A Novel Design of Soft Corrugated Actuator for Creating Precise Head and Neck Positioning System
Abstract
:1. Introduction
- A corrugated actuator design that can produce linear displacement in response to applied pressure, which can aid in the implementation of accurate closed-loop control.
- The development of a multi-actuator system that provides 4 DoF for head positioning during cancer treatment.
2. Materials and Methods
2.1. System Design
2.2. Single Actuator Design and Simulation Studies
2.3. Single Actuator Fabrication and Experimental Setup
2.4. Single Actuator Modeling
- Data Collection;
- Exploratory Data Analysis and Data Pre-Processing;
- ML Model for Single Actuator.
2.4.1. Data Collection
2.4.2. Exploratory Data Analysis and Data Pre-Processing
- Statistical Overview: One fundamental metric examined is the Std Dev, which is used to measure the skewness of the dataset. This could affect the modeling of distance-based methods, such as K-Nearest Neighbor (KNN), discussed in Section 2.4.3. Notably, the weight parameter exhibits a relatively high Std Dev (605.619 g), indicating substantial dispersion among the collected data points. To address this, statistical log transformation is applied to the weight feature, which effectively reduced its Std Dev (1.626 g) [21].Here, denotes transformed weight, log denotes natural logarithm function, W denotes original weight values, and constant value 1 handles 0 g weight. Equation (1) shows statistical log transformation.
- Feature Analysis: The selection of the regression model and appropriate feature transformation is determined based on the relationship between each feature and the target variable. In our case, the two features (weight and displacement) and the target variable (pressure) are analyzed for their correlations to select the most suitable model.This analysis provides the basis for applying strategic polynomial feature transformation [22], which allows us to derive the interaction effects of weight and displacement features on pressure, leading to a better fit for the regression model.The optimal degree of the polynomial features is chosen using a grid search with cross-validation (GridSearchCV) [23] and regression modeling [24], with respect to weight and displacement, to find the degree with the least Mean Squared Error (MSE) in pressure prediction. Regression modeling is further substantiated in Section 2.4.3.From Figure 5, we can infer that polynomial degree 6 is the optimal degree for our model with the least MSE.
- Data Augmentation: To handle insufficient sample size that could lead to overfitting, in which the model performs well on the training set but poorly on unseen data, we employ the following techniques:
- (a)
- Bootstrapping: We augment the dataset to generate an additional 1619 records by iteratively selecting random samples from the original dataset and adding these samples back as new data, which expands the overall dataset size to the desired length [25].
- (b)
- Gaussian Noise: During model validation with unseen data, we observe instances where the model predicted identical pressure values for static weights that only differed in displacement. This behavior indicated overfitting. Gaussian noise [26] is introduced, thereby enabling model generalization, while preserving the statistical properties of the original dataset.
By applying Bootstrapping and introducing Gaussian noise, we are able to improve data quality and quantity, which will help produce effective actuator pressure predictions.
2.4.3. ML Model for Single Actuator
2.4.4. Model Evaluation
3. Application: Multi-Actuator H&N Positioning System
3.1. System Fabrication and Experimental Setup
3.2. Multi-Actuator System Model
4. Results
4.1. Single Actuator Simulation Validation
4.2. Single Actuator Model Validation
4.3. Multi-Actuator System Model Validation
4.4. 4-DoF Multi-Actuator H&N Positioning System
4.5. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ML | Machine Learning |
DoF | Degrees of Freedom |
H&N | Head and Neck |
Std Dev | Standard Deviation |
MSE | Mean Squared Error |
KNN | K-Nearest Neighbor |
SVD | Singular Value Decomposition |
PCA | Principal Component Analysis |
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Dimensions | Actuator |
---|---|
(mm) | 50 |
(mm) | 14 |
(mm) | 60 |
(mm) | 30 |
(mm) | 4 |
(mm) | 5 |
(degree) | 65° |
Model | Mean Squared Error | R2 Score |
---|---|---|
Polynomial Regression | 0.1538 | 0.9050 |
KNN-CV | 0.1015 | 0.9914 |
Weight (g) | Expansion from Experiment (mm) | Expansion from Simulation (mm) | Expansion Deviation (mm) |
---|---|---|---|
0 | 22.66 | 22.05 | 0.61 |
500 | 21.22 | 20.68 | 0.54 |
1000 | 19.98 | 19.67 | 0.31 |
1500 | 18.89 | 18.35 | 0.54 |
2000 | 17.40 | 17.13 | 0.27 |
Input Weight (g) | Input Displacement (mm) | Predicted Pressure (kPa) | Experimentally Measured Displacement (mm) | Displacement Deviation (mm) |
---|---|---|---|---|
300 | 6 | 16.88 | 6 | 0 |
600 | 10 | 33.14 | 11 | 1 |
700 | 4 | 15.02 | 3 | 1 |
950 | 2 | 16.81 | 2 | 0 |
1000 | 2.5 | 33.27 | 2 | 0.5 |
1500 | 7.5 | 15.02 | 7 | 0.5 |
1800 | 4 | 16.95 | 3 | 1 |
2000 | 10 | 15.09 | 9 | 1 |
Predicted P1 (kPa) | Predicted P2 (kPa) | Target Angle (degree) | Measured Angle (degree) |
---|---|---|---|
17.01 | 24.52 | 2.00° | 1.79° |
32.52 | 35.96 | 4.00° | 3.78° |
35.75 | 53.32 | 6.00° | 5.50° |
51.88 | 62.01 | 8.00° | 7.62° |
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Erel, V.; Suresh, S.; Singh, I.; Palomino, A.; Tafannum, F.; Hosseini Nami, S.; Jiang, S.; Peng, H.; Gans, N.; Wijesundara, M.B.J. A Novel Design of Soft Corrugated Actuator for Creating Precise Head and Neck Positioning System. Actuators 2024, 13, 351. https://doi.org/10.3390/act13090351
Erel V, Suresh S, Singh I, Palomino A, Tafannum F, Hosseini Nami S, Jiang S, Peng H, Gans N, Wijesundara MBJ. A Novel Design of Soft Corrugated Actuator for Creating Precise Head and Neck Positioning System. Actuators. 2024; 13(9):351. https://doi.org/10.3390/act13090351
Chicago/Turabian StyleErel, Veysel, Sanjana Suresh, Inderjeet Singh, Allison Palomino, Faiza Tafannum, Seyedehhelya Hosseini Nami, Steve Jiang, Hao Peng, Nicholas Gans, and Muthu B. J. Wijesundara. 2024. "A Novel Design of Soft Corrugated Actuator for Creating Precise Head and Neck Positioning System" Actuators 13, no. 9: 351. https://doi.org/10.3390/act13090351