Arc fault detection using artificial intelligence: Challenges and benefits

Tian, Chunpeng; Xu, Zhaoyang; Wang, Lukun; Liu, Yunjie

doi:10.3934/mbe.2023552

Cited by 57 publications

(25 citation statements)

References 83 publications

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“…For example, Radhakrishnan et al (2021a) proposed three cases from the Danish industry that demonstrate the potential of immersive VR training for improving the performance and safety of workers in various fields. The cases include VR training for welders, electricians and forklift operators (Tian et al , 2023).…”

Section: Resultsmentioning

confidence: 99%

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Deng,

Wang,

Dong

et al. 2023

RIA

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Purpose This systematic review, following preferred reporting items for systematic reviews and meta-analysis guidelines, rigorously investigates the emergent role of virtual reality (VR) technology in human movement training. The purpose of this study is to explore the effectiveness and evolution of VR in enhancing movement training experiences. Design/methodology/approach Acknowledging its pivotal role in diverse applications, such as sports and rehabilitation, human movement training is currently experiencing accelerated evolution, facilitated by the proliferation of wearable devices and mobile applications. This review conducted an exhaustive search across five different electronic databases, such as Web of Science, PubMed and ProQuest, resulting in the selection of 69 eligible articles published within the past five years. It also integrates 40 studies into a narrative summary, categorized based on the level of immersion offered by respective VR systems. Findings Enhanced immersion in VR potentially augments the effectiveness of movement training by engendering more realistic and captivating experiences for users. The immersive and interactive environments provided by VR technology enable tailored training experiences accompanied by precise, objective feedback. This review highlights the benefits of VR in human movement training and its potential to revolutionize the way training is conducted. Originality/value This systematic review contributes significantly to the existing literature by providing a comprehensive examination of the efficacy and evolution of VR in human movement training. By organizing the findings based on the level of immersion offered by VR systems, it provides valuable insights into the importance of immersion in enhancing training outcomes. In addition, this study identifies the need for future research focusing on the impacts of VR on learning and performance, as well as strategies to optimize its effectiveness and improve accessibility.

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Section: Resultsmentioning

confidence: 99%

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Deng,

Wang,

Dong

et al. 2023

RIA

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“…The black area indicates that the electrode channel information in this section contributes 0 to the gesture recognition task, and not all of the electrode channel acquisition information has a positive effect on the gesture recognition task. Tian et al outlined the significant impact of artificial intelligence technology on the application of sensors [24] . Acquisition devices for sEMG signals also generate redundant information during gesture recognition tasks.…”

Section: Grad-cam Resultsmentioning

confidence: 99%

Muscle synergy analysis for gesture recognition based on sEMG images and Shapley value

Ao,

Wang,

Wang

et al. 2023

Intelligence & Robotics

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Muscle synergy analysis for gesture recognition is a fundamental research area in human-machine interaction, particularly in fields such as rehabilitation. However, previous methods for analyzing muscle synergy are typically not end-to-end and lack interpretability. Specifically, these methods involve extracting specific features for gesture recognition from surface electromyography (sEMG) signals and then conducting muscle synergy analysis based on those features. Addressing these limitations, we devised an end-to-end framework, namely Shapley-value-based muscle synergy (SVMS), for muscle synergy analysis. Our approach involves converting sEMG signals into grayscale sEMG images using a sliding window. Subsequently, we convert adjacent grayscale images into color images for gesture recognition. We then use the gradient-weighted class activation mapping (Grad-CAM) method to identify significant feature areas for sEMG images during gesture recognition. Grad-CAM generates a heatmap representation of the images, highlighting the regions that the model uses to make its prediction. Finally, we conduct a quantitative analysis of muscle synergy in the specific area obtained by Grad-CAM based on the Shapley value. The experimental results demonstrate the effectiveness of our SVMS method for muscle synergy analysis. Moreover, we are able to achieve a recognition accuracy of 94.26% for twelve gestures while reducing the required electrode channel information from ten to six dimensions and the analysis rounds from about 1000 to nine.

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“…Undoubtedly, the deployment of AI-driven approaches in real-world scenarios presents several challenges, as exemplified by the cases discussed in this study [37]. To address these challenges and further advance the research, future work will concentrate on implementing the proposed approach in practical settings.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Learner engagement regulation of dual-user training based on deep reinforcement learning

Yang,

Liu,

Liu

et al. 2023

Robotica

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The dual-user training system is essential for fostering motor skill learning, particularly in complex operations. However, the challenge lies in the optimal tradeoff between trainee ability and engagement level. To address this problem, we propose an intelligent agent that coordinates trainees’ control authority during real task engagement to ensure task safety during training. Our approach avoids the need for manually set control authority by expert supervision. At the same time, it does not rely on pre-modeling the trainee’s skill development. The intelligent agent uses a deep reinforcement learning (DRL) algorithm based on trainee performance to adjust adaptive engagement during the training process. Our investigation aims to provide reasonable engagement for trainees to improve their skills while ensuring task safety. Our results demonstrate that this system can seek the policy to maximize trainee participation while guaranteeing task safety.

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Arc fault detection using artificial intelligence: Challenges and benefits

Cited by 57 publications

References 83 publications

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Muscle synergy analysis for gesture recognition based on sEMG images and Shapley value

Learner engagement regulation of dual-user training based on deep reinforcement learning

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