Workout Detection by Wearable Device Data Using Machine Learning
Abstract
:1. Introduction
2. Method
2.1. Experimental Protocol
2.2. Data Analysis and Machine Learning Algorithms
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classifier | Hyperparameter | |
---|---|---|
RF | n_estimators | 16, 32, 64, 128, 256, 512, 1024 |
max_depth | 16, 32, 64, 128, 256, 512, 1024 | |
criterion | gini | |
SVM | C | 0.001, 0.01, 0.1, 1, 10, 100, 1000 |
gamma | 0.001, 0.01, 0.1, 1, 10, 100, 1000 | |
KNN | n_neighbors | 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 |
Classifier | Recall | Precision | F-Score |
---|---|---|---|
RF | 0.962 ± 0.023 | 0.963 ± 0.020 | 0.963 ± 0.021 |
SVM | 0.961 ± 0.023 | 0.962 ± 0.022 | 0.962 ± 0.023 |
KNN | 0.886 ± 0.117 | 0.893 ± 0.094 | 0.886 ± 0.106 |
Classifier: RF | Results of Detection | |||
---|---|---|---|---|
State | Workout | Awake | Sleep | |
Actual | Workout | 3156 | 260 | 0 |
Awake | 131 | 3285 | 0 | |
Sleep | 0 | 0 | 3416 | |
Classifier: SVM | Results of detection | |||
State | Workout | Awake | Sleep | |
Actual | Workout | 3167 | 249 | 0 |
Awake | 140 | 3276 | 0 | |
Sleep | 2 | 5 | 3409 |
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Yoshida, Y.; Yuda, E. Workout Detection by Wearable Device Data Using Machine Learning. Appl. Sci. 2023, 13, 4280. https://doi.org/10.3390/app13074280
Yoshida Y, Yuda E. Workout Detection by Wearable Device Data Using Machine Learning. Applied Sciences. 2023; 13(7):4280. https://doi.org/10.3390/app13074280
Chicago/Turabian StyleYoshida, Yutaka, and Emi Yuda. 2023. "Workout Detection by Wearable Device Data Using Machine Learning" Applied Sciences 13, no. 7: 4280. https://doi.org/10.3390/app13074280