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A preliminary characterization of physical therapist visual behaviors during standing balance tasks using eye tracking

A preliminary characterization of physical therapist visual ...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Emma Nigrelli Leia Stirling Kathleen Sienko Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Industrial and Operations Engineering Robotics Department University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Visual observations provide an important foundation for physical therapist (PT) decision making in balance rehabilitation. This study used eye tracking to identify areas of the body focused on by PTs while evaluating standing balance across different exercise difficulties. Eye tracking data were obtained from five PTs who observed and rated older adults performing standing balance exercises, using a five-point scale. Excluding ratings of five, PTs had an average of approximately 215% more mean visits and an average of approximately 195% longer mean average visit durations to the lower extremities (LE) compared to the other regions of the body. However, visits to the LE shortened with increasing balance challenge, with approximately 75% shorter mean average visit durations to the LE for exercises rated as five compared to exercises rated as one. The number of visits to the head increased with balance challenge, with approximately 230% more mean number of visits to the head for ratings of five compared to ratings of one. Shortened visit durations and an increased number of visits to the upper body suggest an increase in whole body scan patterns with increasing challenge level.

关键词： Measurement visualization Head Decision making Gaze tracking Older adults Engineering in medicine and biology Extremities

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Multiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses

Multiband Group Independent Component Analysis: Unveiling Fr...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Neda Behzadfar Armin Iraji Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Group independent component analysis (ICA) offers a method for decomposing fMRI data from multiple subjects into spatially independent maps and associated time courses. Traditionally, group ICA is applied to full-band fMRI data, with typically sampling rates between 0.25-1 Hz. In this paper, we introduce a novel approach known as "multiband group ICA." This method involves the application of bandpass filters to segment the fMRI data into distinct subbands, followed by the application of blind group ICA to all subbands of multisubject fMRI data, followed by back-reconstruction of individual subband *** assess the feasibility and efficacy of our method, we utilized a bandpass filter to divide the fMRI data into two specific subbands: low frequency and high frequency. Our results not only showcase a substantial distinction in spatial maps and time courses for task-related components but also provide noteworthy insights into network-specific functional network connectivity (FNC) patterns, particularly within the visual network. Notably, motor regions exhibit predominant power in the lower frequency range, while visual regions span both low and high frequencies. Additionally, certain regions, such as the bilateral insular regions, display exclusive engagement in the high-frequency domain. Furthermore, low-frequency task-related components exhibit anticorrelation, contrasting with the strong correlation observed in high-frequency task-related components. This approach enhances our understanding of the frequency-dependent dynamics of functional connectivity in group-level fMRI analyses, offering valuable insights for future studies.

关键词： Band-pass filters visualization Correlation Independent component analysis Functional magnetic resonance imaging Motors Frequency conversion High frequency Engineering in medicine and biology

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Bibliometric Analysis and visualization of Machine Learning-Based Credit Card Fraud Detection

Bibliometric Analysis and Visualization of Machine Learning-...

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Information Technology Research and Innovation (ICITRI), International conference on

作者： Suhartono Syahiduz Zaman Totok Chamidy Department of information engineering UIN Maulana Malik Ibrahim Malang Malang Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Machine learning is often used in credit card fraud detection. Its ability to analyze large amounts of transaction data and identify patterns of fraudulent activity. The aim of this study is to provide insights into the research status, mapping process and annual topics of machine learning research on credit card fraud detection, and to provide relevant references for future research. Research phases with three approaches. The first approach is descriptive statistical analysis for data collection using Scopus web and Herzing’s Publish or Perish. The second approach uses quantitative methods for citation analysis with a bibliometric approach using Vos Viewer application. The research findings are related to the analysis of credit card fraud research divided into three clusters, the first cluster is the research stream using machine learning in data mining, the second cluster is the research stream using machine learning to detect credit card fraud, the third cluster is the research stream using machine learning to classify credit card fraud. The second cluster can resolve the complexity of credit card transaction data and increase the accuracy of the fraud detection system, for the third cluster, we can build a more effective classification in resolving imbalanced data and limited transaction records. A growing research trend is in the third cluster, research related to the performance of credit card fraud classification based on unbalanced data and limited fraud data. Further research is recommended to examine the evolution of the literature on the use of machine learning to detect credit card fraud and to conduct comparative studies between the Scopus, Web of science and scienceDirect databases to expand the literature.

关键词： Support vector machines Technological innovation Statistical analysis Keyword search Machine learning Credit cards Market research Fraud data mining Information technology

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Visual-LIDAR SLAM Based on Supervised Hierarchical Deep Neural Networks

Visual-LIDAR SLAM Based on Supervised Hierarchical Deep Neur...

