Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Optical Fiber Sensor
2.1.2. Electromyographic Sensor
2.1.3. Inertial Sensors
2.1.4. Borg Scale CR10
2.2. Subjects
2.3. Experimental Protocol
2.4. Procedure of Proposed Fatigue Classifier
2.4.1. Data Processing
2.4.2. Training and Validation
2.4.3. Evaluation
2.4.4. Feature Reduction
2.4.5. Model Evaluation (Testing)
3. Results
4. Discussion
4.1. Practical Applications
4.2. Limitations and Future Works
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BC | Bagging Classifier |
CWT | Continuous Wavelet Transform |
DT | Decision Tree |
EFB | Exclusive Feature Bundling |
ET | Extra Trees |
FN | False Negative |
FP | False Positive |
GOSS | Gradient-based One-side Sampling |
GBDT | Gradient Boosting Decision Tree |
HF | High Fatigue |
IMU | Inertial Measurement Units |
IMNF | Instantaneous Mean Frequency |
k-NN | k-Nearest Neighbor |
ML | Machine Learning |
LOOCV | Leave-one-out Cross-validation |
LGB | Light Gradient Boosting |
LED | Light-emitting Diode |
LDA | Linear Discriminant Analysis |
LR | Logistic Regression |
LF | Low Fatigue |
MVC | Maximum Voluntary Contraction |
MNF | Mean Frequency |
MDF | Median Frequency |
MOF | Moderate Fatigue |
MF | Muscle Fatigue |
MSD | Musculoskeletal Disorders |
OFS | Optical Fiber Sensors |
PCA | Principal Component Analysis |
RF | Random Forest |
ROM | Range of Motion |
RPE | Rate of Perceived Exertion |
RMS | Root Mean Square |
SVM | Support Vector Machine |
sEMG | Surface Electromyography |
TP | True Positives |
Appendix A
Items | Fatigue Type |
---|---|
I feel fit | General |
Physically, I feel only able to do a little. | Physical |
I feel very active. | Reduced Activity |
I feel like doing all sorts of nice things. | Reduced Motivation |
I feel tired. | Reduced Activity |
I think I do a lot in a day. | Mental |
When I am doing something. I can keep my thoughts on it | Physical |
Physically, I can take on a lot. | Reduced Motivation |
I dread having to do things. | Reduced Activity |
I think I do very little in a day. | Mental |
I can concentrate well. | General |
I am rested. | Mental |
It takes a lot of effort to concentrate on things. | Physical |
Physically I feel I am in a bad condition. | Reduced Motivation |
I have a lot of plans. | General |
I tire easily. | Reduced Activity |
I get little done. | Reduced Motivation |
I don’t like doing anything. | Mental |
My thoughts easily wander. | Physical |
Physically, I feel I am in an excellent condition | General |
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Gender | Age (Years) | Weight (kg) | Height (cm) |
---|---|---|---|
Female | 24.7 ± 3.4 | 61.1 ± 15.4 | 163.4 ± 7.9 |
Male | 24.2 ± 2.3 | 73.3 ± 8.8 | 177.0 ± 7.1 |
N° | Device | Feature | Description | Reference |
---|---|---|---|---|
1–4 5–8 9–12 13–16 17–20 21–24 | IMU 1 | GyroX GyroY GyroZ AccX AccY AccZ | Mean, standard deviation, RMS value and amplitude calculated per elbow repetition cycle from the IMU located at the neck. The amplitude is calculated as the maximum minus the minimum value from the IMU located at the neck. | [61,62] |
25–28 29–32 33–36 37–40 41–44 45–48 | IMU 2 |
