sEMG
This data was recorded for emg based force/Torque estimation. EMG and torque signals were collected during simultaneous, isometric, but continuously varying contractions, corresponding to two wrist DoF. The experiment was carried out in two trials with a 5-min rest in between. Each trial included six combinations of tasks, separated by 2 min of rest to minimize the effect of fatigue. The performed tasks were categorized into individual and combined (simultaneous) DoF to test the ability to estimate isolated torque and torque in two simultaneous DoF.
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Electromyography is useful for those interested in the study of biological signals and their processing, identifying the characteristics of these signals is valuable for the design of prosthetic and robotic systems or in rehabilitation for patients with pathologies of the lower limb. In this case, in particular, the EMG signals of the biceps femoris generated during the gait cycle are intended to characterize the muscle activation signals during walking. For the development of this database, one hundred users were invited to participate.
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This dataset is for the performance verification of sEMG-based intention recognition algorithm with upper-limb position effect.
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Accurate proportional myo-electric control of the hand is important in replicating dexterous manipulation in robot prostheses. Many studies in this field have focused on recording discrete hand gestures, while few have focused on the proportional and multiple-DOF control of the human hand using EMG signals. To aid researchers on advanced myoelectric hand control and estimation, we present this data from our work "Extraction of nonlinear muscle synergies for proportional and simultaneous estimation of finger kinematics".
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