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G. Fersi
in healthcare to monitor patients. The Smart Homes for All (SM4All) middleware framework [13] has been proposed to help people with special needs in
their homes. This middleware integrates multiple protocols such as UPnP into
the OSGi framework and is able to interoperate with devices employing Zigbee, Bluetooth. This allows heterogeneous devices to connect dynamically and
interact with each other in person-centric surrounding.
The main objective of Uranus middleware [16] is to afford Ambient Assisted
Living (AAL) for users with vital signs monitoring. This ensures a rapid prototyping for multiple applications working on healthcare and users wellness.
Authors presented two case studies that have been tested using Uranus middleware. In the first, the oxygen level in the blood of a chronically ill patient is
monitored at his home. To fulfill the requirements of this case study, an oximeter
should be attached to the patient to measure the oxygen level and send it to a
smart phone which transmits it to the doctor. The second case study aims to
monitor patients that should be injected with radioactive substance. This monitoring alerts nurses in the case of patient’s complications after injection and also
supervise the radiation level in order to specify the convenient examination time
(Each examination type requires a specific radiation level to deliver the accurate
results). To achieve this, each patient is equipped with an RFID tag, a PDA and
an ECG sensor. These equipments in addition to the service discovery ensured by
the middleware enable to monitor the patient’s heart beats. The patient location
is updated when he moves from a room to another in order to track his status
(still waiting, in the examination state, injected and awaiting that the radiation
level reduces). These events are traduced using semantic information.
The contribution [9] is mainly used in sleep monitoring and bedsore prevention. The patient’s positions in the bed are specified and classified according to
the collected RSSI of the sensors using SVM classification method. These positions give an idea about the patient’s sleep and can prevent from bedsore risks.
This middleware helps the caregivers and eases their job by keeping track of
the patient’s position in the bed and decides when and how the patient should
change his position to avoid bedsores. It is made up of two layers. The middleware [17], is able to monitor and offer assistance to disabled people. The system
functionalities are dispatched and divided into independent services. An ECG
sensor monitors the heart activity.
2.5 Event-Driven Middleware
In the event-driven middleware, all the middleware functionnalities are based on
events going from events production to the reaction to events. Authors in [18]
consider that event driven middleware is suitable to the context of healthcare,
since the sensors reading according to the patient’s status and/or activity change
over the time. Also, the majority of medical devices work according to the event
driven process. For example, when the heartbeats rate exceeds a predefined
value, a notification is triggered. Furthermore, the event driven process reinforces
the data abstraction that is needed to ensure applications interoperability. The
proposed middleware is dedicated for smartphone like devices that are compelled
G. Fersi
in healthcare to monitor patients. The Smart Homes for All (SM4All) middleware framework [13] has been proposed to help people with special needs in
their homes. This middleware integrates multiple protocols such as UPnP into
the OSGi framework and is able to interoperate with devices employing Zigbee, Bluetooth. This allows heterogeneous devices to connect dynamically and
interact with each other in person-centric surrounding.
The main objective of Uranus middleware [16] is to afford Ambient Assisted
Living (AAL) for users with vital signs monitoring. This ensures a rapid prototyping for multiple applications working on healthcare and users wellness.
Authors presented two case studies that have been tested using Uranus middleware. In the first, the oxygen level in the blood of a chronically ill patient is
monitored at his home. To fulfill the requirements of this case study, an oximeter
should be attached to the patient to measure the oxygen level and send it to a
smart phone which transmits it to the doctor. The second case study aims to
monitor patients that should be injected with radioactive substance. This monitoring alerts nurses in the case of patient’s complications after injection and also
supervise the radiation level in order to specify the convenient examination time
(Each examination type requires a specific radiation level to deliver the accurate
results). To achieve this, each patient is equipped with an RFID tag, a PDA and
an ECG sensor. These equipments in addition to the service discovery ensured by
the middleware enable to monitor the patient’s heart beats. The patient location
is updated when he moves from a room to another in order to track his status
(still waiting, in the examination state, injected and awaiting that the radiation
level reduces). These events are traduced using semantic information.
The contribution [9] is mainly used in sleep monitoring and bedsore prevention. The patient’s positions in the bed are specified and classified according to
the collected RSSI of the sensors using SVM classification method. These positions give an idea about the patient’s sleep and can prevent from bedsore risks.
This middleware helps the caregivers and eases their job by keeping track of
the patient’s position in the bed and decides when and how the patient should
change his position to avoid bedsores. It is made up of two layers. The middleware [17], is able to monitor and offer assistance to disabled people. The system
functionalities are dispatched and divided into independent services. An ECG
sensor monitors the heart activity.
2.5 Event-Driven Middleware
In the event-driven middleware, all the middleware functionnalities are based on
events going from events production to the reaction to events. Authors in [18]
consider that event driven middleware is suitable to the context of healthcare,
since the sensors reading according to the patient’s status and/or activity change
over the time. Also, the majority of medical devices work according to the event
driven process. For example, when the heartbeats rate exceeds a predefined
value, a notification is triggered. Furthermore, the event driven process reinforces
the data abstraction that is needed to ensure applications interoperability. The
proposed middleware is dedicated for smartphone like devices that are compelled