Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning
Abstract
:1. Introduction
- RQ1. Can kinematic data taken from key points of the torso be used to determine the person’s characteristic type of breathing with satisfactory accuracy for respiratory rehabilitation?
- RQ2. What is the difference in frequency responses of the person’s torso’s marker movement for different types of breathing?
2. Equipment and Methods of Capturing and Preparing Data
2.1. Motion Capture System
- Three VIVE Tracker markers (3.0) (the minimum number required to track the movements of the chest and abdomen);
- Three waist elastic belts with fasteners for markers;
- Two SteamVR Base Stations 2.0.
2.2. Study Design
- date of birth;
- biological sex;
- the last time interval of having COVID-19 (if any);
- percentage of lung damage;
- the presence of respiratory diseases at the time of data collection.
2.3. Data Preprocessing
2.4. Signal Spectrum Analysis
3. Coordinate-Based Machine Learning Models
3.1. Decision Tree Model
3.2. Random Forest Model
3.3. K-Neighbors Model
3.4. Catch 22 Classifier Model
3.5. Rocket Classifier Model
3.6. Hist Gradient Boosting Classifier
3.7. Other Models
4. Two-Stage Method for Determining the Type of Human Breathing
4.1. Algorithm
Algorithm 1 Algorithm of determining the type of human breath |
|
- AMD Ryzen 7 5700U 8x 1.80 GHz;
- AMD Radeon™ RX 640;
- 16 Gb RAM 3200 MHz.
4.2. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
API | Application programming interface |
AUC | Area Under Curve |
EDA | Electrodermal activity |
FCN | Fully Convolutional Network |
GRU | Gated recurrent unit |
IQR | interquartile range |
LR | Logistic Regression |
LSTM | Long short-term memory |
MLP | Multilayer Perceptron |
RGB | red, green, and blue |
SpO | Peripheral oxygen saturation |
SVM | Support Vector Machines |
WSN | Wireless sensor Network |
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Study | Pros | Cons |
---|---|---|
Brown et al. [10] | + High accuracy (>80% AUC) + The convenience of data collection—Web and Android applications for collecting data using a microphone. | - Did not determine the type of breathing. |
Schoun et al. [18] | + No additional equipment is attached to the patient. It can be used to analyze breathing during sleep, and it is not constrained by the patient’s age. + High accuracy (>70%). | - Analysis of general respiratory metrics, whose values can be collected using already established devices with higher accuracy—spirometry, etc. - The need to assemble the installation around the patient. |
Yanbin Gong et al. [16] | + Higher accuracy compared to state-of-the-art models (approximately 95% accuracy). + The system consists of only one speaker. | - It determines only one type of breathing—diaphragmatic. |
Han An Tran et al. [17] | + Cheapness of operation—only an Android-based phone is required. + An average error in determining the type of breathing is 8%. | - Determines only one type of breathing. - A small sample size—17 students aged 23–24 years. |
Hyperparameter | Short Description | Value |
---|---|---|
criterion | A function that evaluates the quality of division at each node of the decision tree. | “gini” |
splitter | The strategy used to select a specific division from the set at each node, taking into account the estimates obtained from the function specified in the criterion parameter. | “best” |
max_depth | Maximum depth of the decision tree. | 5 |
min_samples_split | The minimum number of samples required to split a node of the decision tree. | 2 |
min_samples_leaf | The minimum number of samples required to form a “leaf”. | 1 |
max_features | The number of functions to consider when searching for the best partition. | 3 |
