The growing popularity of Internet of Things' devices, sensors, and applications available on the market is pushing towards a revolution of lifelogging and its relationship with wellness. Thanks to the characteristics of pervasiveness and mobility of wearable IoT tools, we are witnessing the emergence of a new research and development domain: the eWellness. Together with this, new challenges arise, and ask for effective methods and techniques to improve the way extraction, merge, analysis, visualization, and data sharing are currently managed. In this paper, we present our research study on the eWellness domain and in particular the design and development of the SmartFit framework, aimed at supporting the collaborative visual design of IoT rules by means of End-User Development techniques.
Nowadays we are witnesses of the proliferations of
different sensor devices able to produce several types
of data that can be profitable used for advising people
on how to improve their lifestyle, health, and wellness.
The survey reported in [
A scalable and efficient solutions should be devised that can be applied by domain experts in configuring the network of sensors (both physical and social), and services. ETL (extract, transform and load) solutions, usually applied off-line, need to be revised and applied on-line for feeding the system with fresh and timely data. Finally, a user-friendly environment has to be conceived for properly supporting the user in the analysis processes (sensor discovery, feeding, and knowledge inference).
Specifically, in this paper we present and discuss our
sociotechnical study on the design and development of
IoT ecosystems to be used by non-professional sport
teams. The peculiar structure of non-professional sport
organizations is characterized by the existence of small
teams with athletes who live different kind of lives,
being professionals in different domains and meeting
only for some hours a week. Keeping track of their
habits, in terms of physical activity, nutrition, sleep and
so on, would help the coaches in understanding the
variety of the team members and finding successful
schemes of training. In order to enable domain experts
in generating, configuring and managing the network of
sensors and services we adopt a meta-design
approach. By means of this approach we seek to create
social-technical conditions for broad participation in
design activities at both design and use time of all the
involved stakeholders, rather than anticipating all
requirements at design time [
In this section, we present a use case we studied to
experiment the potentials of our solutions both at
meta-design and design level. In this context of use we
consider three different Communities of Practice (CoPs)
[
At meta-design level, IoT Engineers need to configure the network of sensors and services for managing the data-flow to be served at the Design level interactive system. The outcome of this environment is to detect a set of events that coaches and trainers need to manipulate at design level for monitoring the physical activities or daily behavior of their athletes.
StreamLoader provides IoT Engineers with a set of ETL
operators used for manipulating streams of data in real
time (or almost real time). In this way, data are loaded
from a set of sensors and services, providing a
convenient way for user to real-timely read the data
information and make tactical decisions. Commercial
systems such as Talend Studio1, StreamBase Studio2,
Waylay.io3 offer graphical interfaces for designing
workflows and dataflows as graphs of connected nodes
representing tasks and data-sources. While Talend
works on static data coming from fixed data-sources,
StreamBase and WayLay can receive and analyze
continuous data streams and are specifically designed
for IoT. These environments provide rich user interface
support for the full application lifecycle but they are
desktop-based systems and in some cases, specific
conditions can be only created by adopting strategies
based on programming languages paradigms (as for
StreamSQL in StreamBase Studio) or by personalizing
existing templates having well-defined trigger policy (as
in Waylay.io). Visual queries are other examples of
solutions can be used for assisting users to extract
information from databases in an intuitive, visual and
1 www.talend.com
2 www.streambase.com
3 www.waylay.io
natural approach, making information systems
comprehensive and efficient for a wide range of
applications. SmartVortex Visual Query System [
The meta-design environment described earlier in this Section and used by IoT Engineers, constitutes the base for providing coaches and trainers with the possibility to manipulate the flow of events in order to monitor the physical activities or daily behavior of their athletes. The design environment for manipulating these events aims at offering a graphical visual strategy for exploiting the potentials of an IoT environment in the deployment of rules. Systems like JBoss Drools4, OpenRules, and IBM WebSphereJRules5 provide platforms for supporting users in creating complex rules to trigger proper actions when specific conditions occur. In these environments, the coding of complex rules is generally performed by skilled technicians through ad-hoc Rule Engines. Nevertheless, in real contexts of use, the definition of rules is performed by domain experts that are not experienced technician and prefer to use natural language or graphical notations. Other visual strategies typically used in IoT field for modelling ECA (Event-ConditionAction) rules can be described through the most famous systems that apply them: IFTTT, Atooma and Yahoo's Pipes. The first two examples allow users to define sets of desired behaviors in response to specific events. The visual strategy aims at creating automated rules by using graphical notation for programming statements such as: "IF this DO That" or "WHEN trigger THEN action". A