# Automated Patient Positioning with Learned 3D Hand Gestures

arXiv: https://arxiv.org/abs/2407.14903 · HTML: https://mlsanity.com/paper/2407.14903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [3D Hand Shape and Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1903.00812/) (2019)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation](https://mlsanity.com/paper/1808.08180/) (2018)
- [HaGRID - HAnd Gesture Recognition Image Dataset](https://mlsanity.com/paper/2206.08219/) (2022)
- [Contrastive Representation Learning for Hand Shape Estimation](https://mlsanity.com/paper/2106.04324/) (2021)
- [ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2208.11547/) (2022)
- [Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation](https://mlsanity.com/paper/1907.02161/) (2019)
- [Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images](https://mlsanity.com/paper/2007.08340/) (2020)
- [On-device Real-time Hand Gesture Recognition](https://mlsanity.com/paper/2111.00038/) (2021)
- [Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion](https://mlsanity.com/paper/2403.03217/) (2024)
- [PBADet: A One-Stage Anchor-Free Approach for Part-Body Association](https://mlsanity.com/paper/2402.07814/) (2024)

## Abstract (arXiv)

Positioning patients for scanning and interventional procedures is a critical task that requires high precision and accuracy. The conventional workflow involves manually adjusting the patient support to align the center of the target body part with the laser projector or other guiding devices. This process is not only time-consuming but also prone to inaccuracies. In this work, we propose an automated patient positioning system that utilizes a camera to detect specific hand gestures from technicians, allowing users to indicate the target patient region to the system and initiate automated positioning. Our approach relies on a novel multi-stage pipeline to recognize and interpret the technicians' gestures, translating them into precise motions of medical devices. We evaluate our proposed pipeline during actual MRI scanning procedures, using RGB-Depth cameras to capture the process. Results show that our system achieves accurate and precise patient positioning with minimal technician intervention. Furthermore, we validate our method on HaGRID, a large-scale hand gesture dataset, demonstrating its effectiveness in hand detection and gesture recognition.