PMID- 31652601
OWN - NLM
STAT- MEDLINE
DCOM- 20200306
LR  - 20200306
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 19
IP  - 20
DP  - 2019 Oct 22
TI  - Field Programmable Gate Array-Embedded Platform for Dynamic Muscle Fiber 
      Conduction Velocity Monitoring.
LID - 10.3390/s19204594 [doi]
LID - 4594
AB  - 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).
FAU - De Venuto, Daniela
AU  - De Venuto D
AUID- ORCID: 0000-0003-4563-7614
AD  - Department of Electrical and Information Engineering, Politecnico di Bari, 70125 
      Bari, Italy. daniela.devenuto@poliba.it.
FAU - Mezzina, Giovanni
AU  - Mezzina G
AUID- ORCID: 0000-0003-3927-8686
AD  - Department of Electrical and Information Engineering, Politecnico di Bari, 70125 
      Bari, Italy. giovanni.mezzina@poliba.it.
LA  - eng
PT  - Clinical Trial
PT  - Journal Article
DEP - 20191022
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Adult
MH  - *Electric Conductivity
MH  - Electric Power Supplies
MH  - Electromyography
MH  - *Electronics
MH  - Exercise/physiology
MH  - Gait/physiology
MH  - Humans
MH  - Muscle Fibers, Skeletal/*physiology
MH  - Young Adult
PMC - PMC6832537
OTO - NOTNLM
OT  - EMG
OT  - FPGA
OT  - MFCV
OT  - Real-time EMG detection
COIS- The authors declare no conflict of interest.
EDAT- 2019/10/28 06:00
MHDA- 2020/03/07 06:00
PMCR- 2019/10/01
CRDT- 2019/10/27 06:00
PHST- 2019/09/13 00:00 [received]
PHST- 2019/10/18 00:00 [revised]
PHST- 2019/10/20 00:00 [accepted]
PHST- 2019/10/27 06:00 [entrez]
PHST- 2019/10/28 06:00 [pubmed]
PHST- 2020/03/07 06:00 [medline]
PHST- 2019/10/01 00:00 [pmc-release]
AID - s19204594 [pii]
AID - sensors-19-04594 [pii]
AID - 10.3390/s19204594 [doi]
PST - epublish
SO  - Sensors (Basel). 2019 Oct 22;19(20):4594. doi: 10.3390/s19204594.