Experiment performances in
Recently, dance movement has been frequently studied using motion capture, but some movements are unable to be analysed by motion data alone. Systematic research of dance movements using several kinds of data captured by simultaneous measurement of body motion and biophysical information are rarely carried out.
In the research literature there are several studies using the analyses of movement through simultaneous measurement of body motion and biophysical information, for instance, the learning environment for sport-form training (Urawaki, 2005), biomechanical analysis of ballet dancers (Humm et al, 1994), and behaviour capture systems (Kurihara et al, 2002), etc. Although there is one study that extracts a target motion from motion captured dance data (Yoshimura et al, 2001), and another where skillfulness of a dancer is investigated by calculating a typical style of the dancing called
We paid attention to leg movements of the lower half of the body. Leg movements of a dancer generate a path of motion, a tempo, and a dance rhythm. In particular, leg movements in Japanese traditional dance allow dancers to express various performances, shift performances, and transfer and retain body weight (Kunieda, 2003).
In the following research, we aim to quantitatively analyse characteristics of leg movement patterns of an expert traditional dancer using simultaneous measurement of body motion and biophysical information (EMG: ElectroMyoGram).
2. Method of experiment
We carried out experiments on the leg movements of expert Japanese traditional dancers with simultaneous measurement of body motion and EMG (Choi, 2007).
The subjects who participated in this experiment are two
|Performance||Role of subject|
|Performance 2||Woman expert entertainer|
|Performance 3||Man entertainer|
|Performance 7||Novice entertainer|
We measured the traditional Japanese dance named
2.3. Simultaneous measurement of body motion and EMG
In this research, 32 markers were attached on the body of a subject in order to capture motion data, and 12 EMG electrodes on the front and back of both legs.
Recording EMG signals needs electrodes, an amplifier and a data recording device. Each EMG signal is obtained by A/D converting data amplified by the amplifier. In this research, we used the SYNA ACT MT11 system (NEC Corp.). The amplitude of an EMG signal is almost proportional to the scale of muscle force. This relationship between EMG signal and muscle force can therefore be used to analyse various human body movements. Because the raw EMG signal obtained by the equipment is corrupted by high frequency noise, we have to employ some noise reduction techniques like low pass filtering. Also, we have to convert the raw signal into a signal that is proportional to the activities of the muscles. Rectification of the signal, or the RMS (Root Mean Square) of the signal is usually used for the analysis.
As per the literature on EMG (Choi, (2007)), the attaching place of EMG electrodes is fixed on the following six muscles (see Figure 1): Rectus Femoris (RF), Vastus medialis (VM), Tibialis Anterior (TA), Hamstrings (HA), Gastrocnemius (GAS) and Soleus (SOL). As shown in Table 2, these muscles have functions associated with leg movement. The SOL, VM, and TA muscles are mono-articular muscles. HA, RF, and GAS muscles are bi-articular muscles.
|Rectus Femoris (RF)||Extension of knee and flexion of hip|
|Vastus medialis (VM)||Extension of knee|
|Tibialis Anterior (TA)||Dorsal flexion of ankle|
|Hamstrings (HA)||Flexion of knee and extension of hip|
|Gastrocnemius (GAS)||Plantar flexion of ankle and flexion of knee|
|Soleus (SOL)||Plantar flexion of ankle|
To obtain 3D motion data, the Eagle-Hawk system (Motion Analysis Corp.) at Ritsumeikan University was used. This system incorporates 12 infrared cameras detecting small markers attached to a subject who moves in a 4m × 4m area.
We captured data by adjusting the sampling rate of motion capture to 60Hz, and EMG measurement to 1200Hz, and recorded eight performances a total of three times using the simultaneous measurement system.
3. Result and discussion of experiment
In this research, we compared the leg movements of an Expert
3.1. Center of gravity
Firstly, we compared the center of gravity of the two subjects under the condition of a single support phase of both legs in Performance 1.
3.1.1. Computation of center of gravity
|Segment||Segment weight/ Total body weight||Center of gravity/ Segment length|
shows the result of our computation for the location of center of gravity in each body segment for subject. The
3.1.2. Center of gravity on Performance 1
Figure 3 shows the center of gravity data obtained during Performance 1 of Expert
Figure 3 (a) and (c) show leg movement under a condition of a single support phase of the right leg during Performance 1. Subjects maintain their body weight with the right leg, while the left leg is swinging. Figure 3 (b) and (d) show leg movement under a condition of a single support phase of the left leg. Subjects retain their body weight with the left leg, while
the right leg is swinging. The two subjects have no significant difference in leg movement during the single support phases.
