AI Article Synopsis
- Force myography (FMG) is a technique that uses pressure sensors to indirectly measure muscle contractions, offering a cost-effective alternative to traditional myoelectric signal methods for hand gesture recognition.
- A custom-designed hardware system with 16 channels and a maximum sampling frequency of 1 kHz was used to create a FMG database from 10 subjects, allowing researchers to investigate the impact of sampling frequency and the number of channels on gesture recognition accuracy.
- Results show that while a sampling frequency as low as 5 Hz can work for static movements, accuracy is highly influenced by the number of channels (with eight being optimal) and sensor placement on the forearm.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200028 | PMC |
| http://dx.doi.org/10.3390/s21113872 | DOI Listing |
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