Toward Deep Generalization of Peripheral EMG-Based Human-Robot Interfacing: A Hybrid Explainable Solution for NeuroRobotic Systems

Gulati, Paras; Hu, Qin; Atashzar, S. Farokh

doi:10.1109/lra.2021.3062320

Cited by 41 publications

(22 citation statements)

References 23 publications

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“…The preliminary results on the three benchmark datasets revealed that the CNN feature extractor considerably improved the fuzzy classifier?s performance and interpretation ability. With the same intention, Gulati et al (2021) demonstrated a hybrid interpretable solution to identify the 17 types of gestures in a user-specific approach. A GradCAM method was also developed to optimize and the generalized model architecture to explain its predictions.…”

Section: Hybrid Interpretable Modelmentioning

confidence: 99%

Explainable artificial intelligence: a comprehensive review

et al. 2021

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Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.

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Section: Hybrid Interpretable Modelmentioning

confidence: 99%

Explainable artificial intelligence: a comprehensive review

et al. 2021

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“…The MLP model has 30 neurons on the hidden layer. We modified our recently proposed hybrid model [39] specifically for the identification problem. The hybrid model has a CNN module followed by an LSTM module.…”

Section: Comparative Studymentioning

confidence: 99%

“…Note: Gestures are from 1 to 40. Group A ( [9,10,11,12,13,14,16,17,18,19,20,21,34,35,36,38,39,40]) has 18 training gestures in Set 1 and Set 2. Group A c includes the complement gestures.…”

Section: Appendix a Model Optimizationmentioning

confidence: 99%

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X-MyoNET: Biometric Identification using Deep Processing of Transient Surface Electromyography

Sarmadi

Gulati

et al. 2021

Preprint

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The rapid development of the Internet and various applications such as the Internet of Medical Things (IoMT) has raised substantial concerns about personal information security. Conventional methods (e.g., passwords) and classic biological features (e.g., fingerprints) are security deficient because of potential information leakage and hacking. Biometrics that express behavioral features suggest a robust approach to achieving information security because of the corresponding uniqueness and complexity. In this paper, we consider identifying human subjects based on their transient neurophysiological signature captured using multichannel upper-limb surface electromyography (sEMG). An explainable artificial intelligence (XAI) approach is proposed to process the internal dynamics of temporal sEMG signals. We propose and prove the suitability of “transient sEMG” as a biomarker that can identify individuals. For this, we utilize the Gradient-weighted Class Activation Mapping (Grad-CAM) analysis to explain the network’s attention. The outcome not only decodes and makes the unique neurophysiological pattern (i.e., motor unit recruitment during the transient phase of contraction) associated with each individual visualizable but also generates an optimizing two-dimensional (2D) spectrotemporal mask used to significantly reduce the size of the model and the trainable parameters. The resulting mask selectively and systematically samples the spectrotemporal characteristics of the users’ neurophysiological responses, discarding 40% of the input space while securing the accuracy of about 74% with much shallower neural network architecture. In the systematic comparative study, we find that our proposed model outperforms several state-of-the-art algorithms. For broader impacts, we anticipate our design of a compact, practical, interpretable, and robust identification system that requires only a minimal number of gestures and sensors (only 7% of the entire data set) to be a starting point for small and portable identification hardware.

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“…An RNN model can capture the underlying temporal dynamics from sEMG signals since each hidden cell comprises the information from all previous hidden cells and the observation of the current timestamp. Some recent articles (for example [18], [19]), including our previous work [20], [21], have proposed hybrid models that leverage the benefits of both CNNs and RNNs for motor intention detection using sEMG signals. In [20], we proposed a hybrid approach that achieves high performance on conventional user-specific and generalized gesture classi-fication, with reduced need for re-training and re-calibration.…”

Section: Introductionmentioning

confidence: 99%

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Sun

Libby

et al. 2021

Preprint

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Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of “dilation foci” to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

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Toward Deep Generalization of Peripheral EMG-Based Human-Robot Interfacing: A Hybrid Explainable Solution for NeuroRobotic Systems

Cited by 41 publications

References 23 publications

Explainable artificial intelligence: a comprehensive review

Explainable artificial intelligence: a comprehensive review

X-MyoNET: Biometric Identification using Deep Processing of Transient Surface Electromyography

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

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