Measuring Repositioning in Home Care for Pressure Injury Prevention and Management
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. System Setup and Data Collection
2.3. Data Processing
2.4. Ground Truth Participant Position Labels
2.5. Data Analyses
2.5.1. System Accuracy
2.5.2. Incremental Learning
2.5.3. Machine Learning Approach
2.6. Statistical Analyses
2.6.1. Machine Learning Classifiers
2.6.2. Inter-Rater Reliability
3. Results
3.1. Participants
3.2. Machine Learning Models
3.2.1. Mean Accuracy
3.2.2. F1 Scores
3.3. Incremental Learning Levels
3.3.1. Mean Accuracy
3.3.2. F1 Scores
3.4. Inter-Rater Reliability
4. Discussion
4.1. Machine Learning Models
4.2. Incremental Learning
4.3. Ground Truth Labelling
4.4. Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature | Description |
---|---|
mean CoM_x | The mean of CoM_x parallel to the width of the bed |
meanCoM_y | The mean of CoM_y parallel to the length of the bed |
ratio_meanCoM | The quotient of meanCoM_y divided by CoM_x |
stdCoM_x | The standard deviation of CoM_x |
stdCoM_y | The standard deviation of CoM_y |
ratio_stdCoM | The quotient of stdCoM_y divided by stdCoM_x |
CoM_resp_ANG | CoM angle during inhalation phase only, averaged for all occurrences |
stdCoM_resp_ANG | Standard deviation of CoM_resp_ANG |
rmsCoM_resp_x | The root mean square of the x-component of CoM_resp during both inhale and exhale phases, normalized to the 97th percentile |
rmsCoM_resp_y | The root mean square of the y-component of CoM_resp during both inhale and exhale phases, normalized to the 97th percentile |
ratio_rmsCoM_resp | The quotient of rmsCoM_resp_y divided by rmsCoM_resp_x |
rmsPulse | The root mean square of the load cell signals filtered to capture changes from the cardiac cycle |
Layer | Number of Nodes | Activation Function |
---|---|---|
Input | 12 | ReLu |
Fully Connected 1 | 64 | ReLu |
Dropout | N/A | N/A |
Batch Normalization | N/A | N/A |
Fully Connected 2 | 100 | ReLu |
Dropout | N/A | N/A |
Batch Normalization | N/A | N/A |
Output | 3 | Softmax |
Layer | Number of Nodes | Activation Function |
---|---|---|
Input | 12 | ReLu |
Fully Connected 1 | 12 | ReLu |
Dropout | N/A | N/A |
Batch Normalization | N/A | N/A |
Fully Connected 2 | 6 | ReLu |
Dropout | N/A | N/A |
Batch Normalization | N/A | N/A |
Fully Connected 3 | 4 | ReLu |
Dropout | N/A | N/A |
Batch Normalization | N/A | N/A |
Output | 3 | Softmax |
Participant | Sex (M/F) | Age Range (years) | Height (cm) | Weight (kg) | BMI (kg/m2) |
---|---|---|---|---|---|
1 | M | 70–79 | 171.5 | 70.0 | 23.9 |
2 | F | 70–79 | 160.0 | 56.8 | 22.1 |
3 | F | 70–79 | 170.0 | 61.3 | 21.2 |
4 | M | 30–39 | 180.3 | 84.1 | 25.9 |
5 | F | 50–59 | 157.0 | 85.0 | 34.5 |
6 | M | 20–29 | 167.0 | 69.5 | 24.5 |
7 | F | 60–69 | 156.0 | 61.4 | 25.2 |
8 | F | 60–69 | 162.0 | 54.4 | 20.7 |
