In real-time biosignal streams, anomalies and missing values frequently degrade data quality and compromise downstream learning performance. This paper presents a unified restoration framework that integrates Robust Random Cut Forest for online anomaly detection with a sliding-window Real-Time High-Accuracy Low-Rank Tensor Completion algorithm, hereafter referred to as RTHaLRTC, for efficient recovery. The RRCF module identifies and masks anomalous entries without relying on distributional assumptions, while the proposed completion algorithm leverages prior window estimates and dual variable smoothing to reduce latency and improve convergence. Experiments on electromyographic signals from a Myo armband demonstrate that the proposed pipeline improves classification accuracy by 5–12% compared to directly using anomalous data, particularly under severe muscle tremor and sensor noise. RTHaLRTC achieves up to a 10 \(\times \) reduction in completion time compared to standard HaLRTC, while maintaining comparable reconstruction accuracy (RMSE differs by less than 5%). These results confirm the framework’s suitability for real-time biosignal applications such as prosthetic control and online gesture recognition.