Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection
Abstract
:1. Introduction and Related Work
2. Experimental Dataset
3. Methods
4. Experiments
4.1. User-Independent Stress Detection
4.2. Personal Stress Detection
5. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Method | RMSE |
---|---|
Linear regression | 0.23 |
Robust linear regression | 0.24 |
Fine tree | 0.05 |
Medium tree | 0.06 |
Coarse tree | 0.08 |
SVM with linear kernel | 0.24 |
SVM with quadratic kernel | 0.14 |
SVM with cubic kernel | 0.7 |
SVM with fine Gaussian kernel | 0.14 |
SVM with medium Gaussian kernel | 0.8 |
SVM with coarse Gaussian kernel | 0.21 |
Boosted tree based ensemble | 0.16 |
Bagged tree based ensemble | 0.03 |
Regression | |||
---|---|---|---|
Sensors | Balanced accuracy | Sensitivity | Specificity |
ACC+EDA+ST+BVP | 89.0 (13.3) | 86.9 (22.7) | 93.6 (5.8) |
EDA+ST+BVP | 75.0 (16.0) | 71.6 (27.3) | 90.9 (9.2) |
EDA+BVP | 75.1 (15.8) | 73.5 (24.5) | 88.3 (7.8) |
BVP+ST | 82.3 (17.0) | 76.6 (23.8) | 91.0 (6.1) |
EDA+ST | 72.3 (16.7) | 70.9 (24.2) | 84.0 (11.0) |
EDA | 73.5 (15.2) | 72.0 (23.7) | 87.5 (10.8) |
BVP | 78.5 (16.7) | 72.1 (23.7) | 89.0 (7.2) |
ST | 68.6 (11.8) | 56.1 (19.3) | 83.5 (9.0) |
ACC | 94.3 (5.5) | 93.2 (7.0) | 96.2 (3.4) |
Classification | |||
Sensors | Balanced accuracy | Sensitivity | Specificity |
ACC+EDA+ST+BVP | 85.2 (14.2) | 82.4 (22.9) | 91.7 (9.4) |
EDA+ST+BVP | 65.4 (22.8) | 61.6 (33.9) | 76.7 (15.7) |
EDA+BVP | 69.3 (16.9) | 57.9 (33.3) | 78.7 (11.4) |
BVP+ST | 74.1 (16.7) | 66.0 (33.5) | 85.6 (10.9) |
EDA+ST | 64.6 (23.1) | 54.1 (31.8) | 73.2 (17.5) |
EDA | 64.5 (14.6) | 55.8 (23.9) | 78.1 (12.8) |
BVP | 70.4 (20.4) | 64.0 (38.2) | 82.9 (11.0) |
ST | 61.3 (14.9) | 50.5 (27.6) | 76.5 (12.0) |
ACC | 90.2 (10.1) | 91.4 (9.2) | 96.0 (6.4) |
True/Predicted | Non-Stressed | Stressed |
---|---|---|
Non-stressed | 88.0% | 12.0% |
Stressed | 26.6% | 73.4% |
Subject | Classification (%) | Regression (%) |
---|---|---|
NM | 64.3 | 84.6 |
RY | 90.8 | 93.7 |
BK | 87.0 | 88.3 |
MT | 49.0 | 50.5 |
EK | 59.4 | 60.7 |
KGS | 88.3 | 93.7 |
AD | 96.3 | 99.7 |
GM | 69.9 | 95.0 |
SJ | 62.2 | 74.7 |
Mean | 74.1 (STD 16.7) | 82.3 (STD 17.0) |
Subject | RMSE Total | RMSE Stress | R-Squared Total |
---|---|---|---|
NM | 0.31 | 0.26 | 0.09 |
RY | 0.27 | 0.26 | 0.49 |
BK | 0.24 | 0.18 | 0.60 |
MT | 0.43 | 0.18 | 0 |
EK | 0.46 | 0.52 | 0 |
KGS | 0.25 | 0.28 | 0.60 |
AD | 0.20 | 0.25 | 0.75 |
GM | 0.22 | 0.21 | 0.64 |
SJ | 0.48 | 0.60 | 0 |
Mean | 0.32 | 0.31 | 0.35 |
Subject | Balanced Accuracy (%) | Sensor Combination |
---|---|---|
NM | 84.6 | BVP+ST |
RY | 93.7 | BVP+ST |
BK | 91.1 | EDA+BVP+ST |
MT | 64.3 | EDA+BVP+ST |
EK | 83.2 | BVP+EDA |
KGS | 93.7 | BVP+ST |
AD | 99.7 | BVP+ST |
GM | 95.0 | BVP+ST |
SJ | 70.8 | ST |
Mean | 86.3 (STD 11.7) |
Sensors | Valid/Train NM1/NM2 Classif./Regr. | Valid/Train NM1/NM3 Classif./Regr. | Valid/Train RY1/RY2 Classif./Regr. | Valid/Train GM1/GM2 Classif./Regr. |
---|---|---|---|---|
EDA+ST+BVP | 69.3/70.1 | 49.2/68.0 | 50.0/50.0 | 50.0/ 71.4 |
EDA+BVP | 69.9/79.5 | 50.0 /50.0 | 54.8/71.2 | 50.0/50.0 |
BVP+ST | 69.3/88.0 | 50.8/67.9 | 50.0/65.0 | 83.7/91.6 |
EDA+ST | 69.3/70.1 | 80.9/91.4 | 50.0/50.6 | 50.0/50.0 |
EDA | 69.2/83.5 | 49.4/68.8 | 51.1/50.5 | 50.0/50.0 |
BVP | 83.3/84.9 | 51.4/50.8 | 95.0/94.4 | 57.8/91.1 |
ST | 69.3/70.1 | 69.3/69.4 | 50.0/84.7 | 50.0/78.7 |
Sensors | Valid/Train NM1/NM2 + NM3 Classif./Regr. | Valid/Train NM2/NM1 + NM3 Classif./Regr. | Valid/Train NM3/NM1 + NM2 Classif./Regr. |
---|---|---|---|
EDA+ST+BVP | 74.4 /98.3 | 77.5/99.3 | 50.1/52.6 |
EDA+BVP | 62.4/77.2 | 32.0 /50.0 | 51.0/52.4 |
BVP+ST | 85.5/98.8 | 50.0/60.4 | 52.3/52.1 |
EDA+ST | 69.3/85.6 | 80.6/99.4 | 48.2/52.6 |
EDA | 63.8/80.1 | 32.9/68.3 | 49.6/51.0 |
BVP | 74.5/78.7 | 64.8/87.4 | 51.9/50.9 |
ST | 69.3/69.4 | 50.0/51.1 | 56.9/61.2 |
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Siirtola, P.; Röning, J. Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection. Sensors 2020, 20, 4402. https://doi.org/10.3390/s20164402
Siirtola P, Röning J. Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection. Sensors. 2020; 20(16):4402. https://doi.org/10.3390/s20164402
Chicago/Turabian StyleSiirtola, Pekka, and Juha Röning. 2020. "Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection" Sensors 20, no. 16: 4402. https://doi.org/10.3390/s20164402
APA StyleSiirtola, P., & Röning, J. (2020). Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection. Sensors, 20(16), 4402. https://doi.org/10.3390/s20164402