Prior knowledge, or context information, is the knowledge about the environment, the users and the relations between them we have prior to the present moment. In the context of activity recognition, this is the knowledge about the user activities and their relations to the environment or other users that is available prior to receiving the sensor data from which the actions have to be inferred. Additionally, it contains the information about the problem domain6. When creating a human behaviour model, this knowledge is incorporated into the model in order to improve the process of activity recognition and / or to provide additional information about the nature of the user actions.
As mentioned in the previous section, in the recent years there is increasing interest in using statistical methods for activity recognition. Thus the question of prior knowledge’s importance arises. Do we really need it or can we rely only on the sensor data?
If we consider situation where the sensor data describes human behaviour in a particular domain and is collected with the same type of sensors, then a pattern extraction methods could be sufficient for learning the system to recognise future human activities. Especially, when as- suming that humans are creatures of habit and exhibit certain behaviour patterns [22]. However, even changing the sensors type could be a problem for recognising the activity patterns. Even worse, a change in the domain would make activity recognition more difficult if not hardly pos- sible. The reason for this is that by using only sensor data, a learned model is highly dependent on the observed data, so it will be difficult to use it in a different from the observed situation.
On the other hand, a human behaviour model making use of the prior knowledge could be more abstract and flexible, so that it can be used in various situations7and domains by utilising different types of domain knowledge. Another problem that the use of prior knowledge solves, is arriving at suboptimal solution. Geisler gives the following example with a first person shooter in Quake: when relying only on observation data to learn, it is possible that there is shooting only in 5% of the observations. Relying only on observations, the shooter learns not to shoot, thus arriving at an undesirable problem solution [53]. Of course, that also could be solved with selecting the appropriate type of training data, but as a whole it shows that one could rely on the model to cope only with situations it has already seen in the data. This problem too can be solved with obtaining more training data that however is expensive in terms of resources 6Here the meaning of domain complies with that of domain in knowledge representation where it is just some
part of the world about which we wish to express some knowledge [123, p. 300].
7Here situation complies with the definition given by Russell and Norvig, where situation is the initial state of
the world in a given domain before the agents begin performing their actions in order to achieve a goal [123, p. 388].
and time. Applying prior knowledge, on the other hand, could ensure that the model is doing whatever it is specified to do and without the additional costs for training data8.
As hinted above, another issue with statistical methods is the expensive training data. In the recent years the sensor data to be analysed is increasing until we have come to the point where we have huge amount of observations and the process of analysing it becomes tedious and slow or sometimes even impossible [65]. A way to avoid this problem could be to employ prior knowledge that will replace the needed data with the expert knowledge of the model designer. This does not necessarily mean that the time needed for creating successful model will be shortened, but it will reduce the need of involving additional manpower for obtaining the needed amount of training data and the additional storage needed for this data.
In general, prior knowledge can be avoided in specific situations and only the sensor data can be used for model learning [5, 1, 117]. However, for applying such model on a broader spectrum of activity situations without the need of additional training data, as well as avoiding arriving at a suboptimal solutions because of insufficient training data, the incorporation of prior knowledge could be preferred.
1.5.1
Types of prior knowledge
Prior knowledge can come in different forms and from different sources. Here we propose a categorisation of prior knowledge into three groups based on the knowledge incorporated in different models.
Prior knowledge based on cognitive psychology: Cognitive psychology is the study of how people perceive, learn, remember, and think about information [139, p. 2]. Prior knowl- edge based on cognitive psychology consists of all the internal human states such as stress, emotions, perceptions etc. Such type of knowledge is important because cog- nition greatly affects human behaviour, and it is important to understand and take into account its influence on the user actions. Works that apply this kind of prior knowledge are such based on Adaptive Control of Thought – Rational (ACT-R) [128, 146]; such ap- plying the BDI agent model [85]; even some Petri Nets approaches modelling emotional agents [43, 44].
Environmental knowledge: Environment is everything that surrounds a system and that ex- changes different properties with it. In the context of human behaviour modelling, prior knowledge based on the environment is the knowledge about the state of the world out- side the user. It includes information about the elements in the environment but also about the user interactions with it, and how this interaction changes the environment. Such knowledge is important, because it can be essential for determining different situ- ation that may affect the way an activity is executed but still refer to the same activity. For example, environmental knowledge will describe the initial state of the user and the environment, such as what objects are there, where is the user, what has she al- ready done etc. Approaches that employ environmental knowledge are Planning Domain Definition Language (PDDL) [114], Computational Causal Behaviour Models (CCBM) [80], Collaborative Task Modeling Language (CTML) [159].
8Here it should be noted that incorrectly incorporating prior knowledge or incorporating the wrong9kind of
prior knowledge could also lead to suboptimal solutions.
9By wrong we mean knowledge that does not contribute to solving the problem or that incorrectly solves the
Prior knowledge based on ergonomics:Ergonomics is the study that concerns the understand- ing of the interaction between human and other elements of a system and that strives to optimise human well-being and overall performance. Such type of prior knowledge is important, because it may contain important behavioural patterns that will make the recognition of a human activity easier. Approaches employing such kind of prior knowl- edge are Goals, Operators, Methods, and Selection rules (GOMS) [144] and Concurrent Task Trees (CTT) [58].