# Recovering Biomechanical Signals from Missing Keypoints Using Temporal Interpolation in Monocular Gait Analysis

arXiv: https://arxiv.org/abs/2609.09670 · HTML: https://mlsanity.com/paper/2609.09670/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework](https://mlsanity.com/paper/2203.04113/) (2022)
- [Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix](https://mlsanity.com/paper/1807.11147/) (2018)

## Abstract (arXiv)

Monocular pose estimation enables low-cost gait analysis but is sensitive to missing keypoints caused by occlusion, detection errors, or efficiency-driven model reduction. While prior work on recovering missing joints focuses on complex learned models, the effectiveness of simple temporal methods remains underexplored. We evaluate knee-angle estimation under a missing-ankle-keypoint condition and test a first-order temporal interpolation scheme as a recovery mechanism. Across 527 frames of monocular walking video (428 with valid baseline detections), removing the ankle keypoint increased mean angular error to 23.4deg +/- 46.7deg and collapsed signal variance to near zero. Temporal interpolation reduced error to 1.1deg +/- 6.7deg and restored variance and smoothness to within a few percent of baseline. These results indicate that gait signals possess sufficient temporal redundancy for a simple, computationally trivial interpolation scheme to recover a critical missing joint, without resorting to learned reconstruction models. The findings support low-complexity, real-time-compatible designs for gait analysis in resource-constrained or occlusion-prone monocular settings.