Elderly fall detection using data classification on a portable embedded system. Rosero-Montalvo, P., Peluffo-Ordonez, D., Godoy, P., Ponce, K., Rosero, E., Vasquez, C., Cuzme, F., Flores, S., & Mera, Z., A. In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), pages 1-4, 10, 2017. IEEE. ![Elderly fall detection using data classification on a portable embedded system [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The area of research on the detection of falls in the elderly allows to prevent major ailments to a person and not receiving timely medical attention. Although different systems have been proposed for the detection of falls, there are some open problems such as: cost, computational load, precision, portability, among others. This paper presents an alternative approach based on the acquisition of speed variation of the person on the X, Y and Z axes using an accelerometer and machine learning techniques. Since the information acquired by the sensor is very variant, with noise and high volume of data, a prototype selection stage is carried out using confidence intervals and techniques of Leaving-One-Out. Subsequently, automatic detection is performed using the K-nearest neighbors (K-NN) classifier. As a result of fall detection 95% accuracy is achieved in experiments from 5 trials and already used in reality by an older adult, the system has a time of 30 ms for position selection and the detection of drop is maintained in a 92% right.
@inproceedings{
title = {Elderly fall detection using data classification on a portable embedded system},
type = {inproceedings},
year = {2017},
keywords = {Embedded system,Fall detection,Knn,Prototype selection},
pages = {1-4},
websites = {http://ieeexplore.ieee.org/document/8247529/},
month = {10},
publisher = {IEEE},
id = {79255823-2655-3a0d-afbe-3b5f9f49c180},
created = {2022-01-26T03:00:52.814Z},
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citation_key = {Rosero-Montalvo2017a},
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abstract = {The area of research on the detection of falls in the elderly allows to prevent major ailments to a person and not receiving timely medical attention. Although different systems have been proposed for the detection of falls, there are some open problems such as: cost, computational load, precision, portability, among others. This paper presents an alternative approach based on the acquisition of speed variation of the person on the X, Y and Z axes using an accelerometer and machine learning techniques. Since the information acquired by the sensor is very variant, with noise and high volume of data, a prototype selection stage is carried out using confidence intervals and techniques of Leaving-One-Out. Subsequently, automatic detection is performed using the K-nearest neighbors (K-NN) classifier. As a result of fall detection 95% accuracy is achieved in experiments from 5 trials and already used in reality by an older adult, the system has a time of 30 ms for position selection and the detection of drop is maintained in a 92% right.},
bibtype = {inproceedings},
author = {Rosero-Montalvo, P.D. and Peluffo-Ordonez, D.H. and Godoy, Pamela and Ponce, K. and Rosero, E.A. and Vasquez, C.A. and Cuzme, F. and Flores, S.C and Mera, Z. A.},
doi = {10.1109/ETCM.2017.8247529},
booktitle = {2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM)}
}
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