ArticleSensors (Basel, Switzerland)2019
An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Review
- Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review.Sensors (Basel, Switzerland) · 2024Review
- A survey on the state of the art of force myography technique (FMG): analysis and assessment.Medical & biological engineering & computing · 2024Review
- A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures.Sensors (Basel, Switzerland) · 2023Article
- Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures.Sensors (Basel, Switzerland) · 2023Article
- A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.Biosensors · 2022Review
- Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2021Article
- A Review of Force Myography Research and Development.Sensors (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human-machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.
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What OpenQuestion holds
Registered trials
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.