In this study, we examined the effects of static and dynamic stretching on range of motion (ROM), passive torque (PT) at pain onset, passive stiffness, and isometric muscle force. We conducted a randomized crossover trial in which 16 healthy young men performed a total of 300 s of active static or dynamic stretching of the right knee flexors on two separate days in random order. To assess the effects of stretching, we measured the ROM, PT at pain onset, passive stiffness during passive knee extension, and maximum voluntary isometric knee flexion force using an isokinetic dynamometer immediately before and after stretching. Both static and dynamic stretching significantly increased the ROM and PT at pain onset (p<0.01) and significantly decreased the passive stiffness and isometric knee flexion force immediately after stretching (p<0.01). However, the magnitude of change did not differ between the two stretching methods for any measurements. Our results suggest that 300 s of either static or dynamic stretching can increase flexibility and decrease isometric muscle force; however, the effects of stretching do not appear to differ between the two stretching methods.

Conventional research on motion recognition using surface electromyogram (SEMG) is mainly focused on how to process with the signals for pattern recognition. However, it is of much consequence to the motion recognition that measurement channels position including useful information about SEMG pattern recognition is selected. In this paper, we present two topics for the hand motion recognition system based on SEMG. First described is the method to select the suitable measurement channels position of multichannel SEMG for the recognition of hand motion, and the second described is an applied systems based on our proposed method. About channel selection, we use a multichannel matrix-type surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those electrodes, system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. The recognition experiments of 18 hand motions show that the average rate was measured to be greater than 96%. And the number of selected channels ranged from 4 to 7. About applied systems, our developed system works as an input interface for the computer (keyboard and pointing device) and a robot hand.

In this study, we describe the classification method of hand movements using 96 channels matrix-type(16times6) of multi channel surface electrode. Today, there are many systems that use the EMG as a control signal. As for those ordinary systems, it has some problem like most of them require the definition of measuring position. We design the new system with multi channel electrode to solve some of those conventional problems. Our system that has 96 channels electrode does not need to select a particular electrode position. Only attaching this electrode, we can obtain correct EMG and this way means providing with a simple and easy way. The purpose of this study is development of the EMG pattern recognition method using multi channel electrode. From measured 96 channels EMG data, we chose one line (16channels) of this electrode with the smallest noise. The EMG signal is recognized by canonical discriminant analysis. In order to recognize the EMG signal, the first three eigenvectors are chosen to form a discriminant space. And Euclidean distance is applied to classify the EMG. From the experiment in this method, we can discriminate 12 movements of the hand including four finger movements. And the recognition rate that can be done in real-time was measured at 80 percent on the average.

In this paper, we describe the human-interface equipment using surface electromyogram (SEMG) based on optimal measurement channels for each subject. In case the SEMG is used as a control signal, individual differences of SEMG are important issue to obtain high accuracy recognition of motions. To solve this problem, we propose a channel selection method of the suitable measurement channels for the recognition of motions. We use a 96-channel matrix-type (6 x 16) surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those 96 electrodes, our system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. Our system generates 10,000 sets of randomly selected channels, and these sets are evaluated by the recognition rate of hand motions. One set that records a highest recognition rate is selected from 10,000 sets for an optimal set of measurement channels. And the one set with the smallest number of measurement channels which fulfil the recognition rate above 90% or the maximum recognition rate above 95% is used for real-time recognition. Six normal subjects were experimentally tested using our system. The recognition rates of 18 hand motions, including 10 finger movements, were assessed for every subject. We were able to distinguish all the motions, and the average recognition rate in the real-time experiment was measured to be greater than 95%. And the number of selected channels ranged from 4 to 7.

To improve degree of freedom (DOF) of system control using surface electromyogram (SEMG), we made a basic study of the estimation of user's intended motion including combined motion which is performed by more than one basic motion simultaneously. Our developed system requested to obtain three SEMG characteristics of basic motion and one SEMG characteristics of rest state. This study defines the motion of grasp, supination and pronation as basic motion, and two combined motion which is "grasp +supination" and "grasp + pronation" are set. Our system investigates the possibility of combined motion estimation based on SEMG characteristics of basic motions. Estimation method which is utilizing optimal SEMG that are derived from multichannel SEMG signals is performed by canonical discriminant space and tendency of degree of similarity between combined motion and basic motion. In experimental results, we succeeded in estimation of combined motion although it was included an estimation of basic motions which were constructed elements of combined motion.