A Two-Dimensional Microfluidic-Based Tactile Sensor for Tissue Palpation Under the Influence of Misalignment

2015 ◽  
Author(s):  
Yichao Yang ◽  
Jiayue Shen ◽  
Zhili Hao
Keyword(s):  
Contact Point ◽  
Tactile Sensor ◽  
Two Dimensional ◽  
Distributed Sensors ◽  
Normal Contact ◽  
Transducer Array ◽  
Tissue Phantoms ◽  
Tissue Region ◽  
Manual Palpation ◽  
Resistive Transducer

This paper reports on a proof-of-concept study of applying a two-dimensional (2D) microfluidic-based tactile sensor for tissue palpation under the influence of misalignment. Two unavoidable misalignment issues, uncertainty in contact point and non-ideal normal contact, severely distort the genuine elasticity distribution of a tissue region, yielding false identification of abnormality. The core of the 2D tactile sensor is one whole microstructure embedded with an electrolyte-enabled 2D resistive transducer array underneath. This unique configuration allows the tactile sensor to interact with a tissue region in a continuous manner that mimics manual palpation: the whole microstructure (fingertip) presses a tissue region and the corresponding deflection distribution is captured concurrently by the embedded transducer array (distributed sensors under the skin). This continuous manner tackles the misalignment issues encountered by an individual sensor or a sensor array, in that any misalignment encountered by the 2D sensor is manifested as an increasing trend of the distributed deflection-depth relations along the tilt direction. Tissue phantoms with embedded nodules and extrusions are prepared and are measured using the 2D tactile sensor, validating the capability of the tactile sensor to identify abnormalities in soft tissue under the influence of misalignment.

A Novel Method to Search for the Wheel–Rail Contact Point

2019 ◽  
Vol 19 (11) ◽  
pp. 1950142 ◽  
Author(s):  
Hanyun Liu ◽  
Zhiwu Yu ◽  
Wei Guo ◽  
Lizhong Jiang ◽  
Chongjie Kang
Keyword(s):  
Minimum Distance ◽  
Lateral Displacement ◽  
Contact Point ◽  
Normal Contact ◽  
Novel Method ◽  
Maximum Penetration ◽  
Vertical Contact ◽  
Judgment Condition ◽  
Searching Method ◽  
Root Contact

This paper proposed the normal contact searching method (NCSM), a novel method to search for the wheel–rail contact point, which utilizes the normal maximum penetration distance between wheel and rail as the judgment condition. The contact point found by the NCSM can better represent the center of the wheel–rail contact patch which is considered more reasonable than the commonly used vertical contact searching method (VCSM), the latter adopts the vertical minimum distance to determine the wheel–rail contact point. The differences between these two methods are analyzed and compared for the same contact point situation and with same motion parameters. The results show that, for the Chinese LMA wheelset and CHN60 rail profiles, these two methods have slight differences for the same contact point situation. For a wheelset with a lateral displacement less than 7.0[Formula: see text]mm and with no yawing, the NCSM’s contact point is very close to VCSM’s, so both methods are suitable for the dynamic calculation. For a wheelset with a lateral displacement greater than 7.0[Formula: see text]mm or with yawing, an unreasonable jump occurs at the wheel–rail contact point and wheelset angle root contact by applying VCSM, while the NCSM has only small discrete jump on the wheelset tread contact. In this case, the NCSM instead of VCSM should be used in the dynamic analysis.

scholarly journals Magnetostrictive tactile sensor of detecting friction and normal force for object recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093232
Author(s):  
Bing Zhang ◽  
Bowen Wang ◽  
Yunkai Li ◽  
Shaowei Jin
Keyword(s):  
Recognition Rate ◽  
Tactile Sensor ◽  
Tactile Sensing ◽  
Dynamic Force ◽  
Force Output ◽  
Tactile Sensors ◽  
Tactile Information ◽  
Normal Contact ◽  
New Type ◽  
Learning Machine

Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. A new type of tangential friction and normal contact force magnetostrictive tactile sensor was developed based on the inverse magnetostrictive effect, and the force output model has been established. It can measure the exerted force in the range of 0–4 N, and it has a good response to the dynamic force in cycles of 0.25–0.5 s. We present a tactile perception strategy that a manipulator with tactile sensors in its grippers manipulates an object to measure a set of tactile features. It shows that tactile sensing system can use these features and the extreme learning machine algorithm to recognize household objects—purely from tactile sensing—from a small training set. The complex matrixes show the recognition rate is up to 83%.

Effect of Tumor Variables and Sensor Design on the Measured Stiffness Distribution of Tumor-Embedded Tissues: A Numerical Study

2016 ◽  
Author(s):  
Yichao Yang ◽  
Zhili Hao
Keyword(s):  
Indentation Depth ◽  
Numerical Study ◽  
Tactile Sensor ◽  
Element Model ◽  
Design Parameters ◽  
Sensor Design ◽  
Tissue Stiffness ◽  
Tumor Identification ◽  
Tissue Region ◽  
Stiffness Distribution

This paper reports on a numerical study on how the measured stiffness distribution of a tumor-embedded tissue via a two-dimensional (2D) tactile sensor varies with the tumor variables (i.e., elasticity, size and depth) and the sensor design parameters. The sensor entails a polydimethylsiloxane (PDMS) microstructure embedded with a 3×3 sensing-plate/transducer array sitting on a Pyrex substrate. Pressing the sensor against a tissue region with a pre-defined indentation depth pattern, the tissue stiffness distribution is extracted from the measured slopes of the deflections of the 3×3 sensing-plate array versus the indentation depth. A finite element model (FEM) of the tissue-sensor interaction, which includes the Pyrex substrate, the microstructure, and a tumor-embedded tissue, is created using COMSOL Multiphysics. The tumor variables and the sensor design parameters are varied in the model to examine how the measured tissue stiffness distribution is affected by them. Based on the numerical results, the relation of the measured tissue stiffness distribution to the tumor variables and sensor design parameters is obtained, shedding insight on establishing a threshold on the stiffness contrast for tumor identification.

Sign in / Sign up

Export Citation Format

Share Document