Improving Fall Detection Using an On-Wrist Wearable Accelerometer
| Autor: | Enrique de la Cal, José Ramón Villar, Camelia Chira, Samad Barri Khojasteh, Víctor M. González |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Feature extraction Wearable computer 02 engineering and technology Accelerometer Machine learning computer.software_genre lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Injury prevention 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Balance (ability) Artificial neural network elderly people monitoring business.industry wearable sensors 010401 analytical chemistry Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine fall detection 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Sensors, Vol 18, Iss 5, p 1350 (2018) Scopus Sensors (Basel, Switzerland) RUO. Repositorio Institucional de la Universidad de Oviedo instname Sensors; Volume 18; Issue 5; Pages: 1350 |
| ISSN: | 1424-8220 |
| Popis: | Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. |
| Databáze: | OpenAIRE |
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