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https://www.selleckchem.com/ALK.html Recognizing human intentions from the human counterpart is very important in human-robot interaction applications. Surface electromyography(sEMG) has been considered as a potential source for motion intention because the signal represents the on-set timing and amplitude of muscle activation. It is also reported that sEMG has the advantage of knowing body movements ahead of actual movement. However, sEMG based applications suffer from electrode location variation because sEMG shows different characteristics whenever the skin condition is different. They need to recreate the estimation model if electrodes are attached to different locations or conditions. In this paper, we developed a sEMG torque estimation model for electrode location variation. A decomposition model of sEMG signals was developed to discriminate the muscle source signals for electrode location variation, and we verified this model without making a new torque estimation model. Torque estimation accuracy using the proposed method was increased by 24.8% and torque prediction accuracy was increased by 47.7% for the electrode location variation in comparison with the method without decomposition. Therefore, the proposed sEMG decomposition method showed an enhancement in torque estimation for electrode location variation.Bio-impedance analysis provides non-invasive estimation of body composition. Recently, applications based on bio-impedance measurement in skin tissue such as skin cancer diagnosis and skin composition monitoring have been studied. For scanning the electrical properties along the skin depth, the relationship between the electrode topologies and the depth sensitivity should be clarified. This work reports a systematic analysis on designing line electrode topologies to measure the bio-impedance of the skin layer at specific depth using a finite element method (FEM). Four electrodes consisting of two outer current electrodes and two inner voltage electrodes

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