Abstract
The aim of this research work was to develop an embedded electronic sensing system, portable, wireless and wearable prototype that allowed to perform bipolar surface electromyography (sEMG) detection for the Tibialis Anterior (TA) and Peroneus Longus (PL) muscles and measure the angular displacement of the ankle-foot joint for the characterization of Foot Drop (FD) pathology through the establishment of a sensor network. Two Sensor Units were developed around a CPU responsible for receiving the sensors measurements in order to assemble data packets for transmission. The bipolar sEMG detection is carried out through an analogous conditioning module. The sEMG architecture allows to obtain the raw, rectified, and the envelope of the muscular signals. The angular displacement measurement consists in an inertial measurement system. A statistical analysis to validate the precision of the measurements regard to commercial instruments, showing a MSE of \(5,27\%\) for the sEMG and a mean error \(\le \pm 1.5^{\circ }\) for angular displacement measurements. Likewise, an analysis was implemented both in the time and frequency domain for bipolar sEMG detection, to assess the energetic distribution of the TA and PL muscle contractions, showing that the spectral information in the 10–300 Hz range and PSD oscillates in the 0-7e-3 dB/Hz for a subject without FD pathology. The sensor network was implemented on the TA and PL in order to compare the transmitted and received information. The data collected and the experimental platform show the potential of the electronic prototype to measure physiological variables in real-time.
This work has been fund by Electronics Engineering Program and Faculty of Engineering of Universidad El Bosque with the research project PFI2020ES004.
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Murrugarra, C., Noriega-Alvarez, S. (2020). Sensor Network for Bipolar sEMG Detection and Angular Measurement for the Characterization of Foot Drop Pathology. In: Figueroa-García, J.C., Garay-Rairán, F.S., Hernández-Pérez, G.J., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_27
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