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Virtual therapy Exergame for Upper Extremity Rehabilitation Using SmartWearable Sensors 8

Virtual Therapy Exergame for Upper Extremity Rehabilitation ...

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8th IEEE/ACM international conference on Connected Health - Applications, Systems and engineering Technologies (CHASE)

作者： Baron, Lauren Chheang, Vuthea Chaudhari, Arnit Liaqat, Arooj Chandrasekaran, Aishwarya Wang, Yufan Cashaback, Joshua thostenson, Erik Barmaki, Roghayeh Leila Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9798400701023

Virtual reality (VR) has been utilized for several applications and has shown great potential for rehabilitation, especially for home therapy. However, these systems solely rely on information from VR hand controllers, which do not fully capture the individual movement of the joints. In this paper, we propose a creative VR therapy exergame for upper extremity rehabilitation using multidimensional reaching tasks while simultaneously capturing hand movement from the VR controllers and elbow joint movement from a non-invasive fabric-based wearable sensor made by coating knit fabric with carbon nanotube. We conducted a preliminary study with non-clinical participants (n = 12, 7 F). In a 2 x 2 within-subjects study (orientation (vertical, horizontal) x configuration (flat, curved)), we evaluated the effectiveness and enjoyment of the exergame. the results show that there was a statistically significant difference in terms of task completion time between the two orientations. However, no significant differences were found in the number of mistakes in both orientation and configuration of the virtual exergame. this can lead to customizing therapy while maintaining the same level of intensity. the results of the resistance change generated from the wearable sensor revealed that the flat configuration in the vertical orientation induced more elbow stretches than the other conditions. Finally, we reported the subjective measures based on questionnaires for usability and user experience in different study conditions. In conclusion, the proposed VR exergame has the potential as a multi-modal sensory tool for personalized upper extremity home-based therapy and telerehabilitation.

关键词： Virtual therapy virtual reality smart wearable sensors upper extremity telerehabilitation human-computer interaction

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YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algorithm 12

YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algori...

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12th international conference on Advanced Cloud and Big Data, CBD 2024

作者： Ding, Yi Guo, Naixuan Meng, Haitao School of Information Engineering Yancheng Institute of Technology Yancheng China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration China

ISBN: (纸本)9798331511074

Gesture recognition has been widely used in many situations, such as human-computer interaction, virtual reality and smart home. To meet the current demands for accuracy and speed in gesture recognition, this paper proposes an improved algorithm based on YOLOv8, referred to as YOLOv8-GR. By replacing the original YOLOv8 detection head with a Dynamic Head, we integrate scale-aware, spatial-aware, and task-aware attention mechanisms within a unified framework. To enhance context information capture, the SPPF (Spatial Pyramid Pooling - Fast) module is optimized to a modified SPPF-LSKA (Large Separable Kernel Attention) module. In addition, we employ the EIoU loss function for more accurate evaluation of object detection results. To evaluate our algorithm, we integrate a gesture dataset in complex and diverse scenarios through network and field shooting annotations. Compared to the original YOLOv8, the proposed YOLOv8-GR achieves a 22.5% improvement in mAP50 and a 10.0% improvement in mAP50-95 on our dataset. We also compare YOLOv8-GR with the MobileNetv2-based SSD, which is a kind of one-stage object detection algorithm. While maintaining similar recognition accuracy, the computational cost of YOLOv8-GR is 9.9 GFLOPs, which is 15.8% of SSD, and our algorithm can be deployed on Raspberry Pi 4B. © 2024 IEEE.

关键词： Gesture recognition

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Emotion Recognition Using EEG Signals in human Brain Waves and Deep Learning Approaches 10

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. this study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. the classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). these bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). the LSTM and GRU models yield intermediate results, nearing 92% accuracy. the findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. this research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems. © 2025 IEEE.

关键词： Recurrent neural networks

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Shoppers interaction Classification Based on An Improved DenseNet Model Using RGB-D Data 8

Shoppers Interaction Classification Based on An Improved Den...

