日本佐贺大学E-EMG基于控制人体上肢动力辅助的肌肉疲劳的影响研究

Abstract—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. Itis 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 necessaryto 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) anda 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 increaseof the RMS and F Insm5 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.I.I NTRODUCTIONHE upper-limb motions are important for the human daily activities. However, it is sometimes difficult for physically weak elderly, disabled, and injured individuals to perform daily upper-limb activities. Due to the modern science and advancement of the technology, many developed countries have to face the problem of aging societies. Therefore in such societies it is important to improve the quality of living of such persons to care of themselves in the present society. Recently, some assistive devices such as power assists robots have been developed to assist the daily life motions as one of the solutions to these problems [1]-[5].These power-assist robots/exoskeletons are required to generate the movements according to the user’s motion intention. Some of these power-assist robotic systems use a biological signal such as EMG signals of muscles of the users which have been identified as an input signals to estimate the user’s motion intention in real time. Hence, it is important to analyze the relationships between the upper-limb motions and related muscle activities to perform the power-assist of the upper-limb motion. In [6], an electromyography study has been carried out for upper-limb adduction force with varying shoulder and elbow postures. In [7], a study has been conducted to research about the muscle activity and coordination in the normal shoulder. In the recent study [8], the results of human upper–limb muscle activities during daily upper limb motions were elaborated. However, it is important to consider about the time varying characteristics of the EMG signal which is known to be a non-linear signal as well as non stationary signal with respect to time. One ofA Study on Effects of Muscle Fatigue onEMG-Based Control for Human Upper-LimbPower-AssistThilina Dulantha Lalitharatne1, Yoshikai Hayashi2, Kenbu Teramoto3, Kazuo Kiguchi41,2,3 Department of Advanced Technology Fusion,Saga University, Saga, JapanEmail: 1thilina@mech.mrt.ac.lk,2hayashi@me.saga-u.ac.jp,3tera@me.saga-u.ac.jp4 Department of Mechanical Engineering,Kyushu University, Fukuoka, JapanEmail: 4kiguchi@mech.kyushu-u.ac.jpT978-1-4673-1975-1/12/$31.00 ©2012 IEEE ICIAfS’12the main reasons behind the EMG non-stationary is themuscle fatigue. It has a high probability of getting muscles fatigued of the physically weak elderly people or disable individuals as they are more and more exhausted during the day to day life activities throughout the day. However, it is important for power-assist exoskeleton to behave in a reliable and accurate manner as long as it is used. But the alterations to EMG signals due to muscle fatiguing conditions may cause controller of such power-assist exoskeleton to generate an incorrect output. In [9], myoelectric measurements have been carried out for shoulder muscle fatigue during the intermittent dynamic exertions. In the study [10], the results have shown the variation of EMG signals of bicep brachii muscle during the fatiguing dynamic contractions. In this paper, it demonstrates the study on effect of muscle fatigue on EMG based control in human upper-limb power assist. Three EMG features are calculated from the raw EMG signals of four muscles of human upper-limb namely bicep brachii, Deltoid-anterior, Deltoid-posterior and Supinator which are often use in EMG based control of human upper-limb power assist exoskeletons. Three features interested in this study are EMG RMS (Root Mean Square), MPF (Mean Power Frequency) and a spectral feature (FInsm5) explained in [11]. Four basic motions of upper-limb namely Shoulder abduction/adduction, Shoulder vertical flexion/extension, Elbow flexion/extension and Forearm pronation/supination are carried in the beginning of the experiments. In order to get the muscle fatigue conditions a set of muscle fatiguing exercises are conducted. Immediately after the fatiguing exercises, the same motions which conducted before the fatiguing exercises are carried out to measure the effect of muscle fatigue on each of the EMG features in each different motions of the upper-limb.II.M ETHODA.Data AcquisitionIn order to measure the EMG changes due to fatigue, EMG signals of muscles of the upper limb were measured with EMG electrodes with bipolar montage [NE-101A, Nihon Koden Co.] through an amplifier [MEG-6108, Nihon Koden Co.]