A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.
- DOI
- 10.3390/s21030938
- Published
- 2021 Jan 30
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s21030938,
title = {A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.},
author = {Zurbuchen N and Wilde A and Bruegger P},
year = {2021},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s21030938},
url = {https://doi.org/10.3390/s21030938}
}RIS
TY - JOUR TI - A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection. AU - Zurbuchen N AU - Wilde A AU - Bruegger P PY - 2021 JO - Sensors (Basel, Switzerland) DO - 10.3390/s21030938 UR - https://doi.org/10.3390/s21030938 ER -
APA
N, Z., A, W., & P, B. (2021). A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s21030938
Source records
- pubmed · retrieved 2026-09-27T15:00:46.071Z