Control of Power-Assist Exoskeleton Robots with Biological Signals
 K. Kiguchi, “Active Exoskeletons for
Upper-Limb Motion Assist,” Journal of
Humanoid Robotics, vol. 4, no. 3, 2007.
[ 2] K. Kiguchi, S. Kariya, K. Watanabe, K.
Izumi, T. Fukuda, “An Exoskeletal Robot
for Human Elbow Motion Support –
Sensor Fusion, Adaptation, and Control,”
IEEE Trans. on Systems, Man, and
Cybernetics, Part B, vol. 31, no. 3, pp.
[ 3] K. Kiguchi, T. Tanaka, T. Fukuda,
“Neuro-Fuzzy Control of a Robotic
Exoskeleton with EMG Signals,” IEEE
Trans. on Fuzzy Systems, vol. 12, no. 4,
[ 4] K. Kiguchi, R. Esaki, T. Fukuda,
“Development of a Wearable Exoskeleton
for Daily Forearm Motion Assist,”
Advanced Robotics, vol. 19, no. 7, pp.
[ 5] H. Okada, M. Ae, N. Fujii, Y. Morioka,
“Body Segment Inertia Properties of
Japanese Elderly,” Biomechanism 13,
pp.125-139, 1996 (in Japanese).
42 SERVO 03.2008
experiments have been carried out to
determine the relationship between the
human 5 DOF upper-limb motion
( 3 DOF shoulder joint motion, elbow
joint motion, and forearm pronation/
supination motion) and the EMG signals
of the related muscles (deltoid-anterior
part, deltoid-middle part, deltoid-posterior part, pectoralis major-c
lavicular part, pectoralis major-lateral
part, biceps-medial part, biceps-lateral
part, triceps-medial part, triceps-lateral
part, teres major, teres minor, infraspina-tus, pronator teres, flexor carpi radialis,
anconeus, and supinator). The effectiveness of the fuzzy-neuro method has
been proven in many papers -[ 4].
In one experiment, random upper-limb motion is performed by five
healthy, elderly male persons (all of
them are 65 years or older), and the
estimation error of the generated
upper-limb motion is evaluated. The
motion of each subject is measured by a
3D motion capture system (Hawk Digital
System and a high-speed camera from
FIGURE 7. Estimated joint torque
error (subject A).
NAC Image Technology, Inc.). In order to
capture the 3D motion, 17 markers are
put on each subject in the experiment as
shown in Figure 5. The motion of the
subject is monitored with eight cameras.
Each joint torque generated by
each subject during the experiment is
estimated from the motion results
calculated using human models [ 5].
The experimental results of the
torque generated by subject A are
shown in Figure 6 as an example. Figure
7 shows the error between the generated torque by subject A and the estimated torque with and without the second
EMG-based control method. It can be
seen that the amount of the torque error
becomes almost zero when the weight
matrix is adjusted by the second EMG-based control method. Similar results
were obtained with the other subjects.
These results show that the user’s
joint torque can be estimated and a
certain percentage of the estimated
torque would be assisted by the
exoskeleton robot in real time. Thus,
the power-assist is realized with the
proposed EMG-based control method.
Ideas for Future Expansion
Recent progress of robotics technology has brought several exoskeleton
robots onto the market. However, they
are still not ready for daily consumer
use. More advanced soft computing
technologies need to be developed to
make the power-assist robots more
intelligent and flexible. SV