A Wearable Ultrasound System for Sensing Muscular Morphological Deformations

Yang, Xingchen; Chen, Zhen-Feng; Hettiarachchi, Nalinda; Yan, Jipeng; Liu, Honghai

doi:10.1109/tsmc.2019.2924984

Cited by 57 publications

(35 citation statements)

References 39 publications

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“…The angle between the upper arm and the forearm was about 120 • . Eight A-mode ultrasound transducers (Ø 9 × 11 mm) were placed around the forearm with a customized armband [20], approximately 10 cm distal to the elbow. The positions of transducers are shown in Fig.…”

Section: B Offline Experimentsmentioning

confidence: 99%

“…2, where the distance between channel 1 and channel 8 was a little larger and the others were equidistant. The transducers were sequentially driven by a customized wearable ultrasound system [20], with a frame rate of 10 Hz, a sampling rate of 20 MHz, and sampling dots of 1000 for each channel. In addition, a commercial inertial measurement unit (IMU) sensor (Xsens-MTi-100, Xsens Technologies B.V., Netherlands) was attached on the ventral side of the wrist to record the wrist rotation angle, sampling at 100 Hz.…”

Section: B Offline Experimentsmentioning

confidence: 99%

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Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ±1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision (R 2 ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculaturedriven artificial hand control and rehabilitation treatment.Index Terms-Subclass discriminant analysis, principal component analysis, simultaneous wrist/hand control, wearable ultrasound sensing.

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Section: B Offline Experimentsmentioning

confidence: 99%

Section: B Offline Experimentsmentioning

confidence: 99%

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…This high spatial specificity means that muscular cross-talk does not contaminate the extracted control signals that can be used to drive movement of a prosthesis. Numerous studies have established SMG as a viable option for gesture recognition and prosthesis control [23][24][25][26]. In particular, our group has demonstrated the ability to classify five individual digit movements with 97% accuracy [27] and 15 complex grasps with 91% accuracy [28] in able-bodied individuals.…”

Section: Introductionmentioning

confidence: 77%

Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

Engdahl

Dhawan

Lévay

et al. 2020

Preprint

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Controlling multi-articulated prosthetic hands with surface electromyography can be challenging for users. Sonomyography, or ultrasound-based sensing of muscle deformation, avoids some of the problems of electromyography and enables classification of multiple motion patterns in individuals with upper limb loss. Because sonomyography has been previously studied only in individuals with transradial limb loss, the purpose of this study was to assess the feasibility of an individual with transhumeral limb loss using this modality for motion classification. A secondary aim was to compare motion classification performance between electromyography and sonomyography. A single individual with transhumeral limb loss created two datasets containing 11 motions each (individual flexion of each finger, thumb abduction, power grasp, key grasp, tripod, point, pinch, wrist pronation). Electromyography or sonomyography signals associated with every motion were acquired and cross-validation accuracy was computed for each dataset. While all motions were usually predicted successfully with both electromyography and sonomyography, the cross-validation accuracies were typically higher for sonomyography. Although this was an exploratory study, the results suggest that controlling an upper limb prosthesis using sonomyography may be feasible for individuals with transhumeral limb loss.

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“…As a result, cross-talk is effectively suppressed and the control signals derived from the detected muscle activity have a high signal-to-noise ratio. Prior studies have demonstrated clear potential for the use of SMG in controlling a prosthesis or other humanmachine interface (33)(34)(35)(36)(37)(38)(39)(40). Our own work has shown that SMG is capable of accurately classifying motor intent for able-bodied individuals (41,42) and individuals with upper limb loss (43,44) in both offline and real-time settings.…”

mentioning

confidence: 84%

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

Engdahl¹,

Dhawan²,

Bashatah³

et al. 2020

Preprint

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Background: Although surface electromyography is commonly used as a sensing strategy for upper limb prostheses, it remains difficult to reliably decode the recorded signals for controlling multi-articulated hands. Sonomyography, or ultrasound-based sensing of muscle deformation, overcomes some of these issues and allows individuals with upper limb loss to reliably perform multiple motion patterns. The purposes of this study were to determine 1) the effect of training on classification performance with sonomyographic control and 2) the effect of training on the underlying muscle deformation patterns.Methods: A series of motion pattern datasets were collected from five individuals with transradial limb loss. Each dataset contained five ultrasound images corresponding each of the following five motions: power grasp, wrist pronation, key grasp, tripod, point. Participants initially performed the motions for the datasets without receiving feedback on their performance (baseline phase), then with visual and verbal feedback (feedback phase), and finally again without feedback (retention phase). Cross-validation accuracy and metrics describing the consistency and separability of the muscle deformation patters were computed for each dataset. Changes in classification performance over the course of the study were assessed using linear mixed models. Associations between classification performance and the consistency and separability metrics were evaluated using Pearson correlations.Results: The average cross-validation accuracy for each phase was 92% or greater and there was no significant change in cross-validation accuracy throughout training. Misclassifications of one motion as another did not persist systematically across datasets. Few of the correlations were significant, although many were moderate or greater in strength and showed a positive association between accuracy and improved consistency and separability metrics.Conclusions: Participants were able to achieve high classification rates upon their initial exposure to sonomyography and training did not affect their performance. Thus, motion classification using sonomyography may be highly intuitive and is unlikely to require a structured training protocol to gain proficiency.

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A Wearable Ultrasound System for Sensing Muscular Morphological Deformations

Cited by 57 publications

References 39 publications

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

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