ArticleSensors (Basel, Switzerland)2021
Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Optimization of Knitted Strain Sensor Structures for a Real-Time Korean Sign Language Translation Glove System.Sensors (Basel, Switzerland) · 2025Article
- A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures.Sensors (Basel, Switzerland) · 2023Article
- Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals.Journal of neuroengineering and rehabilitation · 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
- Wearable super-resolution muscle-machine interfacing.Frontiers in neuroscience · 2022Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects' data has been created. In this paper, gesture accuracies under different sampling frequencies and channel's number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.
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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.