title: Social-aware Event Handling within the FallRisk Project.
abstract: OBJECTIVE
  With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way.
  
  
  METHODS
  The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account.
  
  
  RESULTS
  The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process.
  
  
  CONCLUSIONS
  The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.
authors.affiliation: Femke De Backere, Department of Information Technology, Internet Based Communication Networks and Services (IBCN), Ghent University - iMinds, Technologiepark 15, B-9052 Gent, Belgium, E-mail: femke.debackere@intec.UGent.be.
authors.forename: Femke
authors.initials: F
authors.lastname: De Backere
authors.forename: Jan
authors.initials: J
authors.lastname: Van den Bergh
authors.forename: Sven
authors.initials: S
authors.lastname: Coppers
authors.forename: Shirley
authors.initials: S
authors.lastname: Elprama
authors.forename: Jelle
authors.initials: J
authors.lastname: Nelis
authors.forename: Stijn
authors.initials: S
authors.lastname: Verstichel
authors.forename: An
authors.initials: A
authors.lastname: Jacobs
authors.forename: Karin
authors.initials: K
authors.lastname: Coninx
authors.forename: Femke
authors.initials: F
authors.lastname: Ongenae
authors.forename: Filip
authors.initials: F
authors.lastname: De Turck
mesh_terms: D000058:Accidental Falls; D000368:Aged; D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D007398:Interpersonal Relations; D012307:Risk Factors; D014584:User-Computer Interface
keywords: Decision making; computer-assisted; decision support systems; information systems; user-computer interface