157
Aspects of Ambient Assisted Living and Its Applications
Decision-making—Artificial Intelligence (AI) planning can be applied for the daily
activity data and also on the complete task recorded earlier for deriving any conclusion.
This type of analysis on previously recorded data will facilitate in recognizing and decision-making in emergency situations. The following is an example for deriving knowledge
from the rule generated.
Subject(?x) ∧Location (?x, ?y) ∧MovementSensor(?s) ∧detectMovement(?, true) ∧inside(?s,?r)
∧HabitableSpaceInBuilding(?r) ∧haveConnection(?r,?c) ∧connectinWith(?c,?c1) ∧equal(?y,?c1)
⇒state(?x, Active) ∧location(?x,?r)
Spatial temporal reasoning—the spatial characteristics and temporal behavior of sensor
data give additional information about an activity. For example, after, before, meets, overlapped, started by, finishes, and during are some of the temporal relationships. Some of these
will be required for medication in health care while suggesting pills right after food, before
food, etc. Capturing these characteristics require special techniques and algorithms. The
algorithm, Jakkula’s TempAI recognizes nine intervals with such temporal relationship used
for prediction (Jakkula et al. 2007). Figure 8.15 states the temporal relationships such as before,
contains, overlaps, meets, and visualization of two activities X and Y with respect to time.
8.10 Software Engineering Approach for AAL Systems
Probabilistic Symbolic Model Checker (PRISM) is a tool used to build AAL system based
on dependability analysis (Kwiatkowska et al. 2004). This tool is highly successful for the
reason that it uses various probabilistic models like discrete time Markov chains (DTMCs),
continuous time markov chains (CTMCs), and Markov decision process (MDP). It can also
derive model based on state-based language reactive model formalisms. This tool also uses
temporal logic like Probabilistic Computational Tree Logic (PCTL) (Rodrigues et al. 2012).
Temporal relations
Visualization
Temporal relations
Visualization
X before Y
Y contains X
X overlaps Y
X meets Y
X equals Y
Y finished by X
X finishes Y
Y started by X
X starts Y
Y
Y
Y
Y
Y
Y
X
X
X
Y
X
X X
X
X
FIGURE 8.15
Temporal interval of two activities X and Y.
Aspects of Ambient Assisted Living and Its Applications
Decision-making—Artificial Intelligence (AI) planning can be applied for the daily
activity data and also on the complete task recorded earlier for deriving any conclusion.
This type of analysis on previously recorded data will facilitate in recognizing and decision-making in emergency situations. The following is an example for deriving knowledge
from the rule generated.
Subject(?x) ∧Location (?x, ?y) ∧MovementSensor(?s) ∧detectMovement(?, true) ∧inside(?s,?r)
∧HabitableSpaceInBuilding(?r) ∧haveConnection(?r,?c) ∧connectinWith(?c,?c1) ∧equal(?y,?c1)
⇒state(?x, Active) ∧location(?x,?r)
Spatial temporal reasoning—the spatial characteristics and temporal behavior of sensor
data give additional information about an activity. For example, after, before, meets, overlapped, started by, finishes, and during are some of the temporal relationships. Some of these
will be required for medication in health care while suggesting pills right after food, before
food, etc. Capturing these characteristics require special techniques and algorithms. The
algorithm, Jakkula’s TempAI recognizes nine intervals with such temporal relationship used
for prediction (Jakkula et al. 2007). Figure 8.15 states the temporal relationships such as before,
contains, overlaps, meets, and visualization of two activities X and Y with respect to time.
8.10 Software Engineering Approach for AAL Systems
Probabilistic Symbolic Model Checker (PRISM) is a tool used to build AAL system based
on dependability analysis (Kwiatkowska et al. 2004). This tool is highly successful for the
reason that it uses various probabilistic models like discrete time Markov chains (DTMCs),
continuous time markov chains (CTMCs), and Markov decision process (MDP). It can also
derive model based on state-based language reactive model formalisms. This tool also uses
temporal logic like Probabilistic Computational Tree Logic (PCTL) (Rodrigues et al. 2012).
Temporal relations
Visualization
Temporal relations
Visualization
X before Y
Y contains X
X overlaps Y
X meets Y
X equals Y
Y finished by X
X finishes Y
Y started by X
X starts Y
Y
Y
Y
Y
Y
Y
X
X
X
Y
X
X X
X
X
FIGURE 8.15
Temporal interval of two activities X and Y.