Design requirements

In vivo measurements for evaluating viscoelastic properties of organs such as the bladder or pelvic organ tissue are beneficial in that they provide for the quantitative assessment of the healthiness of the tissue. The design requirements of the micro-force sensor proposed in this research must consider accessibility to the confined space site as it relates to its size, the range of the normal force to be applied at the tip of the sensor and operational environment conditions.

According to the study conducted by Hudson et al., ideally an outside diameter of a flexible endoscope must be ≤7.4Fr (2.4mm) to avoid ureteric dilatation [31]. In order to maintain a balance between the passage and durability of the device, the outer diameter of commonly used flexible endoscopes in adults falls within a range of 15Fr to 25Fr (5.0mm to 8.3mm) [5]. The force sensor designed by Li. et. al. for accessibility in confined space was reported to be 4mm in diameter [21]. Goud suggested that a device with a limited contact duration (≤24h), needs to be fully packaged and sterilizable to maintain biocompatibility [32].

The environmental constraints include accessibility to confined spaces in the human body, ability to operate in wet/moist conditions and biocompatibility. The issues associated with biocompatibility and the moist operating environment of the bladder could be addressed by encapsulating the sensor with a biocompatible protective sheath or covering. One of the major constraints for the proposed force sensor considering the intended application of bladder diagnosis was the sensor outside diameter which was preferred to be (≤5mm) [5].

Studies showed that pelvic organs can undergo a reaction load of approximately 0.8−1.2N with indentation depth ranging from 8−10mm [6, 7, 17]. The proposed micro-force sensor must safely withstand a normal load of 1.2N with a safety factor suitable for medical devices; in our research we aim for a safety factor greater than 3 similar to that used by Deng et. al. who used a safety factor of 3 [20]. The design requirements for the proposed micro-force sensor are summarized in Table 1.

Table 1 Design requirements for the proposed micro-force sensor

Overall sensor concept and working principle

The CAD model of the proposed sensor is presented in Fig. 1; Fig. 1a is the assembled view and Fig. 1b is the exploded view with an inset showing features of the sensor head indiscernible from the exploded view. The biocompatible covering is not shown for visualization purposes. The nomenclature used for the sensor components follows the labels in Fig. 1b.

Fig. 1
figure1

Solid models of proposed sensor; overall and exploded view, A Assembled sensor model, B Exploded view of the sensor model

The sensor will be positioned at the point of interest and oriented to be perpendicular to the tissue to be queried. The hemispherical surface of the sensor head will engage with the tissue. When the sensor head comes in contact with and indents the tissue, a reaction force will be generated on the hemispherical surface of the sensor head and transferred to the sensing element/beam via the load transmitter on the sensor head. The legs of the sensor head will ensure a sliding motion relative to the sensor base. For an in vivo diagnostic operation, the sensor base could be attached on a micro-robot to gain access to the confined space [29, 30]. The deformation of the beam due to the applied load from the sensor head will generate strain in the beam (the other end of the beam is fastened to the sensor base). The fastener used to affix the beam to the sensor base was a standard 0.5mm Unified National Miniature (UNM) fillister head screw by Antrin Miniature Specialties Inc. (Fallbrook, CA) [33]. The sensing element design considered the allowable space in the sensor base cavity, while maximising the strain sensed without undergoing plastic deformation.

This sensing element served as the mounting structure for a metal foil strain gauge (N2K−06−S5024G−50C/DG/E3) by Micro-Measurements (Wendell, NC) with planar dimensions 1.9mm×1.4mm [34].

Finite element (FE) analysis was performed to identify the dimensions of the sensing element while maximizing the strain experienced due to applied load while remaining in the elastic region and meeting available space constraints. Figure 2 shows the sensing element FE model with applied loads and boundary conditions. As shown in Fig. 1b, the load transmitter does not engage with the sensing element (beam) at the free end but rather on the curved part of the beam at a distance from the free end. This loading condition was modeled as an equivalent normal load and moment at the free end of the beam for the FE analysis. A zero displacement was defined at location A where the fastener will fix the beam element on the sensor base component. Location M defines the center of the active area of the strain gauge. The path from point 1 to point 2 (Fig. 2) on the surface of the sensing element represents the path along which the strain will be evaluated since the strain gauge will be attached along this path. The Von-Mises stress, defined as the uniaxial tensile stress due to the distortion energy by actual combination of applied stresses was used as the failure criterion for designing components within the defined safety factor [35].

Fig. 2
figure2

Sensor component fabrication

The size and complex features of the sensor head and sensor base components could not be easily fabricated using traditional machining processes. This created an impediment to prototyping several iterations of the sensor during design improvements and we investigated the use of 3D printing technology for fabrication.

3D printing served as the rapid prototyping platform for the proposed sensor design due to the geometric features and size of its components without the need to fabricate custom fixtures and molds for traditional machining processes. 3D printing was also used to fabricate the fixtures needed for sensor characterization and experimentation. Fateri & Gebhardt discussed pros and cons of five 3D printing processes; Stereolithography (SLA), Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), Powder-Binding Bonding (3DP) and Layer Laminate Manufacturing (LLM) [36].

As discussed by Ravi et. al., the mean dimensional error for complex geometric models of human organs fabricated using Form3B VP printer (Formlabs, Somerville, MA, USA) with a commercially available Grey material was 260μm with good surface quality [37].

