It may be difficult task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life. Therefore, many assistive devices have been developed in order to improve the quality of life of those people. Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are vital for the daily activities. Electromyography (EMG) signals of the upper limb muscles have sometimes been used as a primary signal to control the power assist exoskeletons since the EMG signals directly reflect the motion intention of the user. But one of the main obstacles for EMG based controller is the muscle fatigue, because the muscle fatigue might change the EMG patterns. It is important for power-assist exoskeleton to correctly assist the user for longer period of time. But it has high probability of user muscles been fatigued because users getting more and more exhausted at the end of the day. Therefore it is necessary to consider the variations of EMG signals due to the effect of muscle fatigue. In this paper it demonstrates the study which was conducted to find out the effects of muscle fatigue on the three EMG features derived from the raw EMG signals of the Bicep brachii, Deltoid-posterior, Deltoid-anterior and Supinator muscles of the upper limb. Shoulder vertical flexion/extension, shoulder abduction/adduction, elbow flexion/extension and forearm pronation/supination motions were carried out before and after a set of muscle fatiguing exercises. The three features computed in this experiment were RMS (Root Mean Square), MPF (Mean Power Frequency) and a spectral feature (FInsm5) which was proposed by Dimitrov. Comparison results of these three features of all muscles before and after the fatiguing exercises showed an percentage increase of the RMS and FInsm5 features whereas MPF showed a percentage decrease with respect to the before fatiguing conditions. The result showed that the EMG RMS may not a reliable feature to use as the only input signal in EMG based control for human upper-limb power assist in the muscle fatiguing conditions. Therefore, it is suggested that a modification method for compensating the effect of muscle fatigue is required on the EMG based control in order to have a long and reliable use of the human upper-limb power assist exoskeletons.
[1]
Maury A. Nussbaum,et al.
Static and dynamic myoelectric measures of shoulder muscle fatigue during intermittent dynamic exertions of low to moderate intensity
,
2001,
European Journal of Applied Physiology.
[2]
Yoshiyuki Sankai,et al.
Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint
,
2002,
IEEE/RSJ International Conference on Intelligent Robots and Systems.
[3]
J. Potvin.
Effects of muscle kinematics on surface EMG amplitude and frequency during fatiguing dynamic contractions.
,
1997,
Journal of applied physiology.
[4]
R. A. R. C. Gopuraa,et al.
A Study on Human Upper-Limb Muscles Activities during Daily Upper-Limb Motions
,
2009
.
[5]
J.C. Perry,et al.
Upper-Limb Powered Exoskeleton Design
,
2007,
IEEE/ASME Transactions on Mechatronics.
[6]
Y Narayan,et al.
An electromyographic study of upper limb adduction force with varying shoulder and elbow postures.
,
1998,
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[7]
Kazuo Kiguchi,et al.
3D Perception-Assist for Upper-Limb Power-Assist Exoskeletons
,
2008
.
[8]
N. Dimitrova,et al.
Muscle fatigue during dynamic contractions assessed by new spectral indices.
,
2006,
Medicine and science in sports and exercise.
[9]
Eduardo Rocon,et al.
Upper-Limb Robotic Rehabilitation Exoskeleton: Tremor Suppression
,
2007
.
[10]
G Németh,et al.
Muscle activity and coordination in the normal shoulder. An electromyographic study.
,
1990,
Clinical orthopaedics and related research.
[11]
Kazuo Kiguchi,et al.
SUEFUL-7: A 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control
,
2009,
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.