A Realistic Evaluation Framework and Staged Hybrid Pipeline (KNN–SVR–MICE) for Robust Missing-Data Imputation in Wearable Sensor Monitoring of Elderly Populations

Continuous health monitoring with wearable sensors frequently suffers from missing data; in geriatric populations device non-compliance, battery depletion, and physiological extremes produce non-random gaps that can distort downstream analysis. Existing methods typically assume a single missingness mechanism and apply a uniform strategy. This study contributes an integrated imputation-and-evaluation framework rather than a new algorithm. The proposed staged method couples k-nearest neighbors (KNN) and support vector regression (SVR), both trained across participants, with a within-participant iterative (MICE-style) refinement that re-estimates each feature conditionally on the others. We evaluate it on window-level features from a public benchmark of 18 older adults (HAR70+) under device-driven failure scenarios spanning MCAR, MAR, and MNAR at three nominal missing-rate settings (10/20/30%), using a leakage-free, leave-one-participant-out protocol with all inference resampled at the participant level, and against mean, KNN, SVR, MICE, missForest, and two temporal baselines. Averaged over 18 conditions and all participants, the staged KNN–SVR–MICE method achieved the lowest reconstruction error (RMSE 0.64, 95% CI [0.61, 0.67]) and the highest explained variance (R2 = 0.62), outperforming every baseline (Holm-adjusted p < 0.001). Linear interpolation was the strongest baseline and the most accurate method under purely random (MCAR) missingness, while the proposed method led under the structured MAR and MNAR scenarios. On a downstream activity-classification task the leading imputers all performed close to the complete-data reference, so the extra reconstruction accuracy did not yield a measurable downstream gain. A real-world ambulatory pilot, reusing pre-existing data, is reported as a feasibility check.