Towards a social and context-aware multi-sensor fall detection and risk assessment platform

• We propose a new extensible and adaptable cloud-based fall detection framework.
• An ontology-based approach is used to model the situation of patient and caregivers.
• The captured information from the sensors is combined with contextual information.
• Existing fall detection systems can easily be integrated to improve accuracy.
• Different combinations of fall detection systems and sensors can be used.

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms.In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task.The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors.