¼ 0). The five points of the tap cycle are:
(A) the start of the down stroke, (B) the beginning of contact, (C) maximum tip force, (D) end of contact, and (E) the beginning of next downswing.
torques at the elbow and shoulder, which may avoid
sustained motor unit recruitment in shoulder muscles
(Thorn et al., 2002; Westgaard and de Luca, 1999).
We observed the phenomenon of the MCP joint
extending during contact for almost all of the conditions
except for the FO condition. This extension is similar to
observations of interphalangeal joint extensions during
finger tapping with the palm of the hand resting and fixed
on a surface (Jindrich et al., 2004; Kuo et al., 2006). This
suggests that the MCP may act as a spring during contact
with the keyswitch and that the finger flexor muscles may
undergo eccentric muscle contractions. However, the
muscles also cross the wrist joint which undergoes an
opposite pattern. A musculoskeletal model is needed to
determine the loading and excursion of the finger flexor
muscles during tapping, to determine if the muscles are
indeed undergoing eccentric contractions (Zajac et al.,
2002).
Generalizing these results is limited by a number of
factors. First, these conclusions are only for a cyclical
tapping condition at a specified rate of 3 taps/s. Other
factors may affect the relationships including the tapping
rate, level of coactivity of the fingers, and the synchronous
cyclical tapping task. The tapping task was chosen for its
simplicity removing the possible effects of other factors,
each of which deserve their own examination. For example,
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J.T. Dennerlein et al. / Journal of Biomechanics 40 (2007) 3013–3022
MCP
120%
Wrist
Elbow
Shoulder
w
100%
Percentage of work
e
80%
60%
s
40%
e
20%
w
w
0%
FS
FO
WO
EO
SO
-20%
-40%
Fig. 6. Contribution of each joint to the total work done on the upper
extremity. Across subject mean values with standard errors are presented.
Work done was calculated from point A (initiation of fingertip downward
movement) to point C (maximum fingertip force). The relative contribution of the wrist and the elbow is large for most conditions, even for the
finger-only condition, suggesting they have an important role in creating
the tap. For the shoulder condition the total contribution of the upper
extremity is much lesser than 100% of the work done on the upper
extremity because other trunk and postural joints moved the shoulder
joint. ANOVA and planned comparisons indicated that statistically
significant differences were observed across conditions with reference to
the freestyle condition (FS) for the wrist denoted with the letter w (F(1.8,
8.9) ¼ 12.9, p ¼ 0.003), elbow denoted by the letter e (F(2.6, 13.2) ¼ 4.1,
p ¼ 0.034), and the shoulder denoted with the letter s (F(4, 20) ¼ 6.4,
p ¼ 0.002).
Aoki and Kinoshita (2001) report that different motor
strategies are used for multiple-finger tapping compared to
single-finger tapping and hence touch-typing will be
different. Nonetheless, the tapping task provides a basis
for exploring the kinematic characteristics of the upper
extremity during a very simple distal task of the fingertip.
In addition, the task lasted for a short period of time, long
enough to obtain 16 taps for averaging and therefore our
results do not reflect possible changes due to longer
exposure to the tapping exercise.
Our tasks were completed without forearm supports.
The kinematics and kinetics will change with the addition
of forearm supports, especially the overall torque at the
shoulder as well as the dynamic contributions of the elbow
joint. Forearm supports have been shown to reduce
shoulder load and musculoskeletal symptoms (Visser et
al., 2000; Rempel et al., 2006) suggesting prolonged
shoulder loads should and can in this way be avoided.
Furthermore, the joint torques calculated do not explore
the specific roles of the muscles that cross these joints. The
muscles must act to generate torques and joint powers, but
a detailed biomechanical model would be necessary to
explore the relationship between these torques and the
muscle forces (Zajac et al., 2002).
There are limitations in the measurements and estimation of joint torques associated with using a skin-mounted,
optical-marker motion analysis system and inverse dynamics. Inverse dynamics and the multilink model have
3021
classic limitations well described in the literature, such as
the time and posture invariant joint centers, and rigid link
assumptions. In addition the sampling frequency was
relatively low compared to the impact typical of tapping
on a key (Rempel et al., 1994). However, impact forces
have not been observed at the proximal joints (Dennerlein
et al., 1999; Dennerlein, 2005) and Bisseling and Hof (2006)
recommend that impact forces not be included in the
inverse dynamics when studying overuse injuries. Hence
the maximum force reported here is not the impact force
observed by Rempel et al. (1994) but more representative
of the maximum tendon forces observed proximal to the
fingertip. The small number of subjects will also limit the
conclusions. Finally, the single joint movements were
difficult for the subjects to complete, especially the
shoulder motion only.
