ReviewPhysical therapy research2025
Markerless Motion Analysis Using New Digital Technology.
Review in Physical therapy research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validations and applications of markerless motion capture using OpenCap: a scoping review.Frontiers in digital health · 2026Pooled it
- Development and Clinical Application of Digital Biomarkers for Sarcopenia.Journal of bone metabolism · 2026Review
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- AI-Based Gait Analysis System for Rehabilitation: Usability Evaluation of Human-Technology Interaction.JMIR rehabilitation and assistive technologies · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motion analysis is essential for physical therapists and athletic trainers to understand the motor function of their patients or athletes. Although marker-based motion analysis systems have been widely utilized in research, they are expensive and demand significant time and effort for measurement and analysis, which can complicate their application in clinical practice. In recent years, markerless motion analysis technologies have emerged as affordable and portable alternatives. These technologies include inertial measurement unit (IMU) sensors, depth cameras, manual digitization, and posture-tracking algorithms. IMU sensors detect motion using accelerometers and gyro sensors and can be worn on body parts. Depth cameras use infrared or laser technology to capture three-dimensional (3D) motion without requiring markers. Manual digitization enables semiautomatic identification of joint positions from images, allowing joint angle measurement without using specific cameras or markers. Posture-tracking algorithms use artificial intelligence to approximate joint positions from standard camera images, enabling automated motion analysis. Despite the enhanced accessibility of these technologies, limitations remain, particularly in analyzing detailed joint movements or individuals with structural abnormalities, and their accuracy depends on the environment and motion task. However, with further development, these technologies are expected to become increasingly reliable and provide physical therapists and athletic trainers with valuable, cost-effective, and easy-to-use tools for assessing movement in clinical and sports settings.
Indexed as
Identifiers
What Socratic 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 Socratic graph.