9 | M | 60–69 | 181.0 | 80.5 | 24.5 |
Model | c = 0% | c = 10% | c = 20% | c = 30% |
---|---|---|---|---|
ADA | 62.27% ± 15.44% | 66.68% ± 12.21% | 71.86% ± 10.33% | 74.37% ± 11.09% |
GBC | 62.27% ± 13.45% | 79.09% ± 8.66% | 84.98% ± 6.33% | 90.13% ± 4.64% |
LGB | 61.68% ± 13.25% | 93.94% ± 4.52% | 96.86% ± 2.00% | 97.99% ± 1.42% |
LR | 67.77% ± 16.01% | 69.05% ± 15.05% | 70.18% ± 14.34% | 71.23% ± 13.81% |
MLP | 67.92% ± 14.95% | 69.35% ± 17.90% | 73.06% ± 15.29% | 75.40% ± 10.68% |
OG | 64.11% ± 16.01% | 78.25% ± 10.91% | 85.69% ± 6.70% | 88.58% ± 6.27% |
SVM | 71.25% ± 15.84% | 72.21% ± 15.54% | 73.09% ± 15.21% | 74.01% ± 14.85% |
XGB | 61.75% ± 14.02% | 93.40% ± 5.12% | 96.83% ± 1.82% | 98.12% ± 1.03% |
Model | c = 0% | c = 10% | c = 20% | c = 30% |
---|---|---|---|---|
ADA | 0.654 ± 0.153 | 0.697 ± 0.1240 | 0.7436 ± 0.1100 | 0.766 ± 0.1190 |
GBC | 0.650 ± 0.147 | 0.808 ± 0.0813 | 0.8637 ± 0.0578 | 0.910 ± 0.0369 |
LGB | 0.639 ± 0.138 | 0.946 ± 0.0361 | 0.9713 ± 0.0152 | 0.981 ± 0.0116 |
LR | 0.695 ± 0.168 | 0.710 ± 0.1540 | 0.7225 ± 0.1440 | 0.733 ± 0.1370 |
MLP | 0.695 ± 0.153 | 0.708 ± 0.1880 | 0.7441 ± 0.1500 | 0.769 ± 0.1120 |
OG | 0.662 ± 0.161 | 0.802 ± 0.0901 | 0.8728 ± 0.0494 | 0.898 ± 0.0470 |
SVM | 0.713 ± 0.167 | 0.723 ± 0.1640 | 0.7317 ± 0.1590 | 0.741 ± 0.1540 |
XGB | 0.637 ± 0.154 | 0.941 ± 0.0403 | 0.9707 ± 0.0144 | 0.982 ± 0.0030 |
Participant | Left-Side (κ) | Supine (κ) | Right-Side (κ) | Combined (κ) | n |
---|---|---|---|---|---|
1 | 0.998 | 0.997 | 0.988 | 0.991 | 1754 |
2 | 0.990 | 0.984 | 0.998 | 0.988 | 1684 |
3 | - | 0.970 | 0.975 | 0.945 | 1435 |
4 | 0.997 | 0.836 | 0.961 | 0.934 | 996 |
5 | 1.000 | 0.984 | 0.996 | 0.994 | 1419 |
6 | 0.999 | 0.989 | 1.000 | 0.995 | 1944 |
7 | 0.999 | 0.769 | 0.974 | 0.959 | 820 |
8 | 1.000 | 0.859 | 0.861 | 0.880 | 1295 |
9 | 1.000 | 1.000 | 1.000 | 1.000 | 737 |
Overall (κ) | 0.960 | 0.969 | 0.948 | 0.935 | 12,084 |
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Gabison, S.; Pupic, N.; Evans, G.; Dolatabadi, E.; Dutta, T. Measuring Repositioning in Home Care for Pressure Injury Prevention and Management. Sensors 2022, 22, 7013. https://doi.org/10.3390/s22187013
Gabison S, Pupic N, Evans G, Dolatabadi E, Dutta T. Measuring Repositioning in Home Care for Pressure Injury Prevention and Management. Sensors. 2022; 22(18):7013. https://doi.org/10.3390/s22187013
Chicago/Turabian StyleGabison, Sharon, Nikola Pupic, Gary Evans, Elham Dolatabadi, and Tilak Dutta. 2022. "Measuring Repositioning in Home Care for Pressure Injury Prevention and Management" Sensors 22, no. 18: 7013. https://doi.org/10.3390/s22187013
APA StyleGabison, S., Pupic, N., Evans, G., Dolatabadi, E., & Dutta, T. (2022). Measuring Repositioning in Home Care for Pressure Injury Prevention and Management. Sensors, 22(18), 7013. https://doi.org/10.3390/s22187013