UI-GAN: Generative Adversarial Network-Based Anomaly Detection Using User Initial Information for Wearable Devices

Nho, Young-Hoon; Ryu, Semin; Kwon, Dong‐Soo

doi:10.1109/jsen.2021.3054394

Cited by 38 publications

(10 citation statements)

References 36 publications

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“…However, ethical considerations and responsible deployment are crucial aspects to address as these transformative capabilities evolve. 116,117…”

Section: Future Directions and Emerging Trendsmentioning

confidence: 99%

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Babu,

Mandal

2024

ACS Appl. Energy Mater.

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Embark on a journey to unlock the pivotal roadmap for seamlessly integrating triboelectric nanogenerators (TENGs) with advanced machine learning algorithms. This review article endeavors to present a comprehensive strategy for the integration of machine learning algorithms with TENGs specifically tailored for gesture monitoring applications. The primary objective is to outline a meticulous methodology for the seamless fusion of TENG technology and machine learning techniques by elucidating the key tools for data collection, robust analysis, potential challenges, and compelling case studies that highlight the tangible applications of this fusion across diverse domains. This review not only delves into the underlying principles of TENG but also explores the fundamental tenets of machine learning, making it accessible to a wide readership, from novices to experts. The goal is to facilitate the effective implementation of this integrated framework, enabling the development of more efficient and sophisticated gesture monitoring solutions for elevating the human−machine interaction to greater heights.

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“…However, ethical considerations and responsible deployment are crucial aspects to address as these transformative capabilities evolve. 116,117…”

Section: Future Directions and Emerging Trendsmentioning

confidence: 99%

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Babu,

Mandal

2024

ACS Appl. Energy Mater.

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“…Sheikh et al [ 12 ] established a wheelchair fall-detection system based on low-cost embedded inertial sensors and unsupervised one-class support vector machines (OCSVM). Nho et al [ 13 ] presented a novel fall-detection method based on the generative adversarial network (GAN) using a heart rate sensor and an accelerometer. Wu et al [ 14 ] constructed a fall-detection system based on wearable sensors.…”

Section: Related Workmentioning

confidence: 99%

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Yan

Wang

Shi

et al. 2023

Sensors

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The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.

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“…However, the detection precision of this model is low in a benchmark dataset. In [19], the author proposed a GANbased anomaly detection model using the value of a heart rate sensor and an accelerometer. In [20], the author developed an anomaly detection framework based on federated learning, combing CNN with LSTM.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

Yang¹,

Wang²,

Hao³

et al. 2022

Preprint

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Industrial control systems (ICSs) are facing increasing cyber-physical attacks that can cause catastrophes in the physical system. Efficient anomaly detection models in the industrial sensor networks are essential for enhancing ICS reliability and security, due to the sensor data is related to the operational state of the ICS. Considering the limited availability of computing resources, this paper proposes a hybrid anomaly detection approach in cloud-edge collaboration industrial sensor networks. The hybrid approach consists of sensor data detection models deployed at the edges and a sensor data analysis model deployed in the cloud. The sensor data detection model based on Gaussian and Bayesian algorithms can detect the anomalous sensor data in real-time and upload them to the cloud for further analysis, filtering the normal sensor data and reducing traffic load. The sensor data analysis model based on Graph convolutional network, Residual algorithm and Long short-term memory network (GCRL) can effectively extract the spatial and temporal features and then identify the attack precisely. The proposed hybrid anomaly detection approach is evaluated using a benchmark dataset and baseline anomaly detection models. The experimental results show that the proposed approach can achieve an overall 11.19% increase in Recall and an impressive 14.29% improvement in F1-score, compared with the existing models.

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UI-GAN: Generative Adversarial Network-Based Anomaly Detection Using User Initial Information for Wearable Devices

Cited by 38 publications

References 36 publications

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

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