ArticleSensors (Basel, Switzerland)2026
Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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
1 citing paper 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
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
backgroundThe integration of markerless motion capture systems such as OpenCap with force platforms expands the possibilities of biomechanical analysis in low-cost environments; however, it requires robust temporal synchronization procedures in the absence of shared hardware triggers.
objectiveTo develop and validate an automatic synchronization algorithm based on heel kinematic events to align OpenCap data with force platform signals during lower-limb functional exercises.
methodsThirty normal-weight adult women (18-45 years) were evaluated while performing between 11 and 14 functional tasks (60° and 90° squats, lunges, sliding variations, and step exercises), yielding 330 motion records. Kinematics were estimated using OpenCap (four iPhone 12 cameras at 60 Hz), and kinetics were recorded using BTS P6000 force platforms synchronized with an OptiTrack system (Gold Standard). The algorithm detected heel contact from the filtered vertical coordinate and aligned this event with the initial rise in vertical ground reaction force. Validation against the Gold Standard was performed in 20 squat repetitions (10 at 60° and 10 at 90°) using Pearson correlation, RMSE, and MAE of the time-normalized and amplitude-normalized (0-1) vertical ground reaction force (vGRF).
resultsThe algorithm successfully synchronized 92.5% of the 330 records; the remaining cases showed kinematic noise or additional steps that prevented robust event detection. During validation, correlations were r = 0.85 (60°) and r = 0.81 (90°), with Root Mean Square Error (RMSE) < 0.17 and Mean Absolute Error (MAE) < 0.14, values representing less than 0.1% of the peak force.
conclusionsThe heel-contact-based algorithm allows accurate synchronization of OpenCap and force platform signals during lower-limb functional exercises, achieving performance comparable to hardware-synchronized systems. This approach facilitates the integration of markerless motion capture in clinical, sports, and occupational settings where advanced dynamic analysis is required with limited infrastructure.
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.