In conclusion, for 3-Hz cyclical tapping, the finger, wrist
and elbow joints work in synergy to produce the fingertip
motion with a majority of the movement being generated
by the finger and the wrist joint. The role of the shoulder
joint was less clear. While the specific instructed tapping
strategies isolated movement to a single joint of the upper
extremity, correlations between the joint powers decreased
and proximal movement strategies were associated with
increased joint power and energy.
Acknowledgments
In addition to the volunteers who participated in the
study, the corresponding author thanks the Harvard
School of Public Health Junior Faculty Sabbatical
Program and his hosts at the VU University Amsterdam.
References
Aoki, T., Kinoshita, H., 2001. Temporal and force characteristics of fast
double-finger, single-finger and hand tapping. Ergonomics 44,
1368–1383.
Aoki, T., Francis, P.R., Kinoshita, H., 2003. Differences in the abilities of
individual fingers during the performance of fast, repetitive tapping
movements. Experimental Brain Research 152, 270–280.
Baker, N.A., Redfern, M.S., 2005. Developing an observational instrument to evaluate personal computer keyboarding style. Applied
Ergonomics 36, 345–354.
Bergqvist, U., Wolgast, E., Nilsson, B., Voss, M., 1995. Musculoskeletal
disorders among visual display terminal workers: individual,
ergonomic, and work organizational factors. Ergonomics 38,
763–776.
Berme, N., Capozzo, A., Meglan, J., 1990. Rigid body mechanics as
applied to human movement studies. In: Berme, N., Capozzo, A.,
Berme, N., Capozzo, A.s. (Eds.), Biomechanics of Human Movement:
Applications in Rehabilitation, Sports and Ergonomics. Bertec,
Worthington, OH, pp. 89–107.
Bisseling, R.W., Hof, A.L., 2006. Handling of impact forces in inverse
dynamics. Journal of Biomechanics 39 (13), 2438–2444.
Buchholz, B., Armstrong, T.J., Goldstein, S.A., 1992. Anthropometric
data for describing the kinematics of the human hand. Ergonomics 35,
261–273.
Buchner, H.J., Hines, M.J., Hemami, H., 1988. A dynamic model for
finger interphalangeal coordination. Journal of Biomechanics 21,
459–468.
ARTICLE IN PRESS
3022
J.T. Dennerlein et al. / Journal of Biomechanics 40 (2007) 3013–3022
Cappozzo, A., Catani, F., Croce, U.D., Leardini, A., 1995. Position and
orientation in space of bones during movement: anatomical frame
definition and determination. Clinical Biomechanics (Bristol, Avon)
10, 171–178.
Dennerlein, J.T., 2005. Finger flexor tendon forces are a complex function
of finger joint motions and fingertip forces. Journal of Hand Therapy
18, 120–127.
Dennerlein, J.T., Diao, E., Mote Jr., C.D., Rempel, D.M., 1999. In vivo
finger flexor tendon force while tapping on a keyswitch. Journal of
Orthopaedic Research 17, 178–184.
Faucett, J., Rempel, D., 1994. VDT-related musculoskeletal symptoms:
interactions between work posture and psychosocial work factors.
American Journal of Industrial Medicine 26, 597–612.
Flanders, M., Soechting, J.F., 1992. Kinematics of typing: parallel control
of the two hands. Journal of Neurophysiology 67, 1264–1274.
Gerr, F., Marcus, M., Ensor, C., Kleinbaum, D., Cohen, S., Edwards, A.,
Gentry, E., Ortiz, D.J., Monteilh, C., 2002. A prospective study of
computer users: I. Study design and incidence of musculoskeletal
symptoms and disorders. American Journal of Industrial Medicine 41,
221–235.
Harding, D.C., Brandt, K.D., Hillberry, B.M., 1993. Finger joint force
minimization in pianists using optimization techniques. Journal of
Biomechanics 26, 1403–1412.
Jindrich, D.L., Balakrishnan, A.D., Dennerlein, J.T., 2004. Finger joint
impedance during tapping on a computer keyswitch. Journal of
Biomechanics 37, 1589–1596.
Kingma, I., Toussaint, H.M., De Looze, M.P., Van Dieen, J.H., 1996.
Segment inertial parameter evaluation in two anthropometric models
by application of a dynamic linked segment model. Journal of
Biomechanics 29, 693–704.
Kuo, P.L., Lee, D.L., Jindrich, D.L., Dennerlein, J.T., 2006. Finger joint
coordination during tapping. Journal of Biomechanics 39 (16),
2934–2942.
