# Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm

## TL;DR

This paper introduces a new algorithm to synchronize motion capture and force data without hardware triggers, enabling low-cost biomechanical analysis.

## Contribution

A novel event-based algorithm for synchronizing OpenCap motion capture with force platforms in the absence of hardware triggers.

## Key findings

- The algorithm successfully synchronized 92.5% of motion records using heel contact events.
- Validation showed strong correlation (r = 0.81–0.85) and low error (RMSE < 0.17, MAE < 0.14) compared to a gold standard system.

## Abstract

Background: The 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. Objective: To 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. Methods: Thirty 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). Results: The 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. Conclusions: The 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.