Abstract
The current machine learning algorithms in fall detection, especially those that use a sliding window, have a high com-putational cost because they need to compute the features from almost all samples. This computation causes energy drain and means that the associated wearable devices re-quire frequent recharging, making them less usable. This study proposes a cascade approach that reduces the compu-tational cost of the fall detection classifier. To examine this approach, accelerometer data from 48 subjects performing a combination of falls and ordinary behaviour is used to train 3 types of classifier (J48 Decision Tree, Logistic Regression, and Multilayer Perceptron). The results show that the cas-cade approach significantly reduces the computational cost both for learning the classifier and executing it once learnt. Furthermore, the Multilayer Perceptron achieves the high-est performance with precision of 93.5%, recall of 94.2%, and f-measure of 93.5%.
Original language | English |
---|---|
Title of host publication | MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies |
Editors | Akram Alomainy, Yang Hao, William Whittow, Konstantina S. Nikita, Clive G. Parini |
Place of Publication | Brussels |
Publisher | ICST |
Pages | 94-99 |
Number of pages | 6 |
ISBN (Electronic) | 9781631900884 |
DOIs | |
Publication status | Published - 22 Dec 2015 |
Event | 5th EAI International Conference on Wireless Mobile Communication and Healthcare: Transforming Healthcare through Innovations in Mobile and Wireless Technologies - London, United Kingdom Duration: 14 Oct 2015 → 16 Oct 2015 Conference number: 5 https://mobihealth.eai-conferences.org/2015/index.html |
Publication series
Name | MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies |
---|
Conference
Conference | 5th EAI International Conference on Wireless Mobile Communication and Healthcare |
---|---|
Abbreviated title | MOBIHEALTH 2015 |
Country/Territory | United Kingdom |
City | London |
Period | 14/10/15 → 16/10/15 |
Internet address |
Bibliographical note
This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.Keywords
- Cascade classifier
- Com-putational cost
- Fall detection
- Machine Learning
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Health Informatics