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Our previous study has shown the potential of using a computer system to accurately decode electromyographic (EMG) signals for neural controlled artificial legs. Because of computation complexity of the training algorithm coupled with real time requirement of controlling artificial legs, traditional embedded systems generally cannot be directly applied to the system. This paper presents a new design...
This paper presents a design and partial implementation of an embedded system as a part of neural-machine interface (NMI) for neural-controlled artificial legs. We have designed a circuit consisting of 30 analog inputs for sampling signals from 16 EMG (Electromyography) electrodes, a 6 degrees of freedom (DOFs) load cell, 5 force sensitive resistors (FSR), and 3 goniometers. The amplified signals...
Applying electromyographic (EMG) signal pattern recognition to artificial leg control is challenging because leg EMGs are non-stationary. Time-frequency features are suitable for representing non-stationary signals; however, the computational complexity to extract time-frequency features is too high and current embedded systems used for artificial limb control are inadequate for real-time computing...
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