GyroX GyroY GyroZ AccX AccY AccZ | Mean, standard deviation, RMS value and amplitude calculated per elbow repetition cycle from the IMU located at the neck. The amplitude is calculated as the maximum minus the minimum value from the IMU located at the wrist. | [61,62] |
49–52 | FIB_mean FIB_std FIB_RMS FIB_ROM | Mean, standard deviation, RMS value and Range of Motion. The ROM is calculated as the maximum minus the minimum value from the elbow joint position signal | [66,67] | |
OFS | ||||
53–56 | FIB_tnorm FIB_MNF FIB_MDF FIB_IMNF | Normalized repetition duration, mean frequency, median frequency and instantaneous mean frequency of the elbow joint position signal | [68] | |
57–60 | EMG_mean EMG_std EMG_RMS EMG_Amp | Mean, standard deviation, RMS value and amplitude. The amplitude is calculated as the maximum minus the minimum value from the biceps brachii EMG signal | [11,69] | |
EMG Sensor | ||||
61–63 | EMG_MNF EMG_MDF EMG_IMNF | Mean frequency, median frequency and instantaneous mean frequency from the biceps brachii EMG signal | [64,65,70] |
Fatigue State | Number of Samples |
---|---|
Low Fatigue | 136 (36.6%) |
Moderate Fatigue | 104 (28.0%) |
High Fatigue | 132 (35.5%) |
Model | Hyperparameters | Accuracy | Precision | Recall | F-Score |
---|---|---|---|---|---|
LGBM | bagging_freq = 4 n_estimators = 100 min_child_samples = 8 num_leaves = 181 | 96.8 | 96.8 | 96.8 | 96.8 |
RF | n_estimators =1400 max_depth = 40 bootstrap = False | 96.2 | 96.3 | 96.2 | 96.2 |
BC | max_features = 0.7 base_estimator_max_depth = 20 n_estimators = 10 | 96.2 | 96.3 | 96.2 | 96.2 |
ET | criterion = log_loss max_features = auto min_samples_leaf = 1 min_samples_split = 2 | 94.6 | 94.7 | 94.6 | 94.6 |
DT | criterion = gini min_samples_leaf =1 min_samples_split =8 | 93.0 | 93.0 | 93.0 | 92.9 |
Sensors | Features | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|
EMG, IMUs, OFS | 5 | 91.7 | 91.6 | 91.7 | 91.6 |
7 | 93.5 | 93.5 | 93.5 | 93.5 | |
11 | 91.7 | 91.7 | 91.7 | 91.7 | |
16 | 95.9 | 95.9 | 95.9 | 95.9 | |
33 | 96.2 | 96.3 | 96.2 | 96.2 | |
EMG | 7 | 78.8 | 78.3 | 78.8 | 78.5 |
OFS | 8 | 86.6 | 86.9 | 86.6 | 86.7 |
IMU1 | 5 | 86.6 | 86.2 | 86.6 | 86.2 |
IMU2 | 4 | 87.9 | 87.7 | 87.9 | 87.6 |
IMUs | 10 | 92.2 | 92.1 | 92.2 | 92.1 |
IMUs, OFS | 13 | 95.4 | 95.4 | 95.4 | 95.4 |
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Otálora, S.; Segatto, M.E.V.; Monteiro, M.E.; Múnera, M.; Díaz, C.A.R.; Cifuentes, C.A. Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors. Sensors 2023, 23, 9291. https://doi.org/10.3390/s23229291
Otálora S, Segatto MEV, Monteiro ME, Múnera M, Díaz CAR, Cifuentes CA. Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors. Sensors. 2023; 23(22):9291. https://doi.org/10.3390/s23229291
Chicago/Turabian StyleOtálora, Sophia, Marcelo E. V. Segatto, Maxwell E. Monteiro, Marcela Múnera, Camilo A. R. Díaz, and Carlos A. Cifuentes. 2023. "Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors" Sensors 23, no. 22: 9291. https://doi.org/10.3390/s23229291
APA StyleOtálora, S., Segatto, M. E. V., Monteiro, M. E., Múnera, M., Díaz, C. A. R., & Cifuentes, C. A. (2023). Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors. Sensors, 23(22), 9291. https://doi.org/10.3390/s23229291