random_state | Setting the fixed state of the random component. | 12 |
Hyperparameter | Short Description | Value |
---|---|---|
criterion | A function that evaluates the quality of division execution at each node of the decision tree. | “gini” |
n_estimators | The number of decision trees in a random forest. | 10 |
max_depth | Maximum depth of decision trees. | 5 |
min_samples_split | The minimum number of samples required to split a node of the decision tree. | 2 |
max_features | The number of functions to consider when searching for the best partition; when “sqrt”: . | “sqrt” |
bootstrap | Indicates whether it is necessary to split the initial sample into several random subsamples when training trees. | True |
random_state | Setting the fixed state of the random component. | 12 |
Hyperparameter | Short Description | Value |
---|---|---|
n_neighbors | Number of neighbors. | 4 |
weights | The weight function used in prediction. | ‘distance’ |
metric | A metric used to calculate the distance to neighbors. | ‘minkowski’ |
Hyperparameter | Short Description | Value |
---|---|---|
outlier_norm | Normalization of each sequence during two additional Catch 22 functions. | True |
n_jobs | Parallelization of calculations into multiple threads. | −1 |
random_state | Setting the fixed state of the random component. | 12 |
Hyperparameter | Short Description | Value |
---|---|---|
num_kernels | Number of kernels. | 500 |
n_jobs | Parallelization of calculations across multiple threads. | −1 |
Hyperparameter | Short Description | Value |
---|---|---|
random_state | Setting the fixed state of the random component. | 15 |
learning_rate | Learning rate. | 1 |
max_depth | Maximum depth of the decision trees. | 15 |
loss | The loss function that the model minimizes during the boosting process. | ‘log_loss’ |
Model Kind | Number of Models | Accuracy | Precision | Recall | F1-Measure | Training time, s | Working Time, s | log_Loss (Logistic Loss) |
---|---|---|---|---|---|---|---|---|
Random Forest Classifier | 1 | 0.53 | 0.5 | 0.54 | 0.47 | 0.018 | 0.002 | 8.67 |
Decision Tree Classifier | 1 | 0.51 | 0.46 | 0.51 | 0.46 | 0.004 | 0.001 | 13.24 |
Catch 22 Classifier | 1 | 0.63 | 0.65 | 0.63 | 0.64 | 8.926 | 8.665 | 0.94 |
Rocket Classifier | 1 | 0.46 | 0.45 | 0.46 | 0.46 | 11.507 | 5.437 | 18.68 |
K-Neighbors Classifier | 1 | 0.41 | 0.38 | 0.41 | 0.36 | 0.006 | 0.001 | 10.8 |
Hist Gradient Boosting Classifier | 1 | 0.42 | 0.51 | 0.42 | 0.37 | 0.324 | 0.009 | 4.22 |
Random Forest Classifier | 2 | 0.46 | 0.46 | 0.46 | 0.46 | 0.047 | 0.485 | 6.78 |
Decision Tree Classifier | 2 | 0.52 | 0.52 | 0.52 | 0.52 | 0.004 | 0.11 | 13.21 |
Catch 22 Classifier | 2 | 0.57 | 0.57 | 0.57 | 0.57 | 19.02 | 34.609 | 0.96 |
Rocket Classifier | 2 | 0.48 | 0.48 | 0.48 | 0.48 | 35.318 | 64.687 | 17.83 |
K-Neighbors Classifier | 2 | 0.4 | 0.4 | 0.4 | 0.4 | 0.005 | 0.407 | 7.89 |
Hist Gradient Boosting Classifier | 2 | 0.81 | 0.83 | 0.82 | 0.82 | 0.875 | 0.75 | 1.98 |
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Orlova, Y.; Gorobtsov, A.; Sychev, O.; Rozaliev, V.; Zubkov, A.; Donsckaia, A. Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning. Algorithms 2023, 16, 249. https://doi.org/10.3390/a16050249
Orlova Y, Gorobtsov A, Sychev O, Rozaliev V, Zubkov A, Donsckaia A. Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning. Algorithms. 2023; 16(5):249. https://doi.org/10.3390/a16050249
Chicago/Turabian StyleOrlova, Yulia, Alexander Gorobtsov, Oleg Sychev, Vladimir Rozaliev, Alexander Zubkov, and Anastasia Donsckaia. 2023. "Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning" Algorithms 16, no. 5: 249. https://doi.org/10.3390/a16050249