second type of applications stems from the outstanding work done with Yahoo's Pipes. These applications are based on the idea of providing a visual pipeline generator for supporting end users in creating aggregation, filtering, and porting of data originated by 4 http://www.drools.org/ 5 http://www.ibm.com/software/websphere/ sources. The visual strategies adopted by following IFTTT or Yahoo's Pipes compliant solutions are promising techniques but, in our opinion, they present some lacks. The former offers a very simple and easy to learn solution based on the definition of ad hoc rules that can notify the end users when something happens – e.g. when their favorite sites are updated, when they check-in in some places or their friends do, or warn them when specific weather conditions are going to take place. However, the language is not enough expressive for the specification of more sophisticated rules based on time and space conditions. On the other hand, the latter offers a too complex solution for supporting the end user in expressing their preferences. Pretending that end users can deal with APIs of several sensors/devices put at risk the success of the visual approach. Moreover, events in each stream of an IoT scenario, are time and space dependent and so the related rules need to take into account these type of conditions. Nevertheless, in the described systems, time and space dimensions are almost neglected. Once the flow of events for a given analysis have been identified by IoT engineers, rules should be defined for specifying the action to be actuated when specific events occur. Our system, named Rule Editor, extends the IF-THIS-THEN-THAT approach and supports the definition of rules in a more articulated way by keeping the complexity at an acceptable level. The idea is to keep the simplicity of the IF-THIS-THEN-THAT system pairing it with the use of formula languages. Moreover, time and space dimensions are exploited and adopted for expressing more loose rules in the statements. From the point of view of language expressiveness, we consider rules that are more expressive than the IF-THIS-THEN-THAT system from different perspectives. Complex conditions are supported to correlate events both from the temporal and spatial dimensions. The time dimension allows end users to set rules that can be fired at some specific time, delayed in case of certain conditions are verified, and may be repeated until some event happens. The space dimension gives users the chance of linking rules to the place/area where the events currently taking place, where they will possibly be in the future or where they are moving into. Moreover, rules are enabled depending on spatio-temporal conditions that are clearly outlined in the rule specification. This allows the identification of the streams that are really relevant in a given time and given location for monitoring given situations and properly react to them. By exploiting a Rule Language, the rules can be specified by means of the graphical Rule Editor. As depicted in Figure 2 the graphical interface is based on drop-down menus that are populated by using the attributes that characterize the JSON of the flow of events produced by the IoT Engineers with the SmartLoader environment.
Figure 2, where the user has defined four rules: (i) R1: Hours of sleep less than 7 (ii) R2: Calories intake at dinner greater than 1,500 (iii) R3: Number of steps less than 8,000 (iv) R4: Activity duration less than 45 (minutes). The rules are built in this way: R1 AND R2 AND (R3 OR R4). The meaning of the Composite Rule created in this example is: “if the hours of sleep of the day before are less than 7 AND if the calories intake at dinner (before the sleep) is greater than 1,500 AND (if the number of steps is less than 8,000 at day OR the duration of physical activity is not less of 45 minutes at day THEN send the athlete a message that warns about the behavior and performances. The rule editor aims at allowing non-technical people to specify rules by using simple drop-down menus. The conditions can be composed by combining groups of statements connected by using the operator AND or OR. Temporal conditions are defined as depicted in Figure 3 and use the automatically assigned names of the Rules as elements to be composed (R1, R2, R3, ...). An example of temporal condition is that R3 (number of steps less than 8,000) has to take place before R2 (calories intake at dinner greater than 1,500) with a range of +/- 5 minutes. In other words, the dinner must begin within 5 minutes after the user has finished to walk (for less of 8000 steps). Another complex temporal condition can be: R4 (activity duration less than 45 minutes) begins before of the R2 (Calories intake at dinner greater than 1,500) and ends after +/- 10 minutes. In other words, the trainer wants to check if her/his athletes eat too much and how, if they eat not seated at a table but when they are on the move and quickly (in less than 45 minutes). Once a rule is created, it is stored in a repository for further re-use or for sharing it among members of a community of trainers and coaches. In this way, it easy for other users to customize existing rules according to their needs.
supporting IoT engineers, coaches and trainers in designing and analyzing data-flows coming from a set of physical/social sensors and services. At the moment we are working for setting up a user test evaluation for our eWellness system thanks to collaboration with the CSI (Centro Sportivo Italiano – Italian Sport Centre). In this use case, a set of sport teams will be involved in order to perform a set of tasks. What we want to study from the user tests, is how far our approach is able to offer new possibilities both at the design and use time and to understand how the idea to combine the design and end user environments appears to be successful and effective solution both for domain and technical experts.