Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing

Shull, Peter B.; Jiang, Shuo; Zhu, Yuhui; Zhu, Xiangyang

doi:10.1109/tnsre.2019.2905658

Cited by 76 publications

(42 citation statements)

References 46 publications

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“…The accuracy of this model was 93.7%, and the R 2 was 0.927. Previous research [27] used the SVM, LDA, and KNN models for the recognition of three sets of static hand gesture, and an RF model for dynamic single finger angle estimation. Thus, the classification and regression attributes of gesture data were not considered jointly.…”

Section: Comparison With Previous Resultsmentioning

confidence: 99%

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Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Gao

Jiang

Shull

2020

Sensors

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Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.

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Section: Comparison With Previous Resultsmentioning

confidence: 99%

“…Each finger was flexed/extended for 25 s, there were 5 fingers per trial ( Figure 2), and the sampling frequency was 40 Hz, so 25 × 5 × 40 = 1000 samples were collected per trial. Each subject performed 10 trials [27], so 10 × 10 = 100 trials were performed. Thus, there was a total of 1000 × 100 = 100, 000 samples.…”

Section: Experiments Protocolmentioning

confidence: 99%

Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Gao

Jiang

Shull

2020

Sensors

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“…Usually, an array of force-sensing resistors is used to achieve better accuracy. However, some closely related implementations use a wide range of special deformation and stretch [ 95 ] sensors, such as optical fibre-based sensors [ 96 ], capacitance-based deformation sensors [ 97 ], Hall-effect based deformation sensors [ 98 ], and barometric sensors [ 99 ]. Another interesting variation of the approach was introduced by Jiang et al [ 100 ], where the sensing system utilized a thin array of adhesive stretchable deformation sensors (e-skin) on the dorsal side of the hand to recognize a set of 10 gestures with 94% accuracy.…”

Section: Approaches Proven For Prosthetic Controlmentioning

confidence: 99%

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

Grushko

Spurný

Černý

2020

Sensors

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The loss of a hand can significantly affect one’s work and social life. For many patients, an artificial limb can improve their mobility and ability to manage everyday activities, as well as provide the means to remain independent. This paper provides an extensive review of available biosensing methods to implement the control system for transradial prostheses based on the measured activity in remnant muscles. Covered techniques include electromyography, magnetomyography, electrical impedance tomography, capacitance sensing, near-infrared spectroscopy, sonomyography, optical myography, force myography, phonomyography, myokinetic control, and modern approaches to cineplasty. The paper also covers combinations of these approaches, which, in many cases, achieve better accuracy while mitigating the weaknesses of individual methods. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each technique along with their relationship with proprioceptive feedback, which is an important factor for intuitive control over the prosthetic device, especially for high dexterity prosthetic hands.

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“…Finger movements are linked with the physical operation of tendons, bones, and ligaments at the wrist [4][5][6][7]. Some studies have shown that an optical sensor [4], accelerometer and gyroscope [5,6], an array of barometric sensors [8], and a combination of sEMG, accelerometer, and gyroscope [9] around the wrist could be used for hand gesture recognition at the wrist. An inertial measurement unit (IMU), containing an accelerometer and a gyroscope, is a lightweight tiny chip that can be easily placed over the wrist [6].…”

Section: Introductionmentioning

confidence: 99%

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

Siddiqui

Chan

2020

PLoS ONE

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To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion. OPEN ACCESSCitation: Siddiqui N, Chan RHM (2020) Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist. PLoS ONE 15(1): e0227039. https://doi.org/10.

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Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing

Cited by 76 publications

References 46 publications

Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

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