JMIR Publications

ABSTRACT

Background:

Accurate motor assessment is crucial for diagnosing and monitoring neurological and musculoskeletal disorders. However, traditional manual and semiautomated methods can be subjective, time-consuming, or expensive. Realtime 3D Human Pose Estimation (HPE) is emerging as a lowcost, noninvasive alternative. However, its clinical adoption is limited due to a lack of thorough, standardized evaluation of its accuracy and robustness in realworld clinical settings.

Objective:

This study introduces a technical pipeline for evaluating the applicability of realtime 3D HPE in simulated clinical rehabilitation settings.

Methods:

The framework facilitates the evaluation of an HPE model's spatial and spatiotemporal accuracy, realtime capability, and robustness against simulated common setup errors and camera position variations, using a marker-based motion capture system as a gold-standard baseline. To address the technical challenge of inter-dataset comparison, the framework includes a morphing model that harmonizes differing joint definitions.

Results:

Applying the framework to an exemplary state-of-the-art algorithm (Mediapipe) demonstrated the pipeline's sensitivity to image distortions. The metrics showed that simulated underexposure and blur had negligible effects, whereas body segment occlusion and insufficient multiocular calibration significantly degraded tracking accuracy. Additionally, the framework showed significant sensitivity to specific camera placements (sagittal views), in contrast to more stable frontal views.

Conclusions:

This study establishes a technical foundation for the standardized evaluation of markerless tracking for clinical applications, like motor rehabilitation. The morphing model enables inter-dataset comparison, and the proposed framework facilitates domain-specific interpretation of HPE performance. While further validation on pathological populations is required, this work provides the necessary engineering foundation for future validation studies.