ArticleSensors (Basel, Switzerland)2021
HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
- Gait Analysis Using an Artificial Intelligence-Based Motion Capture System With a Single Smartphone Camera.Cureus · 2025Article
- Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait.Sensors (Basel, Switzerland) · 2024Article
- Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.Sensors (Basel, Switzerland) · 2022Article
- Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context.International journal of environmental research and public health · 2022Article
- Computer Vision for 3D Perception and Applications.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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Abstract
With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)-a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test-an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO's evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (
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