# Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone

arXiv: https://arxiv.org/abs/2302.10749 · HTML: https://mlsanity.com/paper/2302.10749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Gravity as a Reference for Estimating a Person's Height from Video](https://mlsanity.com/paper/1909.02211/) (2019)
- [Gait-based Age Group Classification with Adaptive Graph Neural Network](https://mlsanity.com/paper/2210.00294/) (2022)

## Abstract (arXiv)

Goal: The countermovement jump (CMJ) is commonly used to measure lower-body explosive power. This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height. Methods: First, three repetitions each of bilateral and unilateral CMJ were performed by sixteen healthy adults (mean age: 30.87±7.24 years; mean BMI: 23.14±2.55 kg/m2) on force plates and simultaneously captured using optical motion capture (OMC) and one smartphone camera. Next, MMC was performed on the smartphone videos using OpenPose. Then, we evaluated MMC in quantifying jump height using the force plate and OMC as ground truths. Results: MMC quantifies jump heights with ICC between 0.84 and 0.99 without manual segmentation and camera calibration. Conclusions: Our results suggest that using a single smartphone for markerless motion capture is promising. Index Terms - Countermovement jump, Markerless motion capture, Optical motion capture, Jump height. Impact Statement - Countermovement jump height can be accurately quantified using markerless motion capture with a single smartphone, with a simple setup that requires neither camera calibration nor manual segmentation.