Hand gesture recognition using force myography of the forearm activities and optimized features

Anvaripour, Mohammad; Saif, Mehrdad

doi:10.1109/icit.2018.8352174

Cited by 25 publications

(14 citation statements)

References 18 publications

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“…Lauren develops a real-time simultaneous hand gesture recognition using intramuscular EMG [6,7] . Anvaripour investigates the forearm muscles movement data processing sensed by an array of Force Sensor Resistor [8] . Zhang uses image processing algorithms to identify multiple gestures [9] .…”

Section: Introductionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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In this paper an automated data labeling (ADL) neural network was proposed to streamline dataset collecting for real-time predicting the continuous motion of hand and wrist, these gestures are only decoded from a surface electromyography (sEMG) array of eight channels. Unlike collecting both the bio-signals and hand motion signals as samples and labels in supervised learning, this algorithm only collects the unlabeled sEMG into an unsupervised neural network, in which the hand motion labels are auto-generated. The coefficient of determination (r2) for three DOFs, i.e. wrist flex/extension, wrist pro/supination, hand open/close, was 0.86, 0.89 and 0.87 respectively. The comparison between real motion labels and auto-generated labels shows that the latter has earlier response than former. The results of Fitts' law test indicate that ADL has capability of controlling multi-DOFs simultaneously even though the training set only contains sEMG data from single DOF gesture. Moreover, no more hand motion measurement needed which greatly helps upper-limb amputee imagine the gesture of residual limb to control a dexterous prosthesis.

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Section: Introductionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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“…The value of a window-based feature depends on the selected window size; therefore, it is important to optimize the feature window for the targeted application [29]. Currently, only a limited number of publications extracted features from the FMG signals for targeted applications [14,29,45,51,58,61,62]. The optimal feature set is highly dependent on the application and it is difficult to identify a universal feature set for FMG signals.…”

Section: Fmg Processing Methodsmentioning

confidence: 99%

A Review of Force Myography Research and Development

Xiao

Menon

2019

Sensors

107

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Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.

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“…Pattern recognition technology is usually used to predict hand gestures from FMG signals. As summarized in Table 1 , RMS is one of the most commonly used FMG feature [ 11 , 13 , 16 ], and SVM [ 9 , 10 , 13 , 16 , 17 , 21 ] and LDA [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 22 ] are the most commonly used classifiers for FMG based hand gesture classification. Although different features (or systems without feature extraction) and different classifiers are applied in this field, one hardly finds which type of feature or classifier outperforms the others as seen in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

Lei

Zhang

Fang

et al. 2021

Sensors

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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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Hand gesture recognition using force myography of the forearm activities and optimized features

Cited by 25 publications

References 18 publications

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

A Review of Force Myography Research and Development

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

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