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.
Highlights
People use their fingers and hand joints to perform activities of daily life (ADL), like hand gestures
Surface EMG is the most commonly used signal for measuring subcutaneous muscle activities, which has been applied in human–computer interaction
We focus on the effect of sampling frequency and the number of channels of Force myography (FMG) signal on the accuracy of gesture recognition
Summary
People use their fingers and hand joints to perform activities of daily life (ADL), like hand gestures. We focus on the effect of sampling frequency and the number of channels of FMG signal on the accuracy of gesture recognition. Carlo et al [26] collected the data from twelve non-disabled volunteers and studied the effect of signal sampling frequency on the integrity of the force morphology signal. In this experiment, the twelve volunteers were asked to repeat hand gestures as fast as possible. FMG signals for hand motion classification tend to use 16 or 32 channels It is still not clear how the channel number would influence the accuracy, which becomes another point for the current study.
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