Connected and Automated Vehicles (CAVs) rely on high-dimensional, multimodal sensor data and Vehicle-to-Everything (V2X) communication to support autonomous driving functions. This strong dependence exposes CAV systems to anomalies arising from sensor faults, environmental disturbances, and coordinated cyber–physical attacks. To address these challenges, this paper proposes a real-time hybrid anomaly detection framework that integrates multiple domain-specific models. The framework combines an LSTM-based time-domain model for sequential behavior analysis, an FFT-based frequency-domain model for spectral anomaly detection, and a context-aware multilayer perceptron (MLP) for incorporating environmental factors. To overcome data scarcity and improve robustness, synthetic anomalies are generated and integrated with benchmark datasets, including NGSIM, CAN-ID, and nuScenes. A learned fusion layer aggregates the outputs of the individual models into a unified anomaly score, while an online learning module dynamically adapts detection thresholds using sliding-window percentile recalibration. In addition, a Deep Q-Network (DQN)–based reinforcement learning agent supports adaptive decision-making under changing conditions. Model interpretability is enhanced through SHAP-based explanations and Human-in-the-Loop feedback, enabling continuous refinement of the detection process. Experimental results show that the proposed system achieves an accuracy of 98.10% before adaptation and improves to 98.91% after online learning and feedback. These results demonstrate that the proposed framework offers strong robustness, adaptability, and real-time suitability for safety-critical CAV environments.
Accurate detection of motion anomalies during physical rehabilitation is important for patient safety and recovery monitoring. Traditional assessment methods rely on visual observation. This often leads to subjective and inconsistent evaluations. To address this limitation, we propose a Support Vector-Guided Decomposition (SVGD) framework for semi-supervised rehabilitation-oriented motion anomaly detection using wearable sensor data. The framework integrates low-rank and sparse matrix decomposition with margin-based discriminative learning. Multi-sensor motion signals are processed through sequential layers of preprocessing, feature extraction, decomposition, and anomaly scoring. This design enables separation of structured rehabilitation-like movements from irregular deviations and compensatory behaviors. The framework was evaluated using structured rehabilitation-like motion data with controlled perturbation-based anomaly generation under the Leave-One-Subject-Out (LOSO) validation protocol. This provides a systematic proxy evaluation in the absence of clinically annotated rehabilitation datasets. The proposed approach achieves strong performance, with a Precision of 0.97, Recall of 0.96, F1-score of 0.965, and AUC of 0.98. These results outperform both classical and deep learning baselines. The findings demonstrate that SVGD is robust, interpretable, and computationally efficient. The framework performs well even with limited labeled data. These results highlight the potential of SVGD for rehabilitation-oriented motion anomaly detection using wearable sensor data. Validation on clinically annotated rehabilitation datasets will be considered in future work.
Kumar Dorthi, Kiran Kumar Mamidi, Ravi Kanth Kotha et al.· Scientific Reports· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