Figure 3 (e) and (f) show the transfer of the center of gravity of Expert
Figure 3 (g) and (h) show the velocity of center of gravity of the two subjects under the single support phase of both legs in Performance 1. In (g), Skilled
Figure 3 (i) and (j) show the average velocity of center of gravity under the single support phase of both legs. In (i), Skilled
Based on the above data, we found that Skilled
3.2. Movement of knee and ankle
Secondly, we analysed the characteristics of leg movement of the subjects Expert
3.2.1. Knee movement
Figure 4 shows the angle of the knees and the RMS of the EMG during Performance 1. Figure 4 (a) and (c) show movements of the right knee of the two subjects under the single support phase of the right leg. Figure 4 (b) and (d) show movements of the left knee during single support phase of the left leg. There is no significant difference in movement of the knees of two subjects during the single support phases.
Figure 4 (e) and (f) show the angle of the knees of both legs of the two subjects during the single support phase. The angle variation of the knee in (e) indicates that the subjects use knee flexion to lower the leg. The difference of angle variation of the knee between the two subjects was approximately 10-20 . This is not a significant difference. Angle variation of the knees in (f) indicates that the subjects use knee extension to raise the leg.
Figure 4 (g) and (h) show the RMS values of the RF muscle for Expert
Figure 4 (i) and (j) show the RMS values of the HA muscles of Expert
The X and Y axes of Figure 4 (k) and (l) show the RMS values of EMG signal from the RF and HA muscles. RF and HA muscles are antagonistic muscle pairs of the knee. Expert
3.2.2. Ankle movement
Next, Figure 5 shows the angle of the ankle and the RMS of the EMG signal during Performance 1. Figure 5 (a) and (c) show the movement of the ankle of Expert
Figure 5 (e) and (f) show ankle angle of the two subjects during the single support phase of both legs. Ankle angle variation in (e) indicates that the subject used the ankle dorsal to lower the leg. The difference between ankel angle variation between the two subjects was approximately 10 . The angle variation of the knee in (f) indicates that the subjects used ankle plantar flexion to raise the leg.
Figure 5 (g) and (h) show the EMG RMS value of the TA muscle of Expert
Figure 5(i) and (j) show the RMS value of the SOL muscles of Expert
The X and Y axies of Figure 5 (k) and (l) show the EMG RMS values of the TA and SOL muscles. TA and SOL muscles are antagonistic muscles of the ankle. Expert
3.3. Efficiency of co-contraction of the knee and ankle
Thirdly, we compared the efficiency of leg movement of the two subjects during the single support phase in Performance 1. The efficiency of leg movement is calculated by observing co-contraction of the two antagonistic muscles of the knee and ankle. The efficiency of co-contraction of antagonistic muscles can be determined by the following equation (Winter, 1990) (see Figure 6).
We compute the efficiency of leg movement via Eq. (2). Table 4 shows the co-contraction of the knee and ankle of two subjects during Performance 1 of
|Single support phase (right)||Single support phase (left)|
3.4. Visualization of the single support phase of Performance 1
Finally, we visualize the leg movement of the two subjects during Performance 1 using CG character animation.
Figure 7 (a) and (b) show the quantized RMS values of the EMG signal for the RF and TA muscles of both legs during single support phase. We used the RMS data of the RF muscle in (g) and (h) of Figure 4, and the RMS data of the TA muscle in (g) and (h) of Figure 5. The RMS data is quantized to 5 levels. We then made a CG character animation using an OpenGL program, colouring the character's legs in accordance with the quantized RMS data. At the same time, we show the leg movement of the single swing phase versus the single support phase of both legs in Figure 7 (a) and (b).
Figure 8 (a) and (b) show snapshots of the CG character animation with generated colours based on the single support phase of both legs. During high EMG activity, the colour becomes deeper than during low EMG activity, in proportion to the EMG signal level as shown in Figure 8. We found that the differences in leg movement between the Expert
As shown in Figure 8 (a) and (b), we notice that the RF and TA muscles of both legs of Skilled
4. Conclusion and future work
In this research, we performed quantitative analysis of leg movement patterns of an expert traditional dancer using simultaneous measurement of body motion and leg muscle EMG.
As a result, we verified that Expert
In the future, we will measure the leg movement control of veteran dancers, especially for quantitatively comparing leg movement skills, by recording the leg movements of masters and beginners. Furthermore, we will investigate leg movement skill by simultaneously using EMG equipment and a force plate.
We would like to thank Ms. Daizo Hanayagi and Ms. Souko Hanayagi for co-operating with us in the motion capturing experiment with EMG measurement. We also thank Prof. Yuka Marumo and Prof. Mamiko Sakata for providing us valuable advice about traditional Japanese dance.This research has been conducted partly by the support of the Global COE Program, the Open Research Center Program, and the Grant-in-Aid for Scientific Research No. (B)16300035, and No. (C)20500105 all from the Ministry of Education, Science, Sports and Culture.