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- 저자 : James Rigor Camacho (검색결과 3 건)
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Implementation of MTCNN-based Face Detection Algorithm on Edge AI Device
James Rigor Camacho,Angela Caliwag,Jonghun Kwon,Wansu Lim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Edge AI devices are high performing computers that can process complex algorithms and perform deep-learning applications. However, implementing a Face Detection algorithm in Edge AI device would require some dependencies and associated components tomake the application work. In this study, we introduced a MTCNN-based Face Detection Algorithm in Jetson Nano as the Edge AI device that will process the computations and perform the detection of facial features and faces. We illustrate the path of how data is being processed in the device. Started from gathering images, storing the data in the memory, then processing the images in our model design to perform the task of locating faces in an image.
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Edge AI-based Brain-Computer Interface for Real-time Applications
Henar Mike Canilang,Chigozie Uzochukwu Udeogu,James Rigor Camacho,Erick Valverde,Angela Caliwag,Wansu Lim 대한인간공학회 2021 대한인간공학회 학술대회논문집 Vol.2021 No.11
Objective: This study aims to integrate brain computer interface (BCI) to edge AI devices for real-time EEG signal processing applications. For the specific implementation in this paper, we applied edge AI device-based EEG signal processing for emotion recognition. Background: The emergence of Electroencephalogram (EEG) based applications for intelligent applications is projected to have rapid advancements in the future. The BCI system enables efficient brain signal acquisition. Current intelligent convergence of EEG based applications includes brain signal processing integrated to deep learning models. It is expected that this convergence in intelligent EEG based applications will push through to on-device local processing such as edge AI devices for portability in state-of-the-art applications. The portability and practical usage of these systems in real-world applications could lead to the development and deployment of many other advanced embedded systems for EEG-based applications. Systems that can run locally on the edge without needing to be connected to a mobile network. Edge AI devices are the leading-edge computing platforms that process data locally to overcome the current constraints of IoT application. This paves way to the integration of edge-based processing as the computing paradigm to process and acquire EEG signals. Owing to the current research advancement for both EEG and edge applications, this paper aims to propose one of the many systematic applications of deploying edge-based EEG using a brain computer interface. Method: The input for this edge-based EEG signal processing is through the BCI interfaced to the edge AI device. The edge AI device deployed with a deep learning model for specific applications locally processes the acquired signal. These acquired signals are valuable for training deep learning models to realize practical applications at the edge. The processed EEG signals enable the system response of the system such as rapid emotion recognition. Results: Varying EEG signals were acquired in each of the BCI channels. These brain signals are segmented to different brain signal clusters such as Gamma waves (30㎐ to 100㎐), Beta waves (12㎐ – 30㎐), Alpha waves (7.5㎐ – 12㎐), Theta waves (4㎐-7.5㎐) and Delta waves (0.1㎐-4㎐) which have specific brain wave description. As for EEG emotion recognition applications, these wave signals are essential for efficient and accurate emotion recognition. The alpha, beta, and gamma waves are identified to be the most discriminative frequency ranges to identify emotion. Each of the EEG signal is classified for emotion recognition and identification such as 1) valence, 2) dominance, 3) arousal and 4) liking. High and low responses from these wave signals have corresponding positive, neutral, and negative emotions based on their neural patterns at parietal and occipital sites. Other applications can use the acquired EEG signals thus maximizing the possible application of edge-based EEG signal processing. Conclusion: The local processing of the EEG signal at the edge enables the edge-based EEG system application thus enabling system response and actuation. Edge EEG also enables local and cloud co-processing whereas this maximizes the benefits of the edge computing paradigm. With this co-processing capability, it enables an adaptive and portable real-time EEG signal processing which is a constraint to conventional EEG based emotion recognition system. Application: EEG is a physiological based emotion recognition which proves to be more accurate than conventional non-physiological emotion recognition. Also, with an edge-based EEG application, it enables portability and flexibility in terms of its deployment. This application aims to be a state-of-the-art innovation to existing physiological and non-physiological emotion recognition. Furthermore, this research paper implementation aims to emphasize the vast possible applications of edge-based EEG signal processing to bridge
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