ArticleSensors (Basel, Switzerland)2020
Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Detecting Intervention-Specific Change in Soft Tissue Mobility Aligned with Regional Pain Through Optically Measured Skin Surface Strains.Annals of biomedical engineering · 2026Article
- Strain-based biomarkers at the skin surface differentiate asymmetries in soft tissue mobility associated with myofascial pain.Journal of the mechanical behavior of biomedical materials · 2025Article
- DeepLabCut custom-trained model and the refinement function for gait analysis.Scientific reports · 2025Article
- Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams-A feasibility study.Frontiers in medicine · 2025Article
- Home-Based, Low-Intensity, Gamification-Based, Interactive Physical-Cognitive Training for Older Adults Using the ADDIE Model: Design, Development, and Evaluation of User Experience.JMIR serious games · 2024Article
- Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill.Bioengineering (Basel, Switzerland) · 2024Article
- A comparison of machine learning methods for quantifying self-grooming behavior in mice.Frontiers in behavioral neuroscience · 2024Article
- Optical Measurements of the Skin Surface to Infer Bilateral Distinctions in Myofascial Tissue Stiffness.World Haptics Conference. World Haptics Conference · 2023Article
- MTGEA: A Multimodal Two-Stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment.Sensors (Basel, Switzerland) · 2023Article
- Effect of Flat Running Shoes on Hip Kinematics in Male Recreational Runners.International journal of environmental research and public health · 2022Article
- Programs Using Stimulation-Regulating Technologies to Promote Physical Activity in People With Intellectual and Multiple Disabilities: Scoping Review.JMIR rehabilitation and assistive technologies · 2022Article
- Applications and limitations of current markerless motion capture methods for clinical gait biomechanics.PeerJ · 2022Review
- Article
- Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors.Journal of neural engineering · 2021Article
- Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.Sensors (Basel, Switzerland) · 2021Article
- A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches.Frontiers in psychology · 2021Article
- A Validation of Supervised Deep Learning for Gait Analysis in the Cat.Frontiers in neuroinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon's signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.