Literature DB >> 31652601

Field Programmable Gate Array-Embedded Platform for Dynamic Muscle Fiber Conduction Velocity Monitoring.

Daniela De Venuto1, Giovanni Mezzina2.   

Abstract

This paper proposes a novel architecture of a wearable Field Programmable Gate Array (FPGA)-based platform to dynamically monitor Muscle Fiber Conduction Velocity (MFCV). The system uses a set of wireless sensors for the detection of muscular activation: four surface electromyography electrodes (EMGs) and two footswitches. The beginning of movement (trigger) is set by sensors (footswitches) detecting the feet position. The MFCV value extraction exploits an iterative algorithm, which compares two 1-bit digitized EMG signals. The EMG electrode positioning is ensured by a dedicated procedure. The architecture is implemented on FPGA board (Altera Cyclone V), which manages an external Bluetooth module for data transmission. The time spent for data elaboration is 63.5 ms ± 0.25 ms, matching real-time requirements. The FPGA-based MFCV estimator has been validated during regular walking and in the fatigue monitoring context. Six healthy subjects contributed to experimental validation. In the gait analysis, the subjects showed MFCV evaluation of about 7.6 m/s ± 0.36 m/s, i.e., <0.1 m/s, a typical value for healthy subjects. Furthermore, in agreement with current research methods in the field, in a fatigue evaluation context, the extracted data showed an MFCV descending trend with the increment of the muscular effort time (Rested: MFCV = 8.51 m/s; Tired: 4.60 m/s).

Entities:  

Keywords:  EMG; FPGA; MFCV; Real-time EMG detection

Mesh:

Year:  2019        PMID: 31652601      PMCID: PMC6832537          DOI: 10.3390/s19204594

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  13 in total

1.  Effect of joint angle on EMG variables in leg and thigh muscles.

Authors:  D Farina; R Merletti; M Nazzaro; I Caruso
Journal:  IEEE Eng Med Biol Mag       Date:  2001 Nov-Dec

Review 2.  Methods for estimating muscle fibre conduction velocity from surface electromyographic signals.

Authors:  D Farina; R Merletti
Journal:  Med Biol Eng Comput       Date:  2004-07       Impact factor: 2.602

Review 3.  Clinical applications of high-density surface EMG: a systematic review.

Authors:  Gea Drost; Dick F Stegeman; Baziel G M van Engelen; Machiel J Zwarts
Journal:  J Electromyogr Kinesiol       Date:  2006-12       Impact factor: 2.368

4.  Non-invasive assessment of muscle fiber conduction velocity during an incremental maximal cycling test.

Authors:  Paola Sbriccoli; Massimo Sacchetti; Francesco Felici; Leonardo Gizzi; Mauro Lenti; Alessandro Scotto; Giuseppe De Vito
Journal:  J Electromyogr Kinesiol       Date:  2009-04-23       Impact factor: 2.368

5.  Differences in myoelectric manifestations of fatigue in sprinters and long distance runners.

Authors:  A Rainoldi; M Gazzoni; G Melchiorri
Journal:  Physiol Meas       Date:  2008-02-22       Impact factor: 2.833

6.  Unchanged muscle fiber conduction velocity relates to mild acidosis during exhaustive bicycling.

Authors:  J P J Schmitz; J P van Dijk; P A J Hilbers; K Nicolay; J A L Jeneson; D F Stegeman
Journal:  Eur J Appl Physiol       Date:  2011-08-23       Impact factor: 3.078

7.  Analogue and digital instruments for non-invasive estimation of muscle fibre conduction velocity.

Authors:  A Fiorito; S Rao; R Merletti
Journal:  Med Biol Eng Comput       Date:  1994-09       Impact factor: 2.602

8.  Motor unit loss and weakness in association with diabetic neuropathy in humans.

Authors:  Matti D Allen; In Ho Choi; Kurt Kimpinski; Timothy J Doherty; Charles L Rice
Journal:  Muscle Nerve       Date:  2013-04-30       Impact factor: 3.217

9.  A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring.

Authors:  Ermis Koutsos; Vlad Cretu; Pantelis Georgiou
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2016-05-13       Impact factor: 3.833

10.  A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network.

Authors:  Juri Taborri; Stefano Rossi; Eduardo Palermo; Fabrizio Patanè; Paolo Cappa
Journal:  Sensors (Basel)       Date:  2014-09-02       Impact factor: 3.576

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