A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition

Jiang, Shuo; Gao, Qinghua; Liu, Huaiyang; Shull, Peter B.

doi:10.1016/j.sna.2019.111738

Cited by 98 publications

(49 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time domain features have been widely used for their classification performance and low computational complexity. 44 Moreover, these features have been reported in other classification studies 41–43 as well.…”

Section: Methodssupporting

confidence: 62%

A comparative study of motion detection with FMG and sEMG methods for assistive applications

Islam

Waris

Kamavuako

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

show abstract

Section: Methodssupporting

confidence: 62%

A comparative study of motion detection with FMG and sEMG methods for assistive applications

Islam

Waris

Kamavuako

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

show abstract

“…The FMG method has inherent drawbacks, such as poor spatial resolution caused by adjacent muscle crosstalk and susceptibility to muscle fatigue. Jiang et al [ 107 ] proposed a co-located approach for capturing both EMG and FMG simultaneously at the same location. Combining these two sensing methods significantly increased the classification accuracy as compared with either of the single sensing modalities.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…While current wearable approaches are limited because of low sensitivity or poor linearity, our prototype takes advantage of the fact that hand motion changes the shape of the wrist due to the expansions and contractions of the muscles, which brings position and thickness transformations of tendons and muscles, producing deformations on the surface contour of the wrist. We thus used a wristband with ten embedded modified barometric sensors with high sensitivity and excellent linearity to measure the activity of the tendons and muscles that cause finger movement [23]. The deformations apply pressure to the surface of modified barometric sensors.…”

Section: Wristband Prototypementioning

confidence: 99%

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

Gao

Jiang

Shull

2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition

Cited by 98 publications

References 21 publications

A comparative study of motion detection with FMG and sEMG methods for assistive applications

A comparative study of motion detection with FMG and sEMG methods for assistive applications

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

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

Contact Info

Product

Resources

About