Tesi etd-04102010-091509 |
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Tipo di tesi
Tesi di laurea specialistica
Autore
COLA, GUGLIELMO
URN
etd-04102010-091509
Titolo
An innovative approach to false alarm recognition in fall detection systems
Dipartimento
INGEGNERIA
Corso di studi
INGEGNERIA INFORMATICA
Relatori
relatore Prof. Corsini, Paolo
relatore Dott. Vecchio, Alessio
relatore Prof. Avvenuti, Marco
relatore Dott. Vecchio, Alessio
relatore Prof. Avvenuti, Marco
Parole chiave
- fall detection systems
- healthcare
- wireless sensor networks
Data inizio appello
06/05/2010
Consultabilità
Non consultabile
Data di rilascio
06/05/2050
Riassunto
Falls are a mayor cause of injury deaths and injury-related hospitalization among people older than 65 years. Even non-injurious falls can be really dangerous, as it has been shown that many elderly people lack the ability to stand up and remain on the ground for even longer than an hour. This is the so called "long-lie", and it has been proved to have devastating effects on health. At the same time, fall related admission of older adults are a significant burden to the health services world wide.
Therefore, in the latest years there has been a great interest on fall detection systems by the healthcare industry. Many attempts to solve this problem using wearable devices has been already made: prevalent methods use a fixed accelerometer threshold to isolate falls from the "activities of daily living" (ADL). This approach does not succeed in distinguishing actual falls from certain fall-like activities such as sitting down or lying quickly, and causes frequent false alarms. In order to improve the detection accuracy, some researchers propose to combine linear acceleration obtained from accelerometers with gyroscope measurement of angular velocity. Unfortunately gyroscopes have a really negative impact on device battery lifetime. Body orientation has also been used to improve detection, but it is not very useful as there is no clear connection between posture and fall.
In this work we present a novel method for false alarm recognition and filtering, which leads to a significantly improved level of detection accuracy. Our approach features low computational costs and real time response. Moreover, it requires only an accelerometer placed at user's waist, achieving a high degree of usability.
Therefore, in the latest years there has been a great interest on fall detection systems by the healthcare industry. Many attempts to solve this problem using wearable devices has been already made: prevalent methods use a fixed accelerometer threshold to isolate falls from the "activities of daily living" (ADL). This approach does not succeed in distinguishing actual falls from certain fall-like activities such as sitting down or lying quickly, and causes frequent false alarms. In order to improve the detection accuracy, some researchers propose to combine linear acceleration obtained from accelerometers with gyroscope measurement of angular velocity. Unfortunately gyroscopes have a really negative impact on device battery lifetime. Body orientation has also been used to improve detection, but it is not very useful as there is no clear connection between posture and fall.
In this work we present a novel method for false alarm recognition and filtering, which leads to a significantly improved level of detection accuracy. Our approach features low computational costs and real time response. Moreover, it requires only an accelerometer placed at user's waist, achieving a high degree of usability.
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