LIFE2014 2014 年 9 月 24 日－26 日 北海道GS12-5

Estimation of individual muscle force within forearm in applying external load at each finger
キラチハッタヤコン
中島但野康博茂サラン（北大）

頓所祐貴

（北大）

（道総研）
（北大）

山田悟史

（北大）

Saran KEERATIHATTAYAKORN, Graduate School of Engineering, Hokkaido University, N13 W8, Kita-ku, Sapporo,Yuuki TONSHO, Hokkaido University, Yasuhiro NAKAJIMA, Hokkaido Research Organization,
Satoshi YAMADA, Hokkaido University, Shigeru TADANO, Hokkaido University
Abstract: The study aimed to develop a method to estimate the muscle force distribution within the forearm. Loads of 0.1
to 1.0 kgf were applied to the fingers of the right hand of four subjects. Surface electromyography (EMG) signals in theforearm were recorded during finger motions using multiple surface electrodes, which were bound around the subject’sforearm. An EMG conduction model was formulated for reverse-estimation of muscle activities based on the distribution ofsurface EMG from the skin surface. In order to estimate the muscle force from the calculated muscle activities, amathematical model based on linear regression relationship between muscle force and muscle activity was proposed. After
performing the calibration process by gripping tasks, the distribution of muscle forces within the forearm was estimated.
The muscle force tomographic images were generated for each load condition. The results show that different muscle force
distribution patterns were obtained when applied different loads.
Key Words: EMG, Finger, Tomography, Muscle force
1. Introduction
Human hand is essential for performing daily activities. The
complex movement of human hand is generated by manymuscles and tendons within the forearm. In vivo estimation ofmuscle force is important to understanding the mechanism ofmuscle force generation. The muscle activity during muscle
contraction can be detected non-invasively by surface electrode.
Electromyography (EMG) signal is well known to be related tomuscle force generation. The main problem is that surface EMG
signals in a region where large number of muscles lie close
together are superimposed. Make it difficult to observe
individual muscle activities within the forearm. To overcome
this problem, the EMG conduction model was developed toestimate muscle activity [1]. The reduction characteristics of thesurface EMG power have been studied. The power of theattenuation in relation to the distance between the surface
electrode and a source of muscle action potential were
calculated using finite element analysis with a cylindricalconduction model [2]. The position and the activity of thesource in the model have been reverse-estimated using
optimization method.
This paper proposes a method to estimate muscle force
distribution within the forearm using surface EMG. The
development of this method will be very useful in the studyingof muscular, which may potentially be used for evaluation ofneuromuscular rehabilitation and clinical application
2. Method
2.1 Experiment procedure
Four healthy subjects (age: 21.8 ± 0.8 years) participated in
the experiment. Firstly, the subjects performed gripping tasks
using a hand grip; the force was held for 5 seconds and repeatedtrice. The gripping loads were 5, 10 and 20 kg. Then, thesubjects sat on a chair with their forearm placed on a horizontal
table. A load was applied to subject’s finger by weight andpulley system (Fig.1). A load was applied for 5 seconds andrepeatedPulley
Ground electrode

Electrode plate

PCWeightFig. 1 Schematic diagram of the experimental setup. A load wasapplied to the subject’s finger using weight and pulley system.
A total of 20 electrode plates were bound around the forearm todetect the surface EMG.
Table 1 Load direction and load position at various fingers
during the experimentNo.
M1-EDP
M2-FPP
M3-FPP
M5-FPP

LoadedfingerThumb
Index
MiddleSmall

Loaddirection
Extension
FlexionFlexionFlexionLoad position
Distal phalanx
Proximal phalanx
Proximal phalanx
Proximal phalanx

repeated thrice. The weights of load were 0.1, 0.5 and 1.0 kg.
Table 1 shows types of load that applied to the subject fingers.
The surface EMG signals from the forearm were recordedwith EMG band which consist of 20 custom-built electrodeplates. The electrode plate comprised four aligned 3 mmdiameter. The inter-electrode distance of the differential
biopolar electrodes were 15 and 45 mm. All EMG signals were
sampled at 1000 Hz, filtered with third order 9 Hz Butterworthhigh pass filter then second order 570 Hz Butterworth low passfilter. The root mean square (RMS) value and mean power of
each channel were calculated from the recorded signals in 500
ms window.
2.2 EMG conduction model
The muscle region within the forearm was divided into
calculation cell. The previous study shown that the RMS valueof the muscle action potential Vi changes in proportion to theGS12-5-1

