A social- and context-aware multi-sensor platform is presented, which integrates information of fall detection systems and sensors at the home of the elderly, by using an ontology. 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.
Introduction
For elderly fall incidents are often life-changing events that might lead to
degradation or loss of autonomy. More than half of the elderly living in nursing homes
and about one third of the elderly living at home fall at least once a year [
The fall risks, e.g., impaired mobility and gait, are assessed by formal
caregivers with standardized tests on prede ned, long time intervals. As a result,
targeted measures and advice are formulated. Attempts to automate this
assessment and follow-up through domotic and monitoring systems are limited and
not integrated [
The FallRisk project1 aims to develop a social- and context-aware multisensor framework to: 1) automatically assess the potential fall risks and the compliance of the elderly to advice by monitoring his or her behavior, 2) reduce the amount of undetected calls and false alarms by combining the information gathered by the plethora of fall detection systems and sensors installed at the home of the elderly, and 3) automatically select the (in)formal caregivers to 1 http://www.iminds.be/en/research/overview-projects/p/detail/fallrisk-2
Motion sensors
Temp/Humidity/Light sensor
Datasources
Controllers MCD MCI,MCK
MCI,MCK
MCI Local Gateway
MCD
OCarePlatform whom an alarm or noti cation should be sent based on the current context. To realize this goal, the framework integrates the heterogeneous and voluminous raw data gathered by all the devices in an ontology. Based on this integrated view of the current context, more intelligent algorithms can be de ned. 2
General Architecture of the FallRisk system To detect falls and assess the fall risk, sensors are installed within the home of the elderly as shown in Figure 1. Three types of context information are captured: 1) the environment, e.g., by temperature, humidity and light sensors, 2) the activity level of the elderly, e.g., by passive infrared motion sensors, and 3) position information, e.g., by pressure sensors. Fall detection and risk assessment systems are also integrated. Finally, data is gathered from the elderly's smartphone. Note that, not all devices should be installed. Depending on the needs and preferences of the elderly, the most appropriate ones should be selected that complement each other and lead to a reliable set-up for continuous monitoring.
The raw Care Data (CD) generated by the devices is gathered on the Local Gateway at the residence. To save bandwidth, the gateway already processes some information, e.g., the video images. To provide a back-up plan when the connection to the servers is lost, the Local Gateway is able to do some rigorous analysis of the data. Finally, the gateway transforms CD into Meta Care Data (MCD) by enriching it with, e.g., timestamps, identi ers and location.
The Controllers manage the connections between the OCarePlatform and the clients providing MCD, i.e., Local Gateways, caregivers' smartphones and databases of the care organizations. The MCD is back-upped within the Datasources. These also store all static information related to the elderly and caregivers. The Controllers transform the MCD to Meta Care Information (MCI) by tagging it with one or more Meta Care Concepts (MCC). The OCarePlatform uses the MCC to identify the corresponding ontological concept such that the MCI can be correctly integrated into the ontology.
The MCI is sent to the OCarePlatform, which infers new Meta Care Knowledge (MCK) by using ontology-based reasoning. Derived knowledge, concerning contextual information and fall estimation and detection, is sent back to the integratedInto isObservationOf Sensorboard isPartOf Sensor
System
Device
FallDetectionSystem xsd:float
hasValue
Actuator
The OCarePlatform facilitates the intelligent and coordinated integration,
analysis, combination and e cient usage of the large amount of MCI sent by the
Controllers by using ontologies. To model the knowledge pertaining to fall
detection and risk assessment, the Ambient-Aware Continuous Care Ontology
(ACCIO) [
As shown in Figure 3, MCI enters the OCarePlatform through the Context Provider Services, which transform it to ontology individuals by analyzing the associated MCCs. As these map on ontological concepts, the Context Provider Services know which type of individuals need to be created and how they should be created by analyzing the axioms de ned in the ontologies.
The OCarePlatform is developed as a modular platform, consisting of an extensible set of MCI Services. These are the brains of the platform. They process the large amount of data in an e cient and manageable manner. Each service has a speci c task, which can be implemented by 1) specifying axioms in the ontology to classify the incoming information and link it to appropriate action, 2) adopting rule engines to perform more complex analyses, and 3) integrating proprietary algorithms. Some example services are shown in Figure 3.
Consequently, there is a need for an intelligent ltering system, capable of
sending only that data to the MCI Services in which they are interested at that
time. For this, the Semantic Communication Bus SCB [
Stairs Monitoring MCI Service
Context Interpreter Reasoner OntoCloorgeies import Domain
Specific
Ontologies publishMMCCKI, pMuCshI, MCK
Context Manager pusbeltifsihlteMr CruIl,eMs&CK MCpI,usMhCK ...
Fall Detection MCI Service
Context Interpreter Reasoner OntoCloorgeies import Domain
Specific
Ontologies publishMMCCKI, pMuCshI, MCK
Context Manager pusbeltifsihltMer CruIl,eMs&CK MCpI,usMhCK Context Disseminator
Core Ontologies
Cache publish MCI
Context Manager
Context Provider Services in, by de ning ontological ltering rules and registering them with the Context Disseminator. This allows to reduce the amount of data that is forwarded to the MCI Services, which prevents them from being ooded with huge amounts of data. It also facilitates an agile approach, where new services can easily be deployed or duplicated for scalability and redundancy. For example, the Fall Risk MCI Service registers the following rule:
Event and hasContext some ((Action and (isPerformedBy some (hasRole some Patient)))
LichtIntensityObservation)
It is important to note that all conclusions, called MCK, drawn by the MCI services are put back on the SCB. In this way, conclusions drawn by one MCI service can be used by a second MCI service as additional situational information. As such, the OCarePlatform supports the composition of complex services from a set of smaller services in a loosely coupled manner. The simple services perform speci c reasoning tasks in parallel and notify their conclusions to other services, which have expressed an interest in this kind of information. Acknowledgment FallRisk is funded by iMinds and IWT and involves COMmeto, Televic Healthcare, TP Vision, Verhaert and Wit-Gele Kruis Limburg.