ReviewHeliyon2024
Deep learning-based human body pose estimation in providing feedback for physical movement: A review.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Sparse frame selection for graph-based deadlift form assessment.Scientific reports · 2026Article
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Fine reconstruction of badminton swing dynamic trajectory assisted by event camera.Scientific reports · 2026Article
- Evaluation of Smartphone Camera Positioning on Artificial Intelligence Pose Estimation Accuracy for Exercise Detection: Observational Study.JMIR mHealth and uHealth · 2026Observational
- Measuring Assistive Technology Outcomes via AI-Based Kinematic Modeling of Individualized Routine Learning in Elite Boccia Athletes with Severe Cerebral Palsy: A Longitudinal Case Series.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.Journal of orthopaedic surgery and research · 2026Review
- Development and implementation of a MediaPipe-based AI teaching-learning model in school physical education for health promotion.Frontiers in public health · 2026Article
- Effects of AI-assisted wushu training on mental health, cognitive performance, and cardiovascular function in university students: a quasi-experimental study.Frontiers in public health · 2026Article
- A real time action scoring system for movement analysis and feedback in physical therapy using human pose estimation.Scientific reports · 2025Article
- Obesity care in Chinese adults: from evidence to clinical practice.Precision clinical medicine · 2025Review
- Camera-based mobile applications for movement screening in healthy adults: a systematic review.Frontiers in sports and active living · 2025Review
- Robust skeletal motion tracking using temporal and spatial synchronization of two video streams.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pose estimation has various applications in analyzing human body movement and behavior, including providing feedback to users about their movements so they can adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human 'body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 45 articles for methods used to estimate human body pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that pose estimation libraries play an important role in the advancement of this field. Nevertheless, the effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.
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.