LIFE2014 2014 年 9 月 24 日－26 日 北海道

power exponent of attenuation b, exponentiated with Lij.[3]synergistic

200150202j2bLoad (N) Lij Vi  V  m    kmuscle j  L0 2(1)10050

where L0 is the unit length (1mm), mj is the strength of themuscle activation (mA dipole/mm2), and V0 is a transformation
coefficient [mV/(mA dipole/mm 2)]. This equation represents theEMG conduction model.

00Fig. 2. Relationship between external load and total muscle
activation during gripping task.

2.3 Calibration process
In order to estimate the muscle force from the calculated
muscle activity, a mathematical model based on linear
regression was proposed;

Fj    m j  Aj2000 4000 6000 8000 10000
Total muscle activation (mA)

0.1 kg

0.5 kg

1.0 kg

M1-EDP(2)Fm=13.33 N

Fm=16.26 N

Fm=23.51 N

M2-FPPMA   m j  A j

Fm =11.13 N

Fm =32.98 N

Fm =32.44 N

M3-FPP(3)

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0Muscle force (N)where Fj is muscle force of element j (N), α is muscle force
coefficient (N/mA dipole), Aj is area of element j (mm2). The
static calibration was performed by gripping tasks. The totalmuscle activation (MA) was defined as the summation of muscle
activation mj within the forearm area calculated byj

Fm =11.21 N

The change in total muscle activation between each loadcondition can be used to calculate α. Thus, for calibration
process;Fe      MA

Fm =26.73 N

Fm =45.98 N

M5-FPP(4)Fm =9.05 N

Fm =32.94 N

Fm =43.64 N

Fig. 3. Muscle force tomographic image during finger motion.
where Fe is external load (N). After calibration, muscle force ofeach element can be calculated by equation 2. The summationof muscle force within the forearm Fm was calculated byFm   F j(5)j3. Results and discussion
Figure 2 shows the relationship between external load andtotal muscle activation during the gripping task. The summationof muscle activation represents the muscle effort to resist theexternal load. A linear relation between external load and total
muscle activation was computed. The regression equation wasFe  0.035  MA  96.5

(6)

The relation had a good correlation (R2 = 0.98). Aftercalibration process, the value of α was 0.035 N/mA dipole.
Figure 3 shows muscle force distribution within the forearm
during finger motions. The pattern of muscle force distributionchanged with load condition. There was a variation ofsummation of muscle force between each load condition. Thismight due to the difference in muscle cocontraction pattern

between each load condition. During finger motion, muscle’sactions may be subdivided into agonistic, antagonistic andsynergistic activity. The high summation of force indicates that
there was high cocontraction of muscles.
In summary, this study has shown that it is possible toestimate muscle force distribution within the forearm using
surface EMG. A mathematical model to estimate muscle force
from muscle activation was developed using a linear regression
model. This method is very useful for studying muscularmechanism which potentially be used for clinical application.
4. Reference[1]Nakajima Y, Yoshinari S, Tadano S, Surface conduction
analysis of EMG signal from the forearm muscles,
Proceedings of 13th International Congress on Biological andMedical Engineering, pp 1904-1907, 2008.
[2]Nakajima Y, Yoshinari S, Tadano S, An experimental modelon the activity of forearm muscles using surface
electromyography, Journal of Biomechanical Science andEngineering 4: pp 212-220, 2009.
[3]Tadano S, Nakajima Y, EMG Conduction Model ofIndividual Muscle Activities in Forearm, Proceeding of 6th
World Congress of Biomechanics, pp 95-98, 2010.

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