We tried to discriminate different forearm's motions by surface EMG signals using neural network. In order to get a higher discrimination rate, the positions of electrodes were improved. We also tried to discriminate similar motions in order to clarify the limitation of the discrimination by surface EMG signals. Two experiments were carried out. One was to discriminate five different motions: grasp, wrist flexion, wrist extension, forearm pronation, and forearm supination (Experiment 1). The other was to discriminate four similar motions which have different quantitative definitions at grasp, wrist flexion/ extension, or forearm pronation/supination (Experiment 2). Four surface electrodes were placed on the skin above the main active muscles: short radial extensor m. of wrist, supinator m., long radial extensor m. of wrist, and ulnar flexor m. of wrist, considering anatomical functions of the forearm's muscles. EMG signals were recorded during 2 sec while the subjects kept the motions. Recorded EMG signals were sampled at 200 msec intervals after full-wave rectifying and low-pass filtering. Therefore, the number of sampling data patterns of EMG signals was 10 for every motion. Three layers of neural network was used for discrimination. The number of units in the input layer is 4, and the number of units in the output layer is 5 or 4. In order to get the best discrimination rate of the motions, we changed the number of units in the hidden layer from 3 to 12. The neural network was trained by the back-propagation algorithm. In Experiment 1, the best average values of discrimination rates under three patterns of EMG signals for each subject were 96.0%, 98.0%, and 87.2% when the numbers of units in the hidden layer were 10, 11, and 3 respectively. In Experiment 2 using original EMG patterns, the best average values of discrimination rates at grasp, extension/flexion, and pronation/supination were 59.5%, 76.0%, and 25.0% respectively. By using normalized EMG patterns, these were 40.0%, 84.8%, and 55.5% respectively.
Modern manufacturing and design should satisfy not only the requirements of high cost performance but also of the user. Besides that, the social environment which surrounds manufacturing is rapidly changing depending on new technologies. To create future products with user satisfaction, the effective use of human physiological data is essential. This is where knowledge of physiological anthropology can be applied. Physiological anthropologists have been pointing out a limit to the interpretation of the physiological data based on its average value. They have begun to notice that the physiological functions of humans show various types according to the blended effect of heredity and the surroundings. Adequate consideration of physiological polymorphism is indispensable to accomplish manufacturing that is well devised for human. In this study the concept of manufacturing and design based on physiological polymorphism is expressed. The target and the methodology for new manufacturing are discussed in seven fields, that is, welfare equipment, clothes, artificial tissue, sporting gear, furniture, building materials, and human interface. Through the above discussion, a procedure to achieve manufacturing and design based on physiological polymorphism is proposed.
A simulation system that is capable of analyzing wheelchair propulsion using a human model which incorporates muscles and bones has been developed. The system calculates the driving force and muscular force for input movements applied to the wheelchair. In this study, three types of sitting arrangements were evaluated, namely a wheelchair in normal seat position which has no cushion, one in upward sitting position which has a seat cushion and one in forward sitting position which has a backrest cushion. This was done to determine the effect of varying the sitting position. The velocity and force applied to the driving wheel were measured by the wheelchair for propelling ability evaluation. The motion of the human body while propelling the wheelchair was captured using a three-dimensional measurement system and muscular forces were measured using the average rectified value of a surface electromyogram (ARV EMG). Measurements were performed on the following muscles: the clavicula and acromion of the deltoids, the pectoralis majors, the infraspinatus, the flexor and extensor of the carpi radialis, the biceps and the triceps. The results of the measurement were compared with the calculated muscular force. Only the acromion of the deltoids was active during the recovery phase, while the other muscles were mainly active during the drive phase of propulsion. In addition, the biceps were active in the early stage of drive phase, while the triceps were active in the latter stage. The switching point between these stages was brought forward when the sitting position was changed. The trend of the calculated muscular force variation corresponded with that of the ARV EMG. These results demonstrate the potential of the developed system. However, there were several discrepancies between the calculated and measured data. The flexor carpi radialis functioned as an agonist during the drive phase and the biceps and triceps, which are antagonist muscles, functioned alternately in the calculated results. In contrast, the biceps functioned as an agonist during the driving phase and functioned simultaneously with the triceps for some periods in the ARV EMG results. Solving these problems is remained for the future study.
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