This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and <2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature.
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