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Youth Academic annual conference of Chinese Association of Automation (YAC)

作者： Yi An Zhuo Sun Chao Zhang Haifeng Yue Yan Zhi Hongliang Xu School of Control Science and Engineering Dalian University of Technology Dalian China School of Electrical Engineering Xinjiang University Urumqi China Ministry of Water Resources Information Center Beijing China State Key Laboratory of Intelligent Mining Equipment Technology Taiyuan China Shanxi TZCO Intelligent Mining Equipment Technology Co. Ltd Taiyuan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Deep learning has become increasingly crucial in simultaneous localization and mapping (SLAM). Supervised deep learning SLAM methods need ground truth data for training but achieve highly accurate predictions. Such training techniques are used by some visual or LiDAR SLAM methods. However, these methods mostly rely on single-sensor data, missing the combined advantages of LiDAR and visual information. Multisensor fusion SLAM merges diverse data types, enriching features and boosting performance. This paper presents a new deep visual-LiDAR SLAM method that integrates both visual and LiDAR data. The SLAM system includes a deep visual-LiDAR odometry module, a deep learning-based loop closure detection module, and a 3D mapping module. The deep visual-LiDAR odometry module uses a hierarchical feature encoding module to capture features from two frames of color point clouds at various levels. An attention feature decoding module employs an attention mechanism to merge these features, determining their relative pose. The iterative pose optimization module continuously refines the pose accuracy, improving robustness to outliers. The loop closure detection module uses a deep learning-based global descriptor for precise positional matching. The 3D mapping module constructs the environmental map. Experimental results on the KITTI odometry dataset show that our method outperforms other supervised and traditional geometric methods, with lower rotation and translation errors. By integrating visual and LiDAR data, our SLAM system achieves higher accuracy and robustness in pose estimation compared to single-modal SLAM systems.

关键词： Point cloud compression Training visualization Simultaneous localization and mapping Three-dimensional displays Laser radar Accuracy

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Motor Imagery Decoding from EEG under Visual Distraction via Feature Map Attention EEGNet

Motor Imagery Decoding from EEG under Visual Distraction via...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yiting Geng Banghua Yang Sixiong Ke Liang Chang Jiayang Zhang Yanyan Zheng School of Mechatronic Engineering and Automation Research Center of Brain Computer Engineering Shanghai University Shanghai China School of Electronics and Electrical Engineering University of Leeds Wenzhou People’s Hospital

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The investigation of motor imagery (MI)-based brain-computer interface (BCI) is vital to the domains of human-computer interaction and rehabilitation. Few existing studies on electroencephalogram(EEG) signals decoding based on MI consider any distractions. However, it is difficult for users to do a single MI task in real life, which is especially affected by visual distraction. In this paper, we aim to investigate the effects of visual distraction on MI decoding performance. We first design a noval MI paradigm under visual distraction and observe distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) in MI under visual distraction. Then, we propose a robust decoding method of MI under visual distraction from EEG signals by using the feature map attention EEGNet (named FMA-EEGNet) and use EEG data under conditions without and with distraction to compare the decoding performance of five methods (including the proposed method and other methods). The results demonstrate that FMA-EEGNet achieved mean accuracy of 89.1% and 82.2% without and with visual distraction, respectively, indicating superior performance compared to other methods while exhibiting minimal degradation in performance. This work contributes significantly to the advancement of practical applications in MI-BCI technology.

关键词： Human computer interaction Degradation visualization Accuracy Motors Electroencephalography Robustness Decoding Synchronization Engineering in medicine and biology

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Gaze intersection points reveal the onset of visual processing during eye and head movement

Gaze intersection points reveal the onset of visual processi...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Weichen Liu Cory Stevenson Chi-Yuan Chang Steven Thurman Russell Cohen-Hoffing Tzyy-Ping Jung Ying Choon Wu Dept. of Computer Science & Eng. UC San Diego San Diego CA USA Inst. for Neural Computation UC San Diego San Diego CA USA Beth Israel Deaconess Medical Center Harvard University Boston MA USA US DEVCOM Army Research Laboratory Aberdeen MD USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study explored the dynamics of steady-state visual evoked potentials (SSVEPs) amid unrestricted head and eye movements using immersive virtual reality (VR) to concurrently record eye and head movement data with a 64-channel electroencephalogram (EEG) during a visual fixation task. Employing a dual-condition approach—eye-shifting and head-turning—the trials were time-locked to stimulus onset, Gaze Intersection Point (GIP) onset, and fixation onset. Eye and head velocity, Canonical Correlation Analysis (CCA) correlation, and Inter-Trial Coherence (ITC) were computed across the trial duration. SSVEP signal detection (reflected in CCA scores) was most closely aligned with the GIP onset. The CCA score increase before fixation suggests that visual processing is not fully suppressed during simultaneous head and eye movements. These findings support the potential of SSVEP for tracking the time course of visual processing in naturalistic settings where research participants are free to move their heads and eyes as they explore their three-dimensional environment.

