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Methods Inf Med 2017; 56(01): 63-73
DOI: 10.3414/ME15-02-0010
Wearable Therapy
Schattauer GmbH

Social-aware Event Handling within the FallRisk Project

Femke De Backere
1   Information Technology Department (INTEC), Ghent University – iMinds, Ghent, Belgium
,
Jan Van den Bergh
2   Expertise center for Digital Media (EDM), Hasselt University – iMinds, Diepenbeek, Belgium
,
Sven Coppers
2   Expertise center for Digital Media (EDM), Hasselt University – iMinds, Diepenbeek, Belgium
,
Shirley Elprama
3   Studies on Media, Information and Technology (SMIT) – Vrije Universiteit Brussel – iMinds, Brussels, Belgium
,
Jelle Nelis
1   Information Technology Department (INTEC), Ghent University – iMinds, Ghent, Belgium
,
Stijn Verstichel
1   Information Technology Department (INTEC), Ghent University – iMinds, Ghent, Belgium
,
An Jacobs
3   Studies on Media, Information and Technology (SMIT) – Vrije Universiteit Brussel – iMinds, Brussels, Belgium
,
Karin Coninx
2   Expertise center for Digital Media (EDM), Hasselt University – iMinds, Diepenbeek, Belgium
,
Femke Ongenae
1   Information Technology Department (INTEC), Ghent University – iMinds, Ghent, Belgium
,
Filip De Turck
1   Information Technology Department (INTEC), Ghent University – iMinds, Ghent, Belgium
› Author AffiliationsThe iMinds FallRisk project is cofunded by iMinds (Interdisciplinary Institute for Technology), a research institute founded by the Flemish Government.
Further Information

Publication History

received: 01 November 2015

accepted in revised form: 09 November 2016

Publication Date:
22 January 2018 (online)

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Summary

Objectives: 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.

Keywords

Decision making - computer-assisted - decision support systems - information systems - usercomputer interface
 

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