Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition
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 nece...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3872 |
| _version_ | 1797531440445915136 |
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| author | Guangtai Lei Shenyilang Zhang Yinfeng Fang Yuxi Wang Xuguang Zhang |
| author_facet | Guangtai Lei Shenyilang Zhang Yinfeng Fang Yuxi Wang Xuguang Zhang |
| author_sort | Guangtai Lei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:43:50Z |
| format | Article |
| id | doaj.art-47ae46fbcf604ae1a0aa98951d3f86ed |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:43:50Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-47ae46fbcf604ae1a0aa98951d3f86ed2023-11-21T22:44:22ZengMDPI AGSensors1424-82202021-06-012111387210.3390/s21113872Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture RecognitionGuangtai Lei0Shenyilang Zhang1Yinfeng Fang2Yuxi Wang3Xuguang Zhang4College of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaForce 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.https://www.mdpi.com/1424-8220/21/11/3872force myographysampling frequencychannel numbergesture recognition |
| spellingShingle | Guangtai Lei Shenyilang Zhang Yinfeng Fang Yuxi Wang Xuguang Zhang Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition Sensors force myography sampling frequency channel number gesture recognition |
| title | Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition |
| title_full | Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition |
| title_fullStr | Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition |
| title_full_unstemmed | Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition |
| title_short | Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition |
| title_sort | investigation on the sampling frequency and channel number for force myography based hand gesture recognition |
| topic | force myography sampling frequency channel number gesture recognition |
| url | https://www.mdpi.com/1424-8220/21/11/3872 |
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