Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin

• URL: http://arxiv.org/abs/2502.03396v1
• Date: Wed, 05 Feb 2025 17:32:07 GMT
• Title: Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin
• Authors: Sarah Al-Shareeda, Yasar Celik, Bilge Bilgili, Ahmed Al-Dubai, Berk Canberk,
• Abstract summary: This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework.<n>Through various testing scenarios, we visually demonstrate the efficacy of our methodology and our key role in significantly reducing the witnessed gap within the HITS's DT.
• Score: 3.906021256484084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.

Related papers

• Time-to-Event Transformer to Capture Timing Attention of Events in EHR Time Series [15.049813932448112]
  LITT is a novel Timing-Transformer architecture that enables temporary alignment of sequential events on a virtual relative timeline''<n>Its interpretability and effectiveness are validated on real-world longitudinal EHR data from 3,276 breast cancer patients.
  arXiv  Detail & Related papers  (2026-02-11T00:13:08Z)
• ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection [0.0]
  This study introduces ConvMambaNet, a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with the Mamba Structured State Space Model (SSM) to enhance temporal feature extraction.<n> evaluated on the CHB-MIT Scalp EEG dataset, ConvMambaNet achieved a 99% accuracy and demonstrated robust performance under severe class imbalance.
  arXiv  Detail & Related papers  (2026-01-19T17:08:34Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests [14.37149160708975]
  This paper proposes a novel hybrid framework combiningHierarchical Temporal Memory (HTM) and Sequential Probability Ratio Test (SPRT) for real-time data drift detection and anomaly identification. Experimental evaluations demonstrate that the proposed method outperforms conventional drift detection techniques like the Kolmogorov-Smirnov (KS) test, Wasserstein distance, and Population Stability Index (PSI) in terms of accuracy, adaptability, and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-24T18:23:18Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner [46.866240648471894]
  Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. We present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales.
  arXiv  Detail & Related papers  (2024-05-06T06:23:06Z)
• DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy [5.8722774441994074]
  We propose a Depth-based Dual-Loop framework for real-time Visually Navigated Bronchoscopy (DD-VNB) The DD-VNB framework integrates two key modules: depth estimation and dual-loop localization. Experiments on phantom and in-vivo data from patients demonstrate the effectiveness of our framework.
  arXiv  Detail & Related papers  (2024-03-04T02:29:02Z)
• Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network [9.349668170221975]
  We develop an unsupervised deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline.
  arXiv  Detail & Related papers  (2022-06-13T17:38:16Z)
• Edge Continual Learning for Dynamic Digital Twins over Wireless Networks [68.65520952712914]
  Digital twins (DTs) constitute a critical link between the real-world and the metaverse. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin and its corresponding cyber twin. The proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-04-10T23:25:37Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications [2.9327503320877457]
  Out-of-distribution (OD) detection is an emerging approach to address the challenge of detecting out-of-distribution in real-time. In this paper, we show how we can robustly detect hazardous motion around autonomous driving agents. Our methods significantly improve detection capabilities of OoD factors to unique driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented.
  arXiv  Detail & Related papers  (2021-07-25T07:52:53Z)
• Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space [43.280123606888395]
  We propose a hyperbolic temporal graph network (HTGN) that takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. HTGN maps the temporal graph into hyperbolic space, and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network. Experimental results on multiple real-world datasets demonstrate the superiority of HTGN for temporal graph embedding.
  arXiv  Detail & Related papers  (2021-07-08T11:24:59Z)
• Spatiotemporal convolutional network for time-series prediction and causal inference [21.895413699349966]
  A neural network computing framework, i.N.N., was developed to efficiently and accurately render a multistep-ahead prediction of a time series. The framework has great potential in practical applications in artificial intelligence (AI) or machine learning fields.
  arXiv  Detail & Related papers  (2021-07-03T06:20:43Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.