Automated Patient Positioning with Learned 3D Hand Gestures
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Gesture RecognitionHand DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow
Automated patient positioning can improve radiology workflow efficiency by reducing the time required for manual table adjustments and scout-based scan planning. We propose a learning-based framework that predicts 3D org…
Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense
In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on 'skeletal' data represented by the body joint coordinates, from the Pr…
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionInternal Organ Localization Using Depth Images
Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate this process by leveraging depth informati…
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder
Severe impairment of the central motor network can result in loss of motor function, clinically recognized as Locked-in Syndrome. Advances in Brain-Computer Interfaces offer a promising avenue for partially restoring com…
DecoderFeature EngineeringSelf-supervised 3D Patient Modeling with Multi-modal Attentive Fusion
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms. Existing CNN-based end-to-end patient modeling solutions typically require a) customize…
Keypoint Detection