=Paper=
{{Paper
|id=Vol-1114/Poster_Backere
|storemode=property
|title=Social-aware and Context-aware Multi-sensor Fall Detection Platform
|pdfUrl=https://ceur-ws.org/Vol-1114/Poster_Backere.pdf
|volume=Vol-1114
|dblpUrl=https://dblp.org/rec/conf/swat4ls/BackereOAHVAT13
}}
==Social-aware and Context-aware Multi-sensor Fall Detection Platform==
https://ceur-ws.org/Vol-1114/Poster_Backere.pdf
Social-aware and context-aware multi-sensor fall
detection platform
Femke De Backere, Femke Ongenae, Floris Van den Abeele, Jeroen Hoebeke,
Stijn Verstichel, Ann Ackaert, and Filip De Turck
Department of Information Technology (INTEC), Ghent University - iMinds,
Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium
Femke.DeBackere@intec.ugent.be
Abstract. A social- and context-aware multi-sensor platform is pre-
sented, which integrates information of fall detection systems and sen-
sors 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.
Keywords: Fall Detection & Risk Assessment, Semantic, Context-aware
1 Introduction
For elderly fall incidents are often life-changing events that might lead to degra-
dation 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 [4]. 10
to 15% of them suffer severe injuries [6]. Psychological consequences can also not
be underestimated. Reliable fall detection and prevention are thus necessities.
The fall risks, e.g., impaired mobility and gait, are assessed by formal care-
givers with standardized tests on predefined, long time intervals. As a result,
targeted measures and advice are formulated. Attempts to automate this assess-
ment and follow-up through domotic and monitoring systems are limited and
not integrated [6]. Several systems exist to detect falls, e.g., Personal Alarm Sys-
tems (PAS), accelerometers, video cameras and micro arrays. However, the PAS
is often not worn and false alarms and undetected falls regularly occur [1, 3, 6].
When the help desk is notified of a fall, a predefined ordered list is used to assign
it. The current context of the (in)formal caregivers, e.g., location or availability,
is thus not taken into account, resulting in unnecessary delays and distractions.
The FallRisk project1 aims to develop a social- and context-aware multi-
sensor 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
2
Temp/Humidity/Light sensor
Motion sensors
Datasources
MCI, MCK
MCD
MCI
OCarePlatform
Local Gateway
Controllers
D
MC MCI, MCK
CD
M
Patient Residence
Pressure Formal Informal
sensors Caregivers Caregivers
Fig. 1: General architecture of the fall detection platform
whom an alarm or notification 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 defined.
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, identifiers 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 Data-
sources. These also store all static information related to the elderly and care-
givers. 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 Knowl-
edge (MCK) by using ontology-based reasoning. Derived knowledge, concerning
contextual information and fall estimation and detection, is sent back to the
3
integratedInto System
isObservationOf isPartOf xsd:float
hasValue
Sensorboard Sensor Device FallDetectionSystem
Actuator
Pressure Motion Humidity Light Temperature Call Portable TV Camera Chair Camera Micro PAS Light
Sensor Sensor Sensor Sensor Sensor Button Phone Lift System Array hasUnit
hasPrevious hasUnit xsd:string xsd:string
Symptom Observation Event hasContext Entity
Observation hasValue xsd:float
hasFault hasSymptom
has External CallButton Light Pressure Fall Humidity Motion
Fault Solution Solution
Temperature Status Intensity Status Observation Observation Observation
Observation Observation Observation Observation hasLocation
Furniture hasLocation Status hasStatus
Location hasProfile
Profile
belongsTo hasLocation
System
Bed Status
Zone hasCentre Coordinate BasicProfile RiskProfile
xsd:int Coordinate Person
Status
hasDegree Staircase Room Hallway
Biological Sociological Psychological Medical Behavioral Task
Trust Person Profile Profile Profile RiskProfile RiskProfile Status
Relationship belongsTo hasRole associated
Gender Nationality Language Impatient FallRisk Action
hasTrustRelationship Role
hasStatus
ownedBy hasCompetence
belongsTo Process
Informal inNeedOf hasStatus
Patient Family Formal Competence
Competence Task
Caregiver Caregiver hasPriority Action
Nurse Doctor Priority assignedTo PlannedTask
requiresAction UnplannedTask TakingStairs
performedBy
Notification hasReason FallReason Reason hasReason Call
Fig. 2: Prevalent concepts of the ambient-aware continuous care ontology for fall
risk estimation and fall detection
Controllers. They are responsible for notifying the correct caregiver(s) or the
emergency response center based on the derived knowledge.
3 The OCarePlatform
The OCarePlatform facilitates the intelligent and coordinated integration, anal-
ysis, combination and efficient usage of the large amount of MCI sent by the
Controllers by using ontologies. To model the knowledge pertaining to fall de-
tection and risk assessment, the Ambient-Aware Continuous Care Ontology (AC-
CIO) [5], was extended as shown in Figure 2.
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 defined 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 efficient and manageable manner. Each service
has a specific 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 filtering 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 [2] was designed, which
uses the extended ACCIO ontology to filter the data based on its semantics,
instead of on syntactical text patterns. Ontological data is published onto the
SCB by Context Provider Services by using the Context Manager. The MCI
Services use this Context Manager to specify the context they are interested
4
Stairs Monitoring MCI Service Fall Detection MCI Service
Context Interpreter Context Interpreter
Reasoner Core Reasoner Core
Notification MCI Ontologies Ontologies
Service import
import
Application Domain Domain
Logic Specific Specific
Ontologies Ontologies
publish push MCI, publish MCI, push
... publish MCI, push
MCI, MCK MCK MCK MCI, MCK MCK MCI, MCK
Context Manager Context Manager Context Manager
set filter rules & push set filter rules & push set filter rules & push
publish MCI, MCK MCI, MCK publish MCI, MCK MCI, MCK publish MCI, MCK MCI, MCK
Semantic Core Ontologies
Communication Context Disseminator Cache
Bus (SCB)
publish MCI Context Manager Context Provider Services MCI
Fig. 3: Architecture of the OCarePlatform
in, by defining ontological filtering 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 flooded 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 informa-
tion. 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 specific 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 COM-
meto, Televic Healthcare, TP Vision, Verhaert and Wit-Gele Kruis Limburg.
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