radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction

The paper presents a clear problem statement, a logical progression of proposed solutions (deconstruction into tasks, MTL, EGA), and connects the methodology steps directly to the research objectives. The evaluation aligns with the claims made about noise robustness and task balancing.

Strengths: Clearly defines the three deconstructed tasks and their objectives., Provides a detailed mathematical formulation for the proposed EGA strategy., Evaluates performance using a comprehensive set of relevant metrics (RMSE, PCC, R2, PPI Error, Timing Error, MDR, Δm%)., Compares against multiple state-of-the-art deep learning frameworks and MTL optimization strategies., Includes dedicated noise robustness tests with different noise types, intensities, and durations., Performs statistical analysis (T-test) to confirm the significance of results., Analyzes computational complexity.
Weaknesses: Relies solely on an existing public dataset; no new experimental data collected specifically for this study's broader scope (e.g., RBM)., Noise robustness tested on test data only, not incorporated into training via data augmentation (authors argue against this for fair comparison, but it's a common practice)., The basis for segmenting the radar signal into 4-sec segments is stated, but sensitivity to this parameter is not explored.

The paper provides extensive experimental results across multiple tasks, noise conditions, and comparison methods. Tables and figures clearly support the claims of superior performance and noise robustness for radarODE-MTL with EGA. Statistical analysis confirms the significance of the findings.

The application of the MTL paradigm to radar-based ECG recovery is claimed as novel. The proposed Eccentric Gradient Alignment (EGA) optimization strategy is presented as a novel contribution to MTL optimization. The explicit focus on noise robustness for radar-based ECG using this framework is also a novel contribution compared to existing literature.

The ability to reconstruct accurate and robust ECG signals from radar under realistic noise conditions, especially RBM, has significant practical value for contactless long-term cardiac monitoring and healthcare applications. The proposed EGA strategy could potentially be applied to other MTL problems.

Strengths: Formal and precise academic language is used., Technical concepts are explained clearly., Mathematical formulations are provided for key methods (EGA)., Results are presented clearly in tables and figures., Logical flow between sections.
Areas for Improvement: Some complex sentences require re-reading., Minor grammatical inconsistencies or awkward phrasing in places (e.g., "deconstructs the radar-based ECG recovery into three individual tasks", "forcible change").