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This paper presents the design and implementation of a cyber physical system (CPS) for neural-machine interface (NMI) that continuously senses signals from a human neuromuscular control system and recognizes the user's intended locomotion modes in real-time. The CPS contains two major parts: a microcontroller unit (MCU) for sensing and buffering input signals and an FPGA device as the computing engine...
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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