Meta-modeling platforms that support the automatic generation of modeling tools open a new quality in information systems development for engineers: Emphasis can be put on the design and use of a modeling language that is customized to the particular needs and desired features. This may contribute to strengthen the information system design phase as it helps to reduce the developers' aversion against overloaded modeling languages and inflexible or expensive modeling tools. Our demo paper introduces HCM-L Modeler, a modeling tool for the Human Cognitive Modeling Language, which has been implemented using the meta-modeling platform ADOxx as a component of an ambient assistance information system.
When thinking of Information Systems (IS), most images in mind are related to the business domain: providing support for managers and their decisions, supporting business processes and thus assisting the employees in their job functions. Typically, though mostly realized on the basis of an integrated standard system, IS are customized to the needs of respective enterprise and its users
Ambient assistance information systems for individuals, however, require an even
more personalized functionality, which leads the notion of self-centered IS: A system
for one particular person, assisting she/him by providing information about and from
herself/himself, in a way tailored to her/his abilities and needs. At a first glance, this
might sound irrelevant. But think about getting older and forgetting how to use a
technical device, how to use the online banking software or even how to dress
yourself on or how to cook your favorite dish: then you might wish to have individual
assistance for mastering your activities of daily life in order to be independent from
others. This leads us to the domain of Ambient Assisted Living (AAL) [
The AAL-project HBMS1 aims at saving relevant information about human behavior of a person in a cognitive model (HCM, Human Cognitive Model) and providing 1 The work is part of the HBMS project - Human Behavior Monitoring and Support: funded by Klaus Tschira Stiftung GmbH, Heidelberg - a research project in the field of Ambient Assisted Living this information to the person when needed. To describe a person’s individual HCM, the Human Cognitive Modeling Language HCM-L, i.e. a Domain-Specific Modeling Language (DSML), has been defined in order (a) to provide a user and use centered language and (b) to enable a mostly automatic model creation and integration out of sensor and/or tracking data. User centeredness should allow and simplify model validation and refinement when desired. As HCM-L is to describe behavioral (“episodic”) knowledge, it can be called a conceptual cognitive modeling language.
This paper concentrates on the modeling tool supporting HCM-L, which forms a component of our HBMS-System (an ambient assistance IS) together with reasoning modules and a web-based support tool. As the tool primarily served as a proof-ofconcept for the modeling language, the novelty of the approach lies mainly in that language. The future HBMS-system users of will be care givers and the supported persons themselves.
Section 2 briefly introduces the HCM-L using an example. Section 3 illustrates some features of the HCM-L Modeler. Section 4 outlines related work. Section 5 gives a resume and outlines future developments. 2
We introduce the HCM-L only shortly here; more detailed information may be
found in [
Creating a HCM-L model starts from the most prominent elements in human behavior: activities. We call the resp. concept Behavioral Unit (BU). Fig. 1 shows a BU ‘create a standing order’.
Daily life activities usually have a goal which is reached by performing a sequence of actions. These actions are captured by the HCM-L concept Operation, graphically drawn inside the resp. BU (expressing that a BU ‘consists’ of operations) and linked by Flows. Having executed an operation without outgoing flow means that the BU’s goal is reached, i.e. in our example: ‘new standing order is created’. There may be alternative actions like the three ways to receive a Transaction Authentication Number (TAN); Pre- and Post-Condition Expressions allow arbitrary granularity for the control flow (graphically simply by naming the logical operator, see XOR in Fig.1).
Create a standing order may be part of a larger BU ‘use the online banking system’; as well, more detailed information about actions may be needed for support; e.g. to request a SMS TAN, again a sequence of actions might be necessary. Therefore operations can be BUs, too. Thus, HCM-L allows for hierarchical structures.
Clearly, support information can not only be derived from dynamic structures.
HCM-L, therefore, provides concepts for modeling structural contexts as well based
one the areas described in [
A comprehensive control pattern-based analysis [
The HCM-L Modeler was developed using the meta-modeling platform ADOxx®2
[
In what follows, we outline some further features that go beyond these basic ones: model stepping for an animated walk-through, querying, checking the consistency of a model, providing reasoning support, reading sensor data for complex scenarios, as well as media file management.
The stepper animates the succession of operations (of the active model) and allows a stepwise pass through a behavioral unit path based on users’ decisions. Basically, this is achieved by highlighting the visited operation.
Once the stepper encounters the need of a user decision (evoked by a pre- or postcondition of the current operation), a selection window is opened where the user can 2 www.ADOxx.org choose the next step. In case of encountering a sub-unit (within a hierarchy of behavioral units) the stepper offers the choice between continuing on the current hierarchy level and walking through the sub-unit.
By visualizing the operation flows that are possible due to the model’s structure, the stepper supports model understanding and validation. The long term idea is to provide this stepper functionality to end users in order to make validation possible for them.
Based on the ADOxx querying feature HCM-L Modeler supports model validation based on (predefined) queries that are formulated using the SQL-like language AQL. Such queries may concern checking the values of attributes, the coherence of elements, the compliance with predefined rules and restrictions as well as the timing of events. AQL queries can be ad hoc formulated by a user or pre-defined by the metamodel developer in the Development Toolkit, e.g., a pre-defined query for event detection (information from sensor data). For ad-hoc formulation the HCM-L Modeler provides an interactive assistant using an ADOxx basic functionality.
