Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?

Botros, Fady S.; Phinyomark, Angkoon; Scheme, Erik

doi:10.1109/tii.2020.3041618

Cited by 81 publications

(88 citation statements)

References 53 publications

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“…Although only four sensors were used in that previous study, their location was consistent with our electrode placement below the head of the ulna. In contrast, Botros et al (16) reported higher accuracies (~88%) for offline single and combined finger prediction using the same feature set, but their electrodes targeted the muscle fibers in the proximal and medial part of the wrist instead of the tendons. The main limitation of the tendon electric signals is their high crosstalk due to the convergence of the muscle tendons in a reduced space.…”

Section: Discussionmentioning

confidence: 97%

“…The results obtained without decoding the neural activity were consistent with those reported in previous studies (15) and indicate poor classification performance. Conversely, the proposed neural decoding allowed for >95% accuracy over ten finger tasks at multiple force levels, which was substantially greater than without decoding the tendon signals as well as than conventional EMG-based interfaces (16,17,41).…”

Section: User Intent Prediction (Offline)mentioning

confidence: 95%

“…A few studies have recorded the electrical activity at the wrist to decode motor intention (14)(15)(16), but the reported accuracy has been generally low. Using temporal EMG features, Botros et al (16) achieved 88% classification accuracy in offline prediction of individual finger tasks and Jiang et al (15) obtained 75% in a real-time control task. These accuracies are lower than those generally reported with classic EMG recordings from the forearm (17,18).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Guerra

Barsakcioglu²,

Vujaklija³

et al. 2021

Preprint

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Despite the promising features of neural interfaces, their trade-off between information transfer and invasiveness has limited translation and viability outside research settings. Here, we present a non-invasive neural interface that provides access to spinal motoneuron activities from a sensor band at the wrist. The interface decodes electric signals present at the tendon endings of the forearm muscles by using a model of signal generation and deconvolution. First, we evaluated the reliability of the interface to detect motoneuron firings, and thereafter we used the decoded neural activity for the prediction of finger movements in offline and real-time conditions. The results showed that motoneuron activity decoded from the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time control. These findings demonstrate the feasibility of a wearable, non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

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Section: Discussionmentioning

confidence: 97%

Section: User Intent Prediction (Offline)mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Guerra

Barsakcioglu²,

Vujaklija³

et al. 2021

Preprint

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show abstract

“…Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG is the comprehensive photoelectrical signal of potential muscle action on the surface of the skin ( Botros et al, 2020 ). It is a kind of non-stationary signal, and its strength is sensitively proportional to the degree of muscle activity, which makes it can accurately represent the gesture of fingers ( Botros et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Among them, surface EMG is the comprehensive photoelectrical signal of potential muscle action on the surface of the skin ( Botros et al, 2020 ). It is a kind of non-stationary signal, and its strength is sensitively proportional to the degree of muscle activity, which makes it can accurately represent the gesture of fingers ( Botros et al, 2020 ). Therefore, surface EMG-based is widely adopted to achieve FGR.…”

Section: Introductionmentioning

confidence: 99%

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Cao

Cai

et al. 2021

Front. Comput. Neurosci.

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Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

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A Multi‐Module Sensing and Bi‐Directional HMI Integrating Interaction, Recognition, and Feedback for Intelligent Robots

Fang,

Zhu,

Zeng

et al. 2023

Adv Funct Materials

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Rapid advances in robotics have placed urgent demands on more intelligent human‐machine interaction technologies. Specifically, the way of establishing dual‐way intuitive communication with a consistent sensory system can greatly enhance efficiency and reliability. Here, a bi‐directional human‐machine interface (HMI) is designed by applying starch‐based hydrogel sensors. The whole system consists of a multi‐modal wearable sensory exoskeleton with a haptic feedback module and sensory robotic hand. The sensory exoskeleton with strain‐sensing glove and rotation‐sensing arm can capture and project the motion of the entire upper limb. The system offers object recognition functions by utilizing a sensing array on the robotic hands and machine learning algorithms, which can identify the shape and hardness information. The recognized results can be delivered back to the operator via vision and vibrational haptic feedback, respectively. This dual‐way intelligent sensory system shows potential application in many key fields such as the Internet of Things, teleoperation, and medical robotics.

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Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?

Cited by 81 publications

References 53 publications

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

A Multi‐Module Sensing and Bi‐Directional HMI Integrating Interaction, Recognition, and Feedback for Intelligent Robots

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