. The EMG signals of the muscles bicep brachii, deltoid-anterior, deltoid-posterior and supinator of the right upper-limb were measured. The locations of the electrodes can be found in [8].Two 3-axis accelerometers attached on the shoulder and the forearm using adhesive tapes were used to measure the shoulder angles and forearm angle respectively. A rotary encoder attached to elbow using two links was used to measure the elbow angle (Fig. 1).B.Experimental ProcedureThree healthy men (age: 24-27), who were not suffering from any previous muscle disorders of upper limb participated in this study. Each subject was given the full instructions about the experimental procedure before the experiments. The subjects were asked to follow four different upper limb motions (shoulder abduction/adduction, shoulder vertical flexion/extension, elbow flexion/extension and forearm pronation/supination). The initial positions and the motion ranges for shoulder vertical movement and shoulder abduction/adduction movement are shown in Fig. 2(a) and Fig. 2(b) respectively. The elbow flexion/extension and the forearm supination/pronation movements are shown in the Fig. 3(a) and Fig. 3(b) respectively. The experimental procedure of the elbow flexion/extension movement is described here, and other experiments were carried out in the same routine. A t the first phase of the elbow motion experiment, subjects were asked to do three repetitions of(a) (b)Fig. 2 Motion ranges of shoulder movements(a) (b)Fig. 3 Motion ranges of elbow and forearm movementsFig. 1 Placement of Sensorscondition. FInsm5 feature showed a significant increase in bicep brachii muscle of subject B. Fig. 7(e) and 7(f) show the variations of FInsm5 features in before and after fatiguing exercise respectively. Similar kinds of changes were observed for other muscles in each subject. In order to compare the percentage variation of EMG features of bicep brachii muscle followed by the muscles fatiguing exercises to initial EMG features, averages across the each contraction cycle were calculated. Since, there were set of three contraction cycles carried out before and after muscle fatiguing exercise, two grand averages were calculated respectively for before and after conditions. Finally ratio between differences of grand averages to initial average was calculated as the percentage variation for each feature in each muscle. Similar procedure was implemented for data of every subject. The final results are shown in Table I, Table II and Table III for subject A, subject B and subject C respectively. It can be considered as these changes on each EMG features due to muscles fatiguing conditions, since all other conditions prior and posterior the fatiguing experiment were approximately maintained at the same levels. Results show that EMG RMS feature is affected by the muscle fatigue. Therefore these results emphasize the need of attention to be focus on muscle fatigue conditions whenever using EMG RMS signals in controlling exoskeletons. For an example, in Fig. 6(a) shows RMS feature changes with respect to elbow angle before fatiguing exercise conducted. A System which has trained for calculating the elbow torque using RMS changes with the elbow angle for before fatiguing exercise will not give accurate results due to different pattern between EMG RMS and the elbow angle after fatiguing exercise. Therefore, it may not accurate to use only the EMG RMS of muscles as the features to calculate the torques, since the subjects who are using the exoskeletons in day to day life can be suffered by muscle fatigue due to long term use and exhaustion. It is necessary to modify the torque calculation for each joint according to the muscle fatigue condition in order to achieve a long term use of the exoskeleton system. On the other hand, MPF and FInsm5 can be used as measures of the muscle fatigue conditions. These two features combine with EMG RMS can be used to accurately calculate the joint torque for long use of the exoskeleton. Since the variation of each EMG features in this study are shown to be non linear signals, a fuzzy reasoning method can be applied as a non linear algorithm to calculate the joint torque. Then a method such as artificial neural network can be used for teaching the system to work in the muscle fatigued conditions and adaption for each subject to use in practical systems.IV.C ONCLUSIONIn this paper, the objective was to study about the effects of muscle fatigue on EMG based control for human upper-limb power assist. The effect of muscle fatigue on the three EMG features namely RMS, MPF and FInsm5 were analyzed in this study. EMG RMS and FInsm5 features were increased with the fatiguing conditions whereas MPF showed a decreasing pattern. It is suggested that only use of EMG RMS as input signal to measure the motion intention in EMG based controller in exoskeleton may not accurate for long-term use, because of the muscle fatiguing conditions. Therefore, a combination of the other two features with EMG RMS can be used as inputs to EMG-based power-assist exoskeleton controllers to compensate the effect of muscle fatiguing condition. Implementation of a fuzzy-neuro modifier which uses the above mentioned EMG features as the inputs is currently underway to compensate the effect of muscle fatigue and ensure the long–term use of the 7-DOF upper limb power assist exoskeleton [3].R EFERENCES[1]S.Lee and Y.Sankai, “ Power A ssist Control for Walking A id withHA L-3 Based on EMG and Impedance A djustment around Knee Joint,” Proc. of IEEE/RSJ International Conf. on Intelligent Robots and Systems, 2002, pp.1499-1504.[2]J.L. Pons, et el., “Upper-Limb Robotic Rehabilitation Exoskeleton:Tremor Suppression,” Rehabilitation Robotics, Book edited by S.S Kommu, pp.453-470, 2007.[3]R.A.R.C. 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