Wrist and Finger Gesture Recognition With Single-Element Ultrasound Signals: A Comparison With Single-Channel Surface Electromyogram

With the ability to detect volumetric changes of contracting muscles, ultrasound (US) was a potential technique in the field of human-machine interface. Compared to the US imaging (B-mode US), the signal from a static single-element US transducer, A-mode US, was a more cost-effective and convenient...

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Vydáno v:IEEE transactions on biomedical engineering Ročník 66; číslo 5; s. 1277 - 1284
Hlavní autoři: He, Jiayuan, Luo, Henry, Jia, Jie, Yeow, John T. W., Jiang, Ning
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adolescent
Adult
Change detection
Electrodes
Electromyography
Electromyography - methods
Female
Fingers - diagnostic imaging
Fingers - physiology
Gesture recognition
Gestures
Human machine interface (HMI)
Humans
Male
Muscles
pattern recognition
Pattern Recognition, Automated
Probes
Signal Processing, Computer-Assisted
surface electromyogram (sEMG)
Thumb
Transducers
Ultrasonic imaging
Ultrasonography - methods
Ultrasound
ultrasound signal
Wrist
Wrist - diagnostic imaging
Wrist - physiology
Young Adult
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:With the ability to detect volumetric changes of contracting muscles, ultrasound (US) was a potential technique in the field of human-machine interface. Compared to the US imaging (B-mode US), the signal from a static single-element US transducer, A-mode US, was a more cost-effective and convenient way toward the real-world application, particularly the wearables. This study compared the performance of the single-channel A-mode US with single-channel surface electromyogram (sEMG) signals, one of the most popular signal modalities for wrist and finger gesture recognition. We demonstrated that A-mode US outperformed sEMG in six out of nine gestures recognition, while sEMG was superior to A-mode US on the detection of the rest state. We also demonstrated that, through feature space analysis, the advantage of A-mode US over sEMG for gesture recognition was due to its superior ability in detecting information from deep musculature. This study presented the clear complementary advantages between A-mode US and sEMG, indicating the possibility of fusing two signal modalities for the gesture recognition applications.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2872593