Human-machine interface based on multi-channel single-element ultrasound transducers: A preliminary study

IEEE Trans. Ind. Electron.

Yan

Zhenfeng

et al. 2020

Self Cite

It is evident that the prevailing solution, myoelectric pattern recognition for prosthetic manipulation, constrains gesture-based interaction due to the lack of proportional control information such as exerted force. This paper reports an attempt, named simultaneous gesture recognition and muscle contraction force estimation, to realize proportional pattern recognition (PPR) control taking advantage of arm muscle deformation via wearable ultrasound sensing. We experiment with eight types of predefined hand motions, with a range of 0 -60% maximum voluntary contraction (MVC) using a wearable multi-channel Amode ultrasound system. The experiment result demonstrates that above 93.7% of gestures are correctly recognized during dynamic muscle contraction forces (0 -60% MVC), albeit only training at a slight force level (<6% MVC). Besides, the adopted non-parametric Gaussian process regression estimates the muscle contraction forces accurately synchronously, with average coefficient of determination (R 2 ) and normalized root-mean-square error (nRMSE) of 0.927 and 0.102, respectively. These research outcomes demonstrate the feasibility of ultrasound-based PPR control, paving the way for musculature-driven applications including finer prosthetic control, remote manipulation and rehabilitation treatment.

Section: Signal Processingmentioning

confidence: 99%

A Proportional Pattern Recognition Control Scheme for Wearable A-mode Ultrasound Sensing

IEEE Trans. Ind. Electron.

Yan

Zhenfeng

et al. 2020

Self Cite

“…Considering the high resolution of the ultrasound imaging, it is suitable for some dexterous tasks like finger motion classification. Some researchers have studied the finger motion classification using the ultrasound imaging technology and derived promising results [21]- [23].…”

Section: B Ultrasound-based Gesture Classificationmentioning

confidence: 99%

Ultrasound-Based Sensing Models for Finger Motion Classification

Huang

IEEE J. Biomed. Health Inform.

et al. 2018

Self Cite

Motions of the fingers are complex since hand grasping and manipulation are conducted by spatial and temporal coordination of forearm muscles and tendons. The dominant methods based on surface electromyography (sEMG) could not offer satisfactory solutions for finger motion classification due to its inherent nature of measuring the electrical activity of motor units at the skin's surface. In order to recognize morphological changes of forearm muscles for accurate hand motion prediction, ultrasound imaging is employed to investigate the feasibility of detecting mechanical deformation of deep muscle compartments in potential clinical applications. In this study, finger motion classification has been represented as subproblems: recognizing the discrete finger motions and predicting the continuous finger angles. Predefined 14 finger motions are presented in both sEMG signals and ultrasound images and captured simultaneously. Linear discriminant analysis classifier shows the ultrasound has better average accuracy (95.88%) than the sEMG (90.14%). On the other hand, the study of predicting the metacarpophalangeal (MCP) joint angle of each finger in nonperiod movements also confirms that classification method based on ultrasound achieves better results (average correlation 0.89 $\pm$ 0.07 and NRMSE 0.15 $\pm$ 0.05) than sEMG (0.81 $\pm$ 0.09 and 0.19 $\pm$ 0.05). The research outcomes evidently demonstrate that the ultrasound can be a feasible solution for muscle-driven machine interface, such as accurate finger motion control of prostheses and wearable robotic devices.

“…Ultrasound could be an alternative due to its capability of penetrating several centimeters below the skin and returning the information of both superficial and deep muscles in a noninvasive manifestation [17], [18]. Recent studies present evidence in visualizing muscle activities and HMI [19]- [32]. The research group led by Zheng et al conducted a few studies about ultrasound-image based HMI.…”

Section: Introductionmentioning

confidence: 99%

“…Hettiarachchi et al conducted the finger motion recognition for transradial amputees using eight customized A-mode ultrasound transducers [31]. Li et al also demonstrated that five single digit flexion could be classified using four A-mode ultrasound transducers, with a classification accuracy up to 96% [32].…”

Section: Introductionmentioning

confidence: 99%

Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition

IEEE Trans. Neural Syst. Rehabil. Eng.

Sun

Zhou

et al. 2018

Self Cite

112

It is evident that surface electromyography (sEMG) based human-machine interfaces (HMI) have inherent difficulty in predicting dexterous musculoskeletal movements such as finger motions. This paper is an attempt to investigate a plausible alternative to sEMG, ultrasound-driven HMI, for dexterous motion recognition due to its characteristic of detecting morphological changes of deep muscles and tendons. A multi-channel A-mode ultrasound lightweight device is adopted to evaluate the performance of finger motion recognition; an experiment is designed for both widely acceptable offline and online algorithms with eight able-bodied subjects employed. The experiment result presents that the offline recognition accuracy is up to 98.83% ± 0.79%. The real-time motion completion rate is 95.4% ± 8.7% and online motion selection time is 0.243 ± 0.127 s. The outcomes confirm the feasibility of A-mode ultrasound based wearable HMI and its prosperous applications in prosthetic devices, virtual reality, and remote manipulation.