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Medical palpation is a task that traditionally requires a skilled practitioner to assess and diagnose a patient through direct touch and manipulation of their body. In regions with a shortage of such professionals, robotic hands or sensorized gloves could potentially capture the necessary haptic information during palpation exams and relay it to medical doctors for diagnosis. From an engineering perspective, a comprehensive understanding of the relevant motions and forces is essential for designing haptic technologies capable of fully capturing this information. This study focuses on thyroid examination palpation, aiming to analyze the hand motions and forces applied to the patient’s skin during the procedure. We identified key palpation techniques through video recordings and interviews and measured the force characteristics during palpation performed by both non-medical participants and medical professionals. Our findings revealed five primary palpation hand motions and characterized the multi-dimensional interaction forces involved in these motions. These insights provide critical design guidelines for developing haptic sensing and display technologies optimized for remote thyroid nodule palpation and diagnosis.
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This content will become publicly available on July 8, 2026
The Impact of Palpation Motion on Capturing Lumps in Tissue with a Force Sensor
Medical palpation is a vital diagnostic technique where practitioners assess a patient’s condition through tactile examination. Advancements in remote health technologies should emphasize supporting tactile/haptic modalities to enable some aspects of physical examination to be conducted at a distance. In thyroid examinations, differentiating nodule sizes is critical for identifying malignant lumps. This study investigates how palpation motion affects the sensing performance of single-point normal force sensors in detecting thyroid nodules. Using a phantom skin model with lumps of varied sizes and depths, force data was captured and visualized as a stiffness distribution (tactile imaging). The captured lump shapes were compared to actual shapes using Correlation Coefficient (CC), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) methods. Results showed that single-point normal force sensors effectively detect lumps, particularly during typical palpation motions such as Poke and Push & Pull, with Poke consistently yielding superior performance across various sizes and depths. However, estimating lump shapes becomes increasingly challenging as lump depth increases, regardless of the motion applied. These findings emphasize the importance of motion in optimizing singlepoint sensors for palpation and provide valuable insights for developing sensorized gloves for clinical use, particularly in remote healthcare systems.
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- Award ID(s):
- 2326453
- PAR ID:
- 10620965
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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