A Kinect-Based System for Upper-Body Function Assessment in Breast Cancer Patients
Abstract
:1. Introduction
2. Related Work
2.1. Upper-Limb Volume Measurements
2.1.1. Traditional Methods
System | Time to Operate | Home/Travel | Accuracy | Cost | Complex |
---|---|---|---|---|---|
Water Displacement: The limb is immersed into a container, and the amount of the displaced water represents the volume [19]. | High | No | High | Low | Medium |
Circumferential Measurements: The volume can be estimated assuming cylindrical/conic volumes between several measures taken along the limb [20]. | High | Yes | Low | Low | Low |
Perometer ®: The device scans the limb with IR light and assesses the volume at small intervals [21]. | Medium | No | High | Medium | High |
CT: Determines the overall cross-section area and quantifies the density of the tissues [22]. | Low | No | High | High | High |
BIS: A small current passes through the body; it measures volumes by comparing the impedance values of both arms [20]. | Medium | No | Medium | High | Medium |
2.1.2. Three-Dimensional Scanners
2.2. Upper-Limb Motion Evaluation
2.2.1. Subjective Methods
Scale | Paper | Type of Measure | Description | Clinical interpretation | Comments |
---|---|---|---|---|---|
PSFS | Stratford et al. [26] | Clinical measure of function | 3 items; 11-point scale | Higher score, better function | For use in the clinical setting: measures the change in function specific to the individual survivor. |
DASH | Hudak et al. [27] | Pain-related upper extremity disability | 30 items; 5-point scale | Higher score, poorer function | Has not been validated in breast cancer patients. |
UEFI | Stratford et al. [28] | Upper-body function | 20 items; 5-point scale | Higher score, better function | Valid and sensitive to changes in the breast cancer population. |
KAPS | Kwan et al. [29] | Upper-body symptoms and function | 13 items; 5-point scale | Higher score, poorer function | Developed to identify shoulder and arm problems during breast cancer treatment. |
2.2.2. Objective Methods
2.3. Summary
3. An Upper-Body Function Evaluation System
3.1. Depth-Map Noise Reduction
3.2. Patient Segmentation
3.3. Arm Point Detection
3.4. Feature Extraction
3.4.1. Volume
3.4.2. Range of Motion
- the ratio of the maximum ROM, obtained at the maximum height achieved by the hand ().
- the average of the ROM ratio along all movement ().
3.4.3. Hand Height and Hand Width
- the ratio of the maximum height achieved by the hand ().
- the width at the maximum height point ().
- the average of the height ratio along all movement ().
- the average of the width ratio along all movement ().
3.4.4. Elbow Flexion
3.4.5. Hand Acceleration
3.5. Classification Models
- | Acronym | Description |
---|---|---|
1 | Average of the volume ratio. | |
2 | ROM at the maximum height achieved by the hand. | |
3 | Maximum height achieved by the hand. | |
4 | Width at the maximum height point. | |
5 | Average of the right and left ratio of the elbow angle. | |
6 | Average of the hand instantaneous acceleration. | |
7 | Average of the height ratio along the movement. | |
8 | Average of the width ratio along the movement. | |
9 | Average of the ROM ratio along the movement. |
4. Results
4.1. Database
4.1.1. Database Acquisition
4.1.2. Database Analysis
4.2. Depth Map Noise Reduction
4.3. Patient Segmentation
Name | Equation | - |
---|---|---|
Dice coefficient (D) | Equation (6) | Measures the extent of spatial overlap between two binary images. It gives more weighting to instances where the two images agree. Its values range between 0 (no overlap) and 1 (perfect agreement). |
Jaccard index (J) | Equation (7) | Measures similarity between finite sample sets and is defined as the number of attributes shared divided by the total number of attributes present in either of them. Its values range between 0 (no similarity) and 1 (equal). |
- | Body Segmentation | Arm Segmentation | ||
---|---|---|---|---|
Mean | SD | Mean | SD | |
Dice coefficient (D) | 0.999 | 0.003 | 0.783 | 0.048 |
Jaccard index (J) | 0.997 | 0.005 | 0.646 | 0.066 |
- | Detected ⇒ GT | GT ⇒ Detected | |||||||
---|---|---|---|---|---|---|---|---|---|
Mean | SD | Max | Min | Mean | SD | Max | Min | ||
Body | h | 3.99 | 7.31 | 42.76 | 0 | 3.60 | 7.42 | 38.60 | 0 |
Avg | 0.09 | 0.21 | 1.28 | 0 | 0.11 | 0.33 | 2.16 | 0 | |
Arm | h | 12.90 | 2.80 | 18.60 | 6.00 | 12.70 | 4.25 | 22.00 | 5.83 |
Avg | 4.82 | 1.03 | 7.28 | 2.67 | 4.91 | 1.18 | 7.20 | 2.83 |
4.4. Arm Segmentation
4.5. Upper-Body Functional Evaluation
Classifier | Kernel | C | Order | γ | MER | Feat. Set |
---|---|---|---|---|---|---|
LDA | - | - | - | - | 0.29 | [, ] |
Naive Bayes | - | - | - | - | 0.27 | [, , ] |
SVM | Linear | - | - | 0.25 | [, , ] | |
SVM | Polynomial | 4 | - | 0.19 | [, ] | |
SVM | RBF | - | 0.75 | 0.19 | [, , ] |
True Prediction | Reduced UBF | Normal UBF |
---|---|---|
Reduced UBF | 18 | 6 |
Normal UBF | 3 | 21 |
Classifier | Precision | Recall | Specificity |
---|---|---|---|
LDA | 0.92 | 0.46 | 0.96 |
Naive Bayes | 0.92 | 0.50 | 0.96 |
Linear SVM | 1.00 | 0.50 | 1.00 |
Polynomial SVM | 0.86 | 0.75 | 0.88 |
RBF SVM | 0.86 | 0.75 | 0.88 |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Moreira, R.; Magalhães, A.; Oliveira, H.P. A Kinect-Based System for Upper-Body Function Assessment in Breast Cancer Patients. J. Imaging 2015, 1, 134-155. https://doi.org/10.3390/jimaging1010134
Moreira R, Magalhães A, Oliveira HP. A Kinect-Based System for Upper-Body Function Assessment in Breast Cancer Patients. Journal of Imaging. 2015; 1(1):134-155. https://doi.org/10.3390/jimaging1010134
Chicago/Turabian StyleMoreira, Rita, André Magalhães, and Hélder P. Oliveira. 2015. "A Kinect-Based System for Upper-Body Function Assessment in Breast Cancer Patients" Journal of Imaging 1, no. 1: 134-155. https://doi.org/10.3390/jimaging1010134