The LET Procedure for Gesture Recognition With Multiple Forearm Angles

Cai, Shaoxiong; Lu, Zongxing; Lin, Guo‐Qiang; Qing, Zengyu; Yao, Ligang

doi:10.1109/jsen.2022.3177475

Cited by 14 publications

(3 citation statements)

References 35 publications

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“…Li et al [ 166 ] made use of A‐mode ultrasound in conjunction with PCA for dimensionality reduction and LDA for classification to recognize with 96% accuracy six distinct movements. Beyond this, there have been studies on force recognition, [ 167 ] wrist movement recognition, [ 168 ] and arm position [ 169 ] based on A‐mode ultrasound. More recently, Lu et al [ 1 ] created a wearable gesture recognition pipeline using PCA for dimensionality reduction and LDA and SVM for the classification of 10 gestures in real time.…”

Section: Applicationsmentioning

confidence: 99%

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Putcha

Nguyen

Smith

et al. 2023

Advanced Intelligent Systems

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Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In the context of human–machine interfaces, muscle trackers can serve as the “sensory” component, providing real‐time information to machines, such as exoskeletons and prosthetics, that can act upon such information for therapeutic and functional aid. Recently developed devices for muscle tracking rely on combinations of sensor modalities and algorithms to extract information from biosignals track muscle activity, or even extrapolate kinematic information, including gestures. However, a number of obstacles remain to be overcome to further facilitate the practical implementation of muscle‐tracking technologies, the most notable being real‐time analysis of biosignal data and extracting kinematic information from complex movements. This review attempts to cover the mechanisms behind various sensor modalities and algorithms commonly used for muscle tracking, as well as establish the current state of applications within the field. Given its multidisciplinary nature and ability to free users from rehabilitation constraints, the field of muscle tracking holds significant promise for future study.

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Section: Applicationsmentioning

confidence: 99%

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Putcha

Nguyen

Smith

et al. 2023

Advanced Intelligent Systems

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“…Human-Computer interaction, which is widely applied inprosthetic control, virtual reality, clinical rehabilitation, etc. Over the past few decades, researchers have explored a variety of HMI based on gesture recognition, including electroencephalography (EEG) [1], electrocorticography (ECoG) [2], mechanomyography (MMG) [3], computer vision, surface electromyography (sEMG) and ultrasound [4]- [5]. Among them: Since sEMG has the characteristics of non-invasiveness, convenience, freedom of space constraints, and close correlation with forearm muscle contraction, researchers have conducted extensive exploration on sEMG-based gesture recognition [6]- [7].…”

Section: Introduction He Human-machine Interface (Hmi) Based On Gestu...mentioning

confidence: 99%

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

Qing

Liu

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not been effectively combined. There are limitations in applications such as prosthetic control and clinical rehabilitation, etc. In this paper, a grasping gesture and force recognition strategy based on wearable A-mode ultrasound and two-stage cascade model is proposed, which can simultaneously estimate the force while classifying the grasping gesture. This paper experiments five grasping gestures and four force levels (5-50%MVC). The results demonstrate that the performance of the proposed model is significantly better than that of the traditional model both in classification and regression (p<0.001). Additionally, the two-stage cascade regression model (TSCRM) used the Gaussian Process regression model (GPR) with the mean and standard deviation (MSD) feature obtains excellent results, with normalized root-mean-square error (nRMSE) and correlation coefficient (CC) of 0.1049 ± 0.0374 and 0.9461±0.0354, respectively. Besides, the latency of the model meets the requirement of real-time recognition (T<15ms). Therefore, the research outcomes prove the feasibility of the proposed recognition strategy and provide a reference for the field of prosthetic control, etc.

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“…Therefore, these systems often rely on amplitude-mode (A-mode) signals to obtain information from several muscles concurrently [14]- [19]. The A-mode signals obtained from the single-element wearable transducers [20]- [22] are processed, and machine learning models are utilized for hand gesture recognition [14], [18], [19], [23]- [25] in individuals with upper extremity amputation and classification of ambulation and gait phases in individuals with lower-limb amputation. In fact, the utility of A-mode wearable ultrasound has also been used demonstrated for assessment of cardiovascular function.…”

mentioning

confidence: 99%

Wearable Ultrasound System using Low-Voltage Time Delay Spectrometry for Dynamic Tissue Imaging

Bashatah,

Mukherjee,

Afsana

et al. 2024

Preprint

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Objective: Wearable ultrasound is emerging as a new paradigm of real-time imaging in freely moving humans and has wide applications from cardiovascular health monitoring to human gesture recognition. However, current wearable ultrasound devices have typically employed pulse-echo imaging which requires high excitation voltages and sampling rates, posing safety risks, and requiring specialized hardware. Our objective was to develop and evaluate a wearable ultrasound system based on time delay spectrometry (TDS) that utilizes low-voltage excitation and significantly simplified instrumentation.Methods: We developed a TDS-based ultrasound system that utilizes continuous, frequency-modulated sweeps at low excitation voltages. By mixing the transmit and receive signals, the system digitizes the ultrasound signal at audio frequency (kHz) sampling rates. Wearable ultrasound transducers were developed, and the system was characterized in terms of imaging performance, acoustic output, thermal characteristics, and applications in musculoskeletal imaging.Results: The prototype TDS system is capable of imaging up to 6 cm of depth with signal-to-noise ratio of up to 42 dB at a spatial resolution of 0.33 mm. Acoustic and thermal radiation measurements were within clinically safe limits for continuous ultrasound imaging. We demonstrated the ability to use a 4-channel wearable system for dynamic imaging of muscle activity.Conclusion: We developed a wearable ultrasound imaging system using TDS to mitigate challenges with pulse echo-based wearable ultrasound imaging systems. Our device is capable of highresolution, dynamic imaging of deep-seated tissue structures and is safe for long-term use.Significance: This work paves the way for low-voltage wearable ultrasound imaging devices with significantly reduced hardware complexity.

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The LET Procedure for Gesture Recognition With Multiple Forearm Angles

Cited by 14 publications

References 35 publications

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

Wearable Ultrasound System using Low-Voltage Time Delay Spectrometry for Dynamic Tissue Imaging

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