Marras, W.S., 1992. Toward an understanding of dynamic variables in
ergonomics. Occupational Medicine 7, 655–677.
Marcus, M., Gerr, F., Monteilh, C., Ortiz, D.J., Gentry, E., Cohen, S.,
Edwards, A., Ensor, C., Kleinbaum, D., 2002. A prospective study of
computer users: II. Postural risk factors for musculoskeletal symptoms
and disorders. American Journal of Industrial Medicine 41, 236–249.
McConville, J.T., Churchill, T.D., Kaleps, I., Clauser, C.E., Cuzzi, J.,
1980. Anthropometric relationships of body and body segment
moments of inertia. Air Force Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio, AFAMRL-TR-80–119.
Murray, W.M., Delp, S.L., Buchanan, T.S., 1995. Variation of muscle
moment arms with elbow and forearm position. Journal of Biomechanics 28, 513–525.
Ortiz, D.J., Marcus, M., Gerr, F., Jones, W., Cohen, S., 1997.
Measurement variability in upper extremity posture among VDT
users. Applied Ergonomics 28, 139–143.
Pascarelli, E.F., Kella, J.J., 1993. Soft-tissue injuries related to use of the
computer keyboard. A clinical study of 53 severely injured persons.
Journal of Occupational Medicine 35, 522–532.
Preite, R., Haslegrave, C., 2003. Typing style and index finger movements
of skilled and unskilled typists. In: Proceedings of the 15th Triennial
Congress of the International Ergonomics Association, IEA 2003,
Seoul, South Korea.
Rempel, D., Dennerlein, J., Mote Jr., C.D., Armstrong, T., 1994. A
method of measuring fingertip loading during keyboard use. Journal of
Biomechanics 27, 1101–1104.
Rempel, D.M., Krause, N., Goldberg, R., Benner, D., Hudes, M.,
Goldner, G.U., 2006. A randomised controlled trial evaluating the
effects of two workstation interventions on upper body pain and
incident musculoskeletal disorders among computer operators. Occupational and Environmental Medicine 63, 300–306.
Scheuerle, J., Guilford, A.M., Habal, M.B., 2000. Work-related cumulative trauma disorders and interpreters for the deaf. Applied Occupational and Environmental Hygiene 15, 429–434.
Schnoz, M., Laubli, T., Krueger, H., 2000. Co-activity of the trapezius
and upper arm muscles with finger tapping at different rates
and trunk postures. European Journal of Applied Physiology 83,
207–214.
Schoenmarklin, R.W., Marras, W.S., Leurgans, S.E., 1994. Industrial
wrist motions and incidence of hand/wrist cumulative trauma
disorders. Ergonomics 37, 1449–1459.
Serina, E.R., Tal, R., Rempel, D., 1999. Wrist and forearm postures and
motions during typing. Ergonomics 42, 938–951.
Silverstein, B.A., Fine, L.J., Armstrong, T.J., 1986. Hand wrist cumulative
trauma disorders in industry. British Journal of Industrial Medicine
43, 779–784.
Smith, S.M., Kress, T.A., Hart, W.M., 2000. Hand/wrist disorders among
sign language communicators. American Annals of the Deaf 145,
22–25.
Thorn, S., Forsman, M., Zhang, Q., K, T., 2002. Low-threshold motor
unit activity during a 1-h static contraction in the trapezius
muscle. International Journal of Industrial Ergonomics 30,
225–236.
Veldpaus, F.E., Woltring, H.J., Dortmans, L.J., 1988. A least-squares
algorithm for the equiform transformation from spatial marker coordinates. Journal of Biomechanics 21, 45–54.
Visser, B., de Korte, E., van der Kraan, I., Kuijer, P., 2000. The effect of
arm and wrist supports on the load of the upper extremity during
VDU work. Clinical Biomechanics (Bristol, Avon) 15 (Suppl. 1),
S34–S38.
Westgaard, R.H., de Luca, C.J., 1999. Motor unit substitution in longduration contractions of the human trapezius muscle. Journal of
Neurophysiology 82, 501–504.
Zajac, F.E., Neptune, R.R., Kautz, S.A., 2002. Biomechanics and muscle
coordination of human walking. Part I: introduction to concepts,
power transfer, dynamics and simulations. Gait Posture 16,
215–232.
Zennaro, D., Laubli, T., Krebs, D., Krueger, H., Klipstein, A., 2004.
Trapezius muscle motor unit activity in symptomatic participants
during finger tapping using properly and improperly adjusted desks.
Human Factors 46, 252–266.