RT journal article
T1 A Wearable Fall Detection System Based on Body Area Networks
A1 La Blunda, Luigi
A1 Gutiérrez Madroñal, Lorena
A1 Wagner, Matthias F.
A1 Medina Bulo, María Inmaculada
A2 Ingeniería Informática
K1 Acceleration
K1 Electrocardiography
K1 Biomedical monitoring
K1 Body area networks
K1 Prototypes
K1 Reliability
K1 Body area network
K1 eHealth
K1 fall detection
K1 human activity
K1 wireless smart sensor networks
AB Falls can have serious consequences for people, leading to restrictions in mobility or, in the worst case, to traumatic-based cases of death. To provide rapid assistance, a portable fall detection system has been developed that is capable of detecting fall situations and, if necessary, alerting emergency services without any user interaction. The prototype is designed to facilitate reliable fall detection and to classify several fall types and human activities. This solution represents a life-saving service for every person that will significantly improve assistance in the case of fall events, which are a part of daily life. Additionally, this approach facilitates independent system operation, since the system does not depend on sensor or network units located within a building structure. This article also introduces fall analysis. To guarantee functional safety, a hazard analysis method named system-theoretic accident model and processes (STAMP) is applied.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC,
SN 2169-3536
YR 2020
FD 2020
LK http://hdl.handle.net/10498/24083
UL http://hdl.handle.net/10498/24083
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026