The fabrication specifications using a Form3 printer were well within the required feature size of the sensor housing components [38]. Low Force Stereolithography (LFS), the fabrication process selected for prototyping the sensor components, is the 3D printing technology of the Form3 printer by Formlabs using Formlabs Grey material [39]. The Grey material could be used to fabricate structures with a layer thickness measuring 25μm as opposed to 50μm and 100μm with other Form3 compatible materials [39]. The fabrication slicing paths were generated using the PreForm software by Formlabs. The sensor components were fabricated at the MAE Design Innovation laboratory (The University of Texas at Arlington, TX).

Sensor characterization

This section describes the experimental setup developed to characterize the performance of the prototyped sensor. The characterization experiments were conducted using randomized experiments (to prevent biasing the results) with one factor (applied load) at five different levels. These experiments were designed to evaluate precision, sensitivity, resolution and accuracy of the micro-force sensor as well as its calibration equation. The assembled sensor was set up on the calibration test platform, as shown in the upper left hand corner of Fig. 3.

Fig. 3
figure3

Data acquisition system for sensor calibration setup

The calibration test platform shows a load holder which apply the load in the normal direction on the sensor head. A set of five dead weights were used for the calibration experiments of the micro-force sensor. The applied load would cause the beam and attached strain gauge to deform. The deformation in the strain gauge would generate a signal which was read by a data acquisition (DAQ) unit, a 24-bit NI-9219 (National Instruments Inc. Austin, TX) module, shown in the left hand bottom corner of Fig. 3. The NI-9174 chassis houses the NI-9219 module and was USB connected to a computer running LabVIEW by National Instruments. A LabVIEW graphical user interface was developed to display and record the raw data acquired by the DAQ system. The resistance from the strain gauge was recorded with just the sensor head resting on top of the sensing element and found to be 5014.89Ω. This resistance will be referred to as the nominal or no-load resistance of the attached strain gauge. The application of a load resulted in a change in the resistance where a decrease in the resistance value indicated a compression load. The change in resistance, ΔRsg, nominal resistance, Rsg, and gauge factor, K, of the strain gauge were used to evaluate the equivalent strain due to an applied load according to Eq. 1.

$$ epsilon=frac{1}{K} frac{Delta{R_{sg}}}{R_{sg}} $$

(1)

The calibration equation relating the measured strain, ε, (strain experienced by the strain gauge) to the applied load, F, and the calibration factor, Cf, is presented in Eq. 2.

$$ epsilon = C_{f} times F $$

(2)

The equivalent sensed load using strain measurements during the tissue properties experimentation was evaluated by re-arranging Eq. 2 to yield Eq. 3.

$$ F =frac{epsilon}{C_{f}} $$

(3)

The resolution of a sensor is defined as the smallest absolute change in resistance that could be detected by the measurement device [40]. Sensitivity is the ability of the sensor to capture the smallest change in output variable (resistance) for a given input variable (applied load) [40]. Accuracy of the sensor is defined as the deviation of the measured quantity from the theoretically estimated value [40]. The accuracy of the sensor is evaluated by comparing the strain evaluated from the measured resistance to the theoretical strain obtained from FE analysis. The error between the theoretical and measured strains is evaluated according to Eq. 4.

$$ Error %,=, left lvert frac{text{Theoretical strain} ,-, text{Experimental strain}}{text{Theoretical strain}} rightlvert !times !100% $$

(4)

Precision refers to how closely individual measurements are in agreement with each other for a particular loading condition [40]. Precision is computed according to Eq. 5, where Msd is the maximum deviation observed throughout the measurement and Avg(Msd) is the average measurement throughout the five sets of data for the particular loading condition [40].

$$ Precision %=left (1-left lvert{frac{M_{sd}}{Avg(M_{sd})}}right rvertright)times 100 $$

(5)

Sensor operational performance

The testbed shown in Fig. 4 was developed to obtain initial reaction strain data to validate sensor operational performance by measuring tissue relaxation, and then use the relaxation data to quantitatively characterize biomechanical properties of the interrogating tissue on a human forearm.

Fig. 4
figure4

Testbed for in vivo performance evaluation

The testbed allowed for the sensor to be manually translated using a micrometer dial to a desired indentation distance. After indentation, the sensor was kept at this position for a predefined time while the tissue relaxed. The collected strain data as function of time were transformed into force using the developed characterization Eq. (3). A number of different models have been proposed to evaluate biomechanical tissue properties such as Voigt model, Kelvin-Voigt model, Prony series, and Neo-Hookean [7, 4144]. In this research, the transformed relaxation force data was used to identify viscoelastic properties of the tissue as function of the relaxation time according to the Voigt model [6, 7, 17]. The Voigt model quantifies the ratio of the elastic constant to the damping coefficient as a function of time and quantification of this ratio helps to estimate viscoelastic property of the tissue [7]. The solution to the Voigt model is given by Eq. 6 where f(t) is the measured reaction force response during tissue relaxation, fpeak is the peak reaction load sensed by the sensor, fresidual is the residual force, and τ is a coefficient representing the tissue recoil during the recovery phase [7].

$$ f(t) = (f_{peak}-f_{residual}) e^{-t/tau} + f_{residual} $$

(6)

The initial tissue characterization using the fabricated force sensor demonstrated promising results.

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