Evidence map›Paper›PMID 31652601›Full record

Trial reportSensors (Basel, Switzerland)2019

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

Daniela De Venuto, Giovanni Mezzina

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 55% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Daniela De VenutoDepartment of Electrical and Information Engineering, Politecnico di Bari, 70125 Bari, Italy. daniela.devenuto@poliba.it.ORCID 0000-0003-4563-7614
Giovanni MezzinaDepartment of Electrical and Information Engineering, Politecnico di Bari, 70125 Bari, Italy. giovanni.mezzina@poliba.it.ORCID 0000-0003-3927-8686
Polytechnic University of Bari · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

Electric ConductivityElectronicsAdultElectric Power SuppliesElectromyographyExerciseGaitHumansMuscle Fibers, SkeletalYoung AdultEMGFPGAMFCVReal-time EMG detection

Identifiers

PMID31652601
PMCPMC6832537
OpenAlexW2981754738

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.