11-04-2012, 12:51 PM
Mobile Care Systems (MoCaS)
[attachment=19896]
Introduction
Body Sensors Networks (BSN) are networks of wireless devices used for biological signals monitoring [1, 2]. All signals are collected by a Personal Mobile Hub (PMH), able to elaborate data and transmit them, on the basis of a predefined protocol (alarms, holter, monitor,…).
The “traditional” approach of BSN is based on “stupid sensors” only able to collect digital signal that are then elaborated on a PMH.
PMH can be implemented using mobile phones, palms or custom elaboration units. Data collected and elaborated by PMH are finally sent to a call center.
Mobile Care Systems (MoCaS) architecture has several innovative features with respect to traditional BSN networks, because it is based on the concept of an intelligent device.
In the present document we will offer an overview of MoCaS features.
. MoCaS architecture
MoCaS have been developed on the basis of a stable and common technology as Bluetooth. The system can be easily updated to different wireless networks, as Zigbee [3], that will allow to reduce power consumption. However, the compliance with a standard as Bluetooth actually allows a very easy integration with different hub platform, in order to redirect alarms and monitored signals to different peers (mobile telephone, PC, Palm, …).
MoCaS sensors
Physical activity, fall and motion
Imprecision in data and information gathered from and about our environment is either statistical (e.g., the outcome of a coin toss) or non-statistical (e.g., the physical activity and postural status). This latter type of uncertainty is called fuzziness; therefore, fuzzy models attempt to capture nonrandom imprecision.
The sets shown in figure 2 are a typical example of events and states characterized by fuzzy definitions and transitions. In order to classify different states, it is necessary to define the physical signals that characterize motion and posture (accelerometer data acquisition), and the features of these signals.
Fuzzy data analysis involves the assessment of objects, which are described by features. The values of these features are the data items to be analyzed [4]. Objects with similar features are grouped together in classes. In this context, the objects are persons in movement (physical activity) and the classes are stasis, movement and fall. The condition intense physical activity will be inferred indirectly, using a further euristic algorithm, not directly from fuzzy classification.
Galvanic skin response (GSR)
MoCaS devices mount leads for the measurement of galvanic skin response (GSR), or the impedance of the skin. The resistance of the skin can change for different reasons, for example wen a person performs physical or mental activities, and when emotional status changes. The GSR is a good indicator of the state of awareness of a person, and also does not require a high sampling rate [6]. The awareness or the state of attention is an important measurement for the assisted living services.
. MoCaS upgrades
As described in previous chapters, MoCaS devices have been developed in order to be the elements of an open system, able to interoperate in a standard environment. This architecture easily allows to extend functionalities and integrate devices in an open communications context. Localization and call center management are two features that have been taken into account, and are programmed as future upgrades of MoCaS project.
[attachment=19896]
Introduction
Body Sensors Networks (BSN) are networks of wireless devices used for biological signals monitoring [1, 2]. All signals are collected by a Personal Mobile Hub (PMH), able to elaborate data and transmit them, on the basis of a predefined protocol (alarms, holter, monitor,…).
The “traditional” approach of BSN is based on “stupid sensors” only able to collect digital signal that are then elaborated on a PMH.
PMH can be implemented using mobile phones, palms or custom elaboration units. Data collected and elaborated by PMH are finally sent to a call center.
Mobile Care Systems (MoCaS) architecture has several innovative features with respect to traditional BSN networks, because it is based on the concept of an intelligent device.
In the present document we will offer an overview of MoCaS features.
. MoCaS architecture
MoCaS have been developed on the basis of a stable and common technology as Bluetooth. The system can be easily updated to different wireless networks, as Zigbee [3], that will allow to reduce power consumption. However, the compliance with a standard as Bluetooth actually allows a very easy integration with different hub platform, in order to redirect alarms and monitored signals to different peers (mobile telephone, PC, Palm, …).
MoCaS sensors
Physical activity, fall and motion
Imprecision in data and information gathered from and about our environment is either statistical (e.g., the outcome of a coin toss) or non-statistical (e.g., the physical activity and postural status). This latter type of uncertainty is called fuzziness; therefore, fuzzy models attempt to capture nonrandom imprecision.
The sets shown in figure 2 are a typical example of events and states characterized by fuzzy definitions and transitions. In order to classify different states, it is necessary to define the physical signals that characterize motion and posture (accelerometer data acquisition), and the features of these signals.
Fuzzy data analysis involves the assessment of objects, which are described by features. The values of these features are the data items to be analyzed [4]. Objects with similar features are grouped together in classes. In this context, the objects are persons in movement (physical activity) and the classes are stasis, movement and fall. The condition intense physical activity will be inferred indirectly, using a further euristic algorithm, not directly from fuzzy classification.
Galvanic skin response (GSR)
MoCaS devices mount leads for the measurement of galvanic skin response (GSR), or the impedance of the skin. The resistance of the skin can change for different reasons, for example wen a person performs physical or mental activities, and when emotional status changes. The GSR is a good indicator of the state of awareness of a person, and also does not require a high sampling rate [6]. The awareness or the state of attention is an important measurement for the assisted living services.
. MoCaS upgrades
As described in previous chapters, MoCaS devices have been developed in order to be the elements of an open system, able to interoperate in a standard environment. This architecture easily allows to extend functionalities and integrate devices in an open communications context. Localization and call center management are two features that have been taken into account, and are programmed as future upgrades of MoCaS project.