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Updated: Aug 27, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

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Three-Dimensional Foot Position Estimation Based on Footprint Shadow Image Processing and Deep Learning for Smart

Se-Kyung Park¹, Jun-Kyu Park², Hong-In Won³

¹Ansan R&D Campus, LG Innotek, Ansan 15588, Korea.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This study introduces a deep learning system to track foot position on a smart trampoline, enhancing home fitness and rehabilitation. The novel approach accurately estimates 3D foot movement without calibration, improving exercise evaluation.

Keywords:

3D foot contact position estimation deep learning footprint shadow image processing smart fitness trampoline wide-angle camera

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Last Updated: Aug 27, 2025

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Area of Science:

Biomedical Engineering
Computer Vision
Sports Technology

Background:

The digital fitness market is rapidly growing, with smart home exercise equipment like trampolines gaining traction.
Accurate user motion recognition is crucial for effective self-guided exercise systems in smart fitness equipment.
Previous methods for motion tracking faced challenges with calibration and environmental variations.

Purpose of the Study:

To develop a deep learning-based system for estimating the 3D foot positions using footprint shadow images from a smart trampoline.
To overcome limitations of prior approaches, such as geometric calibration and sensitivity to illumination changes.

Main Methods:

A smart trampoline system with an upward-looking wide-angle camera and an embedded processing board was utilized.
A modified Fast-RCNN network based on ResNet-50 was employed for end-to-end deep learning without calibration.
The region proposal network was adapted for location regression, distinct from bounding box regression.

Main Results:

The proposed deep learning algorithm achieved high accuracy in estimating 3D foot positions.
Root mean square errors for X, Y, and Z directions were 8.32 mm, 15.14 mm, and 4.05 mm, respectively.
The system demonstrated effectiveness in motion recognition and performance evaluation for jumping exercises.

Conclusions:

The developed deep learning system accurately estimates 3D foot positions from footprint shadows on a smart trampoline.
This technology enables enhanced motion recognition and performance evaluation for home-based fitness and rehabilitation exercises.
The calibration-free, end-to-end learning approach offers a robust solution for smart fitness equipment.