关键词： visualization Electric potential Head Correlation Tracking Virtual reality Magnetic heads Steady-state Engineering in medicine and biology Signal detection

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Machine Learning (ML) based Electricity Price Prediction with visualization

Machine Learning (ML) based Electricity Price Prediction wit...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： Abisha D Prasanna Kumar D Ida Christy J Navedha Evanjalin R Rajesh P Mohamed Haris K Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model’s performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency.

关键词： Training Costs Accuracy Electricity Prediction algorithms data models Numerical models Wind forecasting Random forests Business

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Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning

Precision Enhancement in Sustained Visual Attention Training...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Maryam Norouzi Mohammad Zaeri Amirani Yalda Shahriari Reza Abiri Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant’s attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.

关键词： Support vector machines Wireless communication Wavelet transforms visualization Accuracy Feature extraction Brain modeling Electroencephalography Faces Classification tree analysis

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Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach

Sleep structure discriminates patients with isolated REM sle...

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annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Simon Feuerstein Ambra Stefani Raphael Angerbauer Kristin Egger Abubaker Ibrahim Evi Holzknecht Birgit Högl Antonio Rodríguez-Sánchez Matteo Cesari Department of Neurology Medical University of Innsbruck Innsbruck Austria Department of Computer Science University of Innsbruck Innsbruck Austria

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Rapid eye movement (REM) sleep behavior disorder (RBD) is a disorder characterized by increased muscle tone and dream-enactment behaviors in REM sleep. In its isolated form (iRBD), it is a prodromal stage of neurodegenerative diseases. Currently, diagnosis of RBD requires time-consuming and subjective visual inspection of polysomnography (PSG). We propose a novel fast and objective deep learning model to identify patients with iRBD based on their sleep structure. A total of 86 iRBD and 81 controls, who underwent PSG, were included in the study. A validated algorithm was used to generate hypnodensity graphs (i.e., probabilistic representations of sleep structure). A ResNet-18 model was trained on five datasets consisting of whole night hypnodensities (with and without augmentation), and shorter segments (4 hours, 2 hours, and 30 minutes) to discriminate iRBD from controls. Using entire-night hypnodensity had notable benefits in terms of performance compared to shorter length segments, leading to a mean macro F1 score of 0.717 (per-segment), and of 0.784 (per-subject). Our findings show that sleep structure is important for iRBD classification and could potentially help clinicians.

关键词： Deep learning Measurement visualization Accuracy Muscles Probabilistic logic data models Rapid eye movement sleep Object recognition Wearable devices

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Inertial Strengthened CLIP model for Zero-shot Multimodal Egocentric Activity Recognition

Inertial Strengthened CLIP model for Zero-shot Multimodal Eg...

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Asia-Pacific Signal and Information Processing Association annual Summit and conference (APSIPA)

作者： Mingzhou He Haojie Wang Shuchang Zhou Qingbo Wu King Ngi Ngan Fanman Meng Hongliang Li School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The popular CLIP model has empowered various zero-shot learning tasks by unifying them into the vision-language alignment framework. However, due to the dynamic subject-object interactions and complex motion variations, the egocentric videos become more diverse and widen the gap between the vision and language data, which limits the applicability of the CLIP model. Thanks to the widely used Inertial Measurement Unit (IMU) in wearable devices, this paper proposed an Inertial Strengthened CLIP (IS-CLIP) model to refine the visual representation of egocentric videos, which achieves highly effective zeroshot multimodal egocentric activity recognition. Our IS-CLIP is composed of two modules, i.e., the Subject-object Interaction Refinement Module (SIRM) and the Subject-motion Guided Aggregation Module (SGAM). On the one hand, SIRM embeds the motion patterns of IMU into the visual representation of each frame to enhance the action-related features. On the other hand, the SGAM maps the IMU to the frame-wise weights to aggregate the visual representations of all frames, which reduces the differences between the videos of the same action with different speeds, directions, and temporal locations. Experiments on the UESTC-MMEA-CL dataset show that the proposed IS-CLIP outperforms many state-of-the-art methods in the zero-shot multimodal egocentric activity recognition task.

关键词： Hands visualization Measurement units Zero shot learning Dynamics Inertial navigation Information processing Activity recognition Wearable devices Videos

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