Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy
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Abstract (arXiv)
3D pose estimation offers the opportunity for fast, non-invasive, and accurate motion analysis. This is of special interest also for clinical use. Currently, motion capture systems are used, as they offer robust and precise data acquisition, which is essential in the case of clinical applications. In this study, we investigate the accuracy of the state-of-the-art 3D position estimation approach MeTrabs, compared to the established inertial sensor system MTw Awinda for specific motion exercises. The study uses and provides an evaluation dataset of parallel recordings from 10 subjects during various movement therapy exercises. The information from the Awinda system and the frames for monocular pose estimation are synchronized. For the comparison, clinically relevant parameters for joint angles of ankle, knee, back, and elbow flexion-extension were estimated and evaluated using mean, median, and maximum deviation between the calculated joint angles for the different exercises, camera positions, and clothing items. The results of the analysis indicate that the mean and median deviations can be kept below 5deg for some of the studied angles. These joints could be considered for medical applications even considering the maximum deviations of 15deg. However, caution should be applied to certain particularly problematic joints. In particular, elbow flexions, which showed high maximum deviations of up to 50deg in our analysis. Furthermore, the type of exercise plays a crucial role in the reliable and safe application of the 3D position estimation method. For example, all joint angles showed a significant deterioration in performance during exercises near the ground.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 1 Jun 2023.
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7 of 25 references.
References · 7
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
- 2020Noise or Signal: The Role of Image Backgrounds in Object Recognitioncited by 343
- 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
- 2020MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimationcited by 35
- 2020A review of 3D human pose estimation algorithms for markerless motion capturecited by 24
- 2020PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimationcited by 15
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