Combining RSSI and Accelerometer Features for Room-Level Localization
Abstract
:1. Introduction
- Testing the efficiency of accelerometer measurements in room-level localization task, which is actually a classification problem.
- Examining the performance of feature extraction from RSSI readings, based on the features suggested in [8].
- Assessing the performance of RSSI and accelerometer data together, in room-level localization, by combining them in an early level or in the results level. To achieve this, we applied several ensemble learning methods, which are not usually implemented in such tasks, but they are very common in wearable sensors’ analysis for problems like activity recognition or fall detection.
- We manipulated the initial dataset [11] in two different ways, which we later refer to as evaluation protocols, to apply the aforementioned framework. We finally compare the individual performance of RSSI and accelerometer features, with the performance of the concatenated features (early fusion) as well as the performance of the late fusion algorithms.
2. Related Work
3. Methods
- (1)
- Initialization of the population of chromosomes.
- (2)
- Selection of the part of the population that survive using the fitness function as a criterion.
- (3)
- Creation of a new generation of chromosomes through a combination of genetic operators: crossover and mutation.
- The crossover is a genetic operation used to combine two parents to create a new chromosome.
- The mutation is a genetic operation used to maintain diversity from one generation to the next.
- (4)
- Repetition of steps 2 and 3 until a termination condition is reached.
4. Results
4.1. Data
4.2. Experimental Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Features | |||
---|---|---|---|
Mean | Standard deviation | 25% quantile | Skewness |
Median | Minimum | 75% quantile | Kurtosis |
Variance | Maximum | Interquartile range |
Classifier | RSSI | Acc | DR Weighted Fusion | Accuracy Weighted Fusion | Early Fusion | Averaging | GA Weighted |
---|---|---|---|---|---|---|---|
KNN | 0.7506 | 0.4906 | 0.6622 | 0.7399 | 0.7466 | 0.6997 | 0.7614 |
LDA | 0.6769 | 0.5402 | 0.5898 | 0.6662 | 0.6099 | 0.5416 | 0.7185 |
RF | 0.7895 | 0.5201 | 0.7212 | 0.7668 | 0.7131 | 0.7439 | 0.7989 |
SVM | 0.7386 | 0.1367 | 0.5268 | 0.7265 | 0.5389 | 0.5979 | 0.7212 |
Stacking Algorithms | KNN | LDA | RF | SVM |
---|---|---|---|---|
SVM | 0.6769 | 0.6501 | 0.3539 | 0.4638 |
GBM | 0.7319 | 0.7721 | 0.7493 | 0.5550 |
Classifier | RSSI | Acc | DR Weighted Fusion | Accuracy Weighted Fusion | Early Fusion | Averaging | GA Weighted |
---|---|---|---|---|---|---|---|
KNN | 0.4447 | 0.2304 | 0.3502 | 0.4539 | 0.5184 | 0.3871 | 0.4677 |
LDA | 0.4954 | 0.4470 | 0.4908 | 0.5369 | 0.6198 | 0.5161 | 0.5922 |
RF | 0.4516 | 0.4055 | 0.4839 | 0.5138 | 0.5276 | 0.4792 | 0.5553 |
SVM | 0.5092 | 0.4217 | 0.3433 | 0.4147 | 0.5323 | 0.3433 | 0.4378 |
Stacking Algorithms | KNN | LDA | RF | SVM |
---|---|---|---|---|
SVM | 0.3249 | 0.1429 | 0.2419 | 0.3065 |
GBM | 0.4470 | 0.5392 | 0.4355 | 0.5691 |
Classifier | RSSI | Acc | DR Weighted Fusion | Accuracy Weighted Fusion | Early Fusion | Averaging | GA Weighted |
---|---|---|---|---|---|---|---|
KNN | 0.9526 | 0.9544 | 0.7807 | 0.9807 | 0.9561 | 0.9807 | 0.9860 |
LDA | 0.8877 | 0.8018 | 0.7211 | 0.9298 | 0.5579 | 0.8439 | 0.9333 |
RF | 0.9825 | 0.8105 | 0.6982 | 0.9912 | 0.9842 | 0.9842 | 0.9912 |
SVM | 0.9754 | 0.9456 | 0.4825 | 0.9509 | 0.9561 | 0.9509 | 0.6175 |
Stacking Algorithms | KNN | LDA | RF | SVM |
---|---|---|---|---|
SVM | 0.9649 | 0.9807 | 0.5782 | 0.7346 |
GBM | 0.9543 | 0.8875 | 0.9859 | 0.9684 |
Classifier | RSSI | Acc | DR Weighted Fusion | Accuracy Weighted Fusion | Early Fusion | Averaging | GA Weighted |
---|---|---|---|---|---|---|---|
KNN | 0.8372 | 0.4469 | 0.5448 | 0.8290 | 0.2414 | 0.7586 | 0.8524 |
LDA | 0.8717 | 0.5228 | 0.5559 | 0.7917 | 0.7131 | 0.6221 | 0.8855 |
RF | 0.8428 | 0.2869 | 0.4648 | 0.8303 | 0.4966 | 0.7393 | 0.8428 |
SVM | 0.8593 | 0.2234 | 0.5214 | 0.8497 | 0.2414 | 0.6497 | 0.8386 |
Stacking Algorithms | KNN | LDA | RF | SVM |
---|---|---|---|---|
SVM | 0.7793 | 0.8359 | 0.6455 | 0.6759 |
GBM | 0.7945 | 0.8276 | 0.6386 | 0.6703 |
Classifier | RSSI | Acc | DR Weighted Fusion | Accuracy Weighted Fusion | Early Fusion | Averaging | GA Weighted |
---|---|---|---|---|---|---|---|
KNN | 0.9390 | 0.3009 | 0.8573 | 0.9351 | 0.9364 | 0.8936 | 0.9429 |
LDA | 0.9610 | 0.3307 | 0.8832 | 0.9598 | 0.9572 | 0.9481 | 0.9715 |
RF | 0.9455 | 0.3929 | 0.7937 | 0.9351 | 0.9364 | 0.8716 | 0.9468 |
SVM | 0.9416 | 0.3942 | 0.7886 | 0.9274 | 0.9572 | 0.8690 | 0.9429 |
Stacking Algorithms | KNN | LDA | RF | SVM |
---|---|---|---|---|
SVM | 0.8872 | 0.9572 | 0.9183 | 0.9092 |
GBM | 0.9339 | 0.9585 | 0.9429 | 0.9092 |
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Tsanousa, A.; Xefteris, V.-R.; Meditskos, G.; Vrochidis, S.; Kompatsiaris, I. Combining RSSI and Accelerometer Features for Room-Level Localization. Sensors 2021, 21, 2723. https://doi.org/10.3390/s21082723
Tsanousa A, Xefteris V-R, Meditskos G, Vrochidis S, Kompatsiaris I. Combining RSSI and Accelerometer Features for Room-Level Localization. Sensors. 2021; 21(8):2723. https://doi.org/10.3390/s21082723
Chicago/Turabian StyleTsanousa, Athina, Vasileios-Rafail Xefteris, Georgios Meditskos, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2021. "Combining RSSI and Accelerometer Features for Room-Level Localization" Sensors 21, no. 8: 2723. https://doi.org/10.3390/s21082723