As an example, the following query unveils all BUs in the given model that should occur between 06:00am and 11:30am. The user can create the following domain specific AQL query using the AQL queries window of HCM-L Modeler: (<"Behavioral Unit">[?"atTime" >= "00:000:06:00:00"]) AND (<"Behavioral Unit">[?"atTime" <= "00:000:11:30:00"]
Although this query is not terribly realistic in our running example, we show the HCM-L Modeler result in Fig. 2 in order to give an impression on how the system operates: the tabular output consists of the IDs, descriptions and the titles of all behavioral units that should occur between 06:00 and 11:30. Clearly, queries may be more complex by addressing value type restrictions for attributes or complex events in the sense of aggregations of simpler or atomic ones.
A major issue in modeling processes is the fact that comprehensive consistency checks are difficult, in particular for inexperienced users. However, inconspicuous mistakes in the logic may affect the whole model: contradictory semantics reduce the performance of reasoning processes and yield invalid results. For the HCM-L Modeler we considered three main consistency issues: (1) using the right syntax of logical operators, (2) consistent naming of model elements throughout the whole model and (3) the overall syntax check during modeling to allow the right connection between different types of classes and relation classes. Whereas (2) and (3) are automatically checked during the modeling process, (1) is accomplished using the AQL feature: After clicking on the button “pre-defined queries”, HCM-L Modeler yields a menu of different consistency checks for every model and sub model, e.g., checking the correct syntax of the pre-condition lable of an operation or operation-makro. Further consistency check-queries are in preparation.
Figure 3 shows the result of the consistency check “Post-Condition of Operation with Pre Post and Suboperations”. Apparently there were problems with the post conditions of two operations (“check the TAN number” and “Insert data”).
Both model and rule based reasoning approaches for behavior modeling requires the extraction of different features out of the given overall model. HCM-L Modeler, among others, offers the possibility to calculate the frequency of specific activities based on the user history: every operation is supported by a percentage value.
In addition to that, the HCM-L Modeler calculates for each operation the “importance value” based on the user history, and (if wanted by a user) the “cost value” based on the similarity between the current user profile and other users.
Furthermore, it delivers for every operation the smallest number of the remaining operations (i.e., operations to be executed) until reaching the current BU’s goal, together with all possible paths leading to that goal and under consideration of all subunits (e.g., see Fig. 4). I.e., the possible next steps are shown (for loops only one loopiteration). As the selection of the next step is always based on the user’s decision, loops are no problem.
As ADOxx offers the possibility to import and export models in a generic XML format, all those reasoning attributes can be used, e.g. by external inference or reasoning tools.
As already mentioned, the HCM-L Modeler is a part of a HBMS-System with different components. User monitoring will be provided through run-time by using sensors. This sensor data will be used to create the models using HCM-L (firstly simple sequences and after integration more generalized models).
ADOxx provides means to read content from files and databases to be included in the model (object or model level). It can read text, CSV, XLS, XML and DB formats. The HCM-L Modeler currently uses this feature for importing sensor data that are provided in XML.
The file should contain the ID of the sensor, the state of the action (true or false) and the time stamp of the selected activity. For user convenience we included predefined AQL queries into the HCM-L Modeler to simply check active operations and their states.
The HCM-L Modeler offers the possibility to upload media files (video, audio and images files in different formats) into the tool. This feature allows using such files for visualizing complex issues and situations in the support phase (web-based support tool of the HBMS-System).
For example, if the user has to insert the card security code (CSC), sometimes called card verification data (CVD), the corresponding picture is presented to the user (automatically or after request) to show where this code is printed on the card.
Generally, developing a modeling tool by use of a meta-modelling platform proved to be a good way for implementing a tool with basic functionalities in a short period of time. In particular, ADOxx turned out to be an appropriate platform for DSML modeling tool development.
Moreover, the support provided by the ADOxx experts was helpful to implement the desired functionalities. They provided helpful examples additionally to the ADOxx standard tutorial.
Despite of the previous advantages we still have more complex requirements that are not implemented yet because of limitations of the meta-meta model definition, e.g., a visualization of static and dynamic elements in one view with a stepping functionality or the generation of predefined model element instances for a certain scenario (instances of living room, dining room and kitchen for the AAL domain). 4
Several projects concern about activity recognition in the AAL domain. For
example, [
Regarding modeling approaches, most related research and development endeavors
also are in favor of using DSMLs (see, e.g., [
As AAL is a rapidly growing domain, many projects aim at providing support for
people, e.g. [
As already been mentioned, the HCM-L Modeler is just one of the HBMS
information system components. The main access point for the end users will be the
support component. For this, the models are transformed into a step-by-step description
which can be displayed on an appropriate device. A first prototype was implemented
and tested with 40 people in 2012 [
The support component uses the Operations’ label, description and media files
(like video, audio and images) to display a single behavioral step. In one of our
enduser studies we investigated the best way of presenting information to them by using a
set of mock-ups. The results showed that a combination of pictures and audio
information is the preferred presentation form [
As we have shown, the HCM-L Modeler is a powerful and comprehensive tool for developing, managing and exchanging models written in HCM-L. The next development steps for the HCM-L Modeler will focus on the design of advanced reasoning approaches, model optimization, model checker, complex event detection and sensor data fusion with respect to sensors’ uncertainty.
Furthermore, we will work on the model visualization layers to show the overall model architecture (all models and sub-models with respect to the structural context) in 3D to give the modeler and the software developers the possibility to understand the models and the interaction between them more easy.
The support component will be further tested, and we will pay more intention on
individual users’ preferences. [