Design, implementation, and estimation of MFCV for 4-different position of human body using FPGA. (November 2020) - Sudharsan, R. Raja; Deny, J.; Muthukumaran, E.; Selvi, S. Chitra
Abstract: The motivation behind this article is to demonstrate the precision and dependability of a Field Programmable Gate Array-based implementation of muscle fibre conduction velocity (MFCV) in customary unique constrictions. The framework, leveraging the DSP unit, acquires the bio signals from four different positions of the human body, including Radial Nerve, Ulnar nerve, Auxiliary Nerve, Median nerve. The information derived through Surface Electromyogram (EMG) Electrodes is depicted as a dynamic bitstreams of the signal. The latest bitstreams are subjected to an algorithm. The subjection corresponds to the correlation of the signals from similar muscle nerves, obtained through the EMG electrodes. The whole calculation framework completely works progressively on the Altera Cyclone-V FPGA. This method is better than finding the signal dependence using a silicon resonator due to its cost and tedious design of the sensors. The myoelectric (Electromyographic) signal is recorded by the electrodes set on the four different locations associated with 1550 unique contractions. The in-vivo estimations illustrate that below the equivalent trial states, in 10 test days, the framework uncovers an MFCV mean estimation of 6.60 (±) 0.42 m/s for Median nerve Position, 6.43 (±) 0.33 m/s for Ulnar Nerve Position, 6.54 (±) 0.41 m/s for Radial nerve Position and 7.21 (±) 0.49 m/s for Auxiliary Nerve Position. The values evidence decent dependability of the estimations in an eternal application. The core part of the device being MFCV estimation, upon empirical evaluations with clinical values, shows an accuracy of 97%. Highlights: Design of Electromyography Device for the detection of Nerve Disorders. Implementation and Estimation of Muscle Fibre Conduction Velocity (MFCV). Obtaining the Accuracy of 97% for this experimentation.