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8th international conference on Systems and Informatics, ICSAI 2022

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram University of Sfax Miracl Laboratory Enet'com Sfax Tunisia Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science Alkharj11942 Saudi Arabia

ISBN: (纸本)9781665474481

this study aims to present a deep learning approach utilizing transfer learning and an RGB-D dataset termed HADA (Hands dataset) acquired by a depth sensor from a top-view configuration capable of monitoring customers and classifying their interaction in intelligent retail settings. With the intention of developing an automated RGB-D approach for video analysis, we provide an innovative, intelligent technology that can comprehend customer behavior, in particular their interactions with items on the shelves. the camera system identifies the presence of humans and classifies their interactions with products accurately. through the RGB and depth frames, the system determines consumer interactions with shelf objects and identifies if a product is picked up, taken and subsequently returned, or if there is no touch at all. Our approach obtained good accuracy, precision, and recall, demonstrating the efficiency of the proposed model, and testing findings have proved that its performance in real-world conditions is adequate. © 2022 IEEE.

关键词： Classification (of information)

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Load Balancing Strategy Based on Stochastic theory 8

Load Balancing Strategy Based on Stochastic Theory

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Guo, Ruifeng Li, Youkang Wang, Hongliang Xiu, Pengfei Liu, Yunjiang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Provience Hum.-Comp. Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9781665450515

In order to further improve the load balancing strategy, this paper summarizes and designs a load balancing method based on random selection. By introducing the Balls into Bins problem into the load balancing strategy, random selection is used to reduce the time spent in the allocation process. And batch selection is used to reduce the possibility of overloading nodes. the result shows that these methods algorithm can get a better task allocation method in as short a time as possible. © 2022 IEEE.

关键词： Stochastic systems

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A Real Time Physics-Based Industrial Control System Honeynet Architecture for the Smart Grid 8

A Real Time Physics-Based Industrial Control System Honeynet...

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8th international conference on Information and Network Technologies, ICINT 2023

作者： De Pace, Gabriel Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350301458

Many details of cyber attacks occurred to Industrial Control Systems (ICS) such as power grids remain as a secret. To reveal those secrets and benefit a broad research community, we propose an original cyber-physical honeypot architecture that can seamlessly couple IT and OT components in an power grid. Specifically, we will provide high interaction with adversaries as physical processes of an ICS move forward, revealing attack strategies according to adversaries' knowledge on physical trajectories of the target system. Our preliminary evaluations demonstrate that the proposed CPS honeypot can present realistic device fingerprints and application-layer payload that piggybacks meaningful measurements conforming to the physical model of a power grid. In addition, by leveraging runtime high-fidelity OPAL-RT simulator, our CPS honeypot causes small latency, at least one order of magnitude smaller compared to the existing work. © 2023 IEEE.

关键词： Smart power grids

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Application Controlling Using Hand Gestures through Yolov5s 8th

Application Controlling Using Hand Gestures Through Yolov5s

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8th international conference on computers, Management and Mathematical Sciences, ICCM 2022

作者： Rajendra Prasad, K. Tarun Naga Sai, Chadaram Bhaskar Ganesh, Guntuboina Rohini Raghavi, Baina Sri Saketh, Gunnam Computer Science Engineering Gitam School of Technology Gandhi Institute of Technology and Management Gitam Visakhapatnam India

ISBN: (纸本)9783031511660

With the aid of Python, OpenCV, Yolov5s, and PyAutoGui, we attempt to use hand gestures for controlling an application. the need for unfettered interaction has made it difficult for conventional input devices like the keyboard and mouse to be used. In linguistics, gestures with the hand are a crucial part of body language (Nagalapuram GD, Roopashree S, Varshashree D, Dheeraj D, Nazareth DJ, Controlling media player with hand gestures using convolutional neural network. In: 2021 IEEE Mysore sub section international conference (MysuruCon). 978-1-6654-3888-9/21/$31.00 ©2021 IEEE, https://***/10.1109/MYSURUCON52639.2021.9641567, 2021). Using the hand as a device makes it simple to interface with humans and computers. It would be intriguing to engage with machines using hand gestures (Li G, Li D, Yang A, Real-time hand gesture detection based on Yolov5s. In: Proceedings of the 41st Chinese control conference, July 25–27, Hefei, China, 2022). Different applications, Various software programs, including Media Player, PowerPoint, PDF viewers, and software that accepts keyboard and mouse input, are controlled by hand gestures. interaction is simple, practical, and requires no tedious additional equipment when gestures are used (Paliwal M, Sharma G, Nath D, Rathore A, Mishra H, Mondal S, A dynamic hand gesture recognition system for controlling VLC media player. In: 2013 international conference on advances in technology and engineering (ICATE). IEEE, 2013). A camera-based hand detection system that employs a live webcam stream to identify the gesture once it has been observed. this system gives the user access to essential keyboard functions with the aid of the PyAutoGui package. With this technology, we can increase the usability of computers for people with mobility issues or who cannot have physical contact with peripherals. A deep learning algorithm called Yolov5s is used for object detection. the precision and recall are near to 1, the mAP (0.5) value for hand gest

