Next Article in Journal
Pressure-Sensitive Nano-Sheet for Optical Pressure Measurement
Previous Article in Journal
Projections of IoT Applications in Colombia Using 5G Wireless Networks
 
 
Article

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

1
Department of Neurology, Leipzig University, 04103 Leipzig, Germany
2
Department of Neuromorphic Information Processing, Leipzig University, 04009 Leipzig, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Angelo Maria Sabatini and Lorenzo Scalise
Sensors 2021, 21(21), 7166; https://doi.org/10.3390/s21217166
Received: 24 August 2021 / Revised: 12 October 2021 / Accepted: 25 October 2021 / Published: 28 October 2021
(This article belongs to the Section Biomedical Sensors)
Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications. View Full-Text
Keywords: fall detection; machine learning; SVM; kNN; random forest; older adults; cross-dataset validation fall detection; machine learning; SVM; kNN; random forest; older adults; cross-dataset validation
Show Figures

Figure 1

MDPI and ACS Style

Alizadeh, J.; Bogdan, M.; Classen, J.; Fricke, C. Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults. Sensors 2021, 21, 7166. https://doi.org/10.3390/s21217166

AMA Style

Alizadeh J, Bogdan M, Classen J, Fricke C. Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults. Sensors. 2021; 21(21):7166. https://doi.org/10.3390/s21217166

Chicago/Turabian Style

Alizadeh, Jalal, Martin Bogdan, Joseph Classen, and Christopher Fricke. 2021. "Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults" Sensors 21, no. 21: 7166. https://doi.org/10.3390/s21217166

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop