ArticleSensors (Basel, Switzerland)2020
A Piezoelectric Tactile Sensor for Tissue Stiffness Detection with Arbitrary Contact Angle.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The T-Blep: A Soft Optical Sensor for Stiffness and Contact Force Measurement.Micromachines · 2024Article
- Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation.Sensors (Basel, Switzerland) · 2023Review
- Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring.Micromachines · 2022Article
- Static Tactile Sensing Based on Electrospun Piezoelectric Nanofiber Membrane.Sensors (Basel, Switzerland) · 2022Article
- Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation.Sensors (Basel, Switzerland) · 2021Article
- Assessing the Stiffness Perception of Acupressure Massage Beginning Learners: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
In this paper, a piezoelectric tactile sensor for detecting tissue stiffness in robot-assisted minimally invasive surgery (RMIS) is proposed. It can detect the stiffness not only when the probe is normal to the tissue surface, but also when there is a contact angle between the probe and normal direction. It solves the problem that existing sensors can only detect in the normal direction to ensure accuracy when the degree of freedom (DOF) of surgical instruments is limited. The proposed senor can distinguish samples with different stiffness and recognize lump from normal tissue effectively when the contact angle varies within [0°, 45°]. These are achieved by establishing a new detection model and sensor optimization. It deduces the influence of contact angle on stiffness detection by sensor parameters design and optimization. The detection performance of the sensor is confirmed by simulation and experiment. Five samples with different stiffness (including lump and normal samples with close stiffness) are used. Through blind recognition test in simulation, the recognition rate is 100% when the contact angle is randomly selected within 30°, 94.1% within 45°, which is 38.7% higher than the unoptimized sensor. Through blind classification test and automatic k-means clustering in experiment, the correct rate is 92% when the contact angle is randomly selected within 45°. We can get the proposed sensor can easily recognize samples with different stiffness with high accuracy which has broad application prospects in the medical field.
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Registered trials
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