关键词： Deep learning

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SequenceShield: A Robust and Accurate DDoS Detection Method via Serializing the Traffic with Direction Information 24

SequenceShield: A Robust and Accurate DDoS Detection Method ...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Deng, Zeyi Yang, Wei Xu, Minchao Du, Meijie Li, Yuzhen Zhou, Zhou Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Engineering Laboratory of Information Security Technologies Beijing China

ISBN: (纸本)9798350319934

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. Previous methods try to reveal the pattern of attacks by using artificially selected statistical features or header fields of network packets. However, they suffer from low accuracy and are not robust in the face of evasion attacks (clever attackers can change some specific header fields of the packet or slow down the packet rate to avoid being detected). To achieve robust and accurate DDoS detection, we propose a method named SequenceShield. Sequence Shield is based on our observation that the direction information of each network packet can reveal the behavior pattern of DDoS attack traffic. Specifically, we innovatively serialize the network traffic between two endpoints into a sequence structure named interaction sequence (IS) with direction information. then the converted IS is sent to a gated recurrent unit (GRU) network to learn the behavior pattern of attacks. SequenceShield only uses each packet's direction and protocol information, which makes our proposed method robust enough in the face of evasion attacks. Moreover, experimental results on two well-known datasets indicate that the classification performance of SequenceShield outperforms the state-of-the-art DDoS detection methods. © 2022 IEEE.

关键词： Denial-of-service attack

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A Continuous Gesture Segmentation and Recognition Method for human-Robot interaction 8

A Continuous Gesture Segmentation and Recognition Method for...

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2022 8th international conference on Electrical engineering, Control and Robotics, EECR 2022

作者： Fan, Jinlong Yue, Yang Wang, Yu Wan, Bei Li, Xudong Hua, Gengpai Shanghai Aerospace Control Technology Institute Shanghai China

the process of human-computer cooperation using gesture recognition can make people get rid of the limitations of traditional input devices such as mouse and keyboard, and control artificial intelligence devices more efficiently and naturally. As a new way of human-robot interaction (HRI), gesture recognition has made some progress. there are many ways to realize gesture recognition combined with visual recognition, motion information acquisition and EMG signal. the research on isolated language gesture recognition has been quite mature, but the expression semantics of isolated gestures is single. In order to improve the interaction efficiency, the application of continuous gesture recognition is essential. this paper studies its application in continuous sign language sentence recognition based on inertial sensor and rule matching recognition algorithm. the recognition rate of nine single HRI gestures is 92.7%, and the HRI of combined gestures is realized. © Published under licence by IOP Publishing Ltd.

关键词： Gesture recognition

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An Efficient Neuro-Fuzzy Classification System for Identifying Emotions

An Efficient Neuro-Fuzzy Classification System for Identifyi...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S Kavitha A Jaya Mabel Rani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

A person’s facial expressions reveal a great deal about their emotional state. the field of automated facial expression recognition holds great significance in the context of human-computer interaction (HCI). the Wavelet Transform Features serve as the foundation for the suggested facial emotion identification technique. As texture characteristics, the image’s grey-level co-occurrence parameters and discrete wavelet transform image properties were employed. the neuro-fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for classification. Validation of the suggested methodology’s performance yields encouraging results that demonstrate the recognition system’s efficacy.

关键词： human computer interaction Adaptive systems Databases Face recognition Termination of employment Aerospace electronics Feature extraction Discrete wavelet transforms Complexity theory Reliability

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