Presented by: Marcela D. Rodríguez CICESE/UABC, Ensenada, México 1st International Workshop on Ubiquitous Mobile Instrumentation

Challenges to deploy a sensing campaign  Deciding the granularity of the sensed information Components that collect low-level data vs high-level data  Calibrating the sensing components to the population to be monitored. To the particular participants characteristics Indicating a calibration criteria  The target users are researchers with little or no technical background Developing a tool for behavioral data collection from mobile phones to enable researchers with low technical skills to implement a sensing application: InCense

InCense implementation model  Session: group of components connected to achieve a sensing goal.  Sensors: act as interfaces with the mobile phone’s sensors  Filters: preprocess raw data from sensors  Survey: multiple choice or open- ended questions  Triggers: start sessions if certain conditions are met  Sink: data pool wherein the sensed information is assembled into files

OntoInCense User Customize Deploy Analyse Implement Ontology-based GUI Ontology to support customization Code generation Sensors Library Filters Library Specification language and re-usable components InCense API Template Engine Class Builder Contextual Database Mobile application Project Server JSON Filter Explorer Filter Generator Configuration file Generator InCense Architecture InCense Manager Use of the InCense API for implementing a sensing application

OntoInCense User Customize Deploy Analyse Implement Ontology-based GUI Ontology to support customization Code generation Sensors Library Filters Library Specification language and re-usable components InCense API Template Engine Class Builder Contextual Database Mobile application Project Server JSON Filter Explorer Filter Generator Configuration file Generator InCense Architecture InCense Manager OntoInCense

User Customize Deploy Analyse Implement Ontology-based GUI Ontology to support customization Code generation Sensors Library Filters Library Specification language and re-usable components InCense API Template Engine Class Builder Contextual Database Mobile application Project Server JSON Filter Explorer Filter Generator Configuration file Generator InCense Architecture InCense Manager OntoInCense

User Customize Deploy Analyse Implement Graphical Widget Ontology to support customization Code generation Sensors Library Filters Library Specification language and re-usable components InCense API Template Engine Class Builder Contextual Database Mobile application Project Server JSON Filter Explorer Filter Generator Configuration file Generator InCense Architecture InCense Manager

Scenario:  “A public health organization (PHO) is interested in comparing the walking habits of older adults in the winter and in the spring. They began using InCense for data gathering from 392 individuals during two weeks in the middle of January, and then again in May. The application captures the individual location, the activity level obtained from the accelerometers. A filter infers from the GPS and accelerometer, if the individual is walking or in a vehicle as he leaves his home. When InCense detects that the user is back at home, the mobile phones, will ask the individuals to complete a survey with question related to the activity being performed and their wellness. The data captured from the individuals is sent to the PHO to find interesting correlations with standard statistical packages.”

Extending the Filter Library Implement a FilterRegister a Filter Registrar variables to callibrate Add the Filter to OntoIncense Graphical Widget Filter Explorer

Extending the Filter Library Implement a FilterRegister a Filter Registrar variables to callibrate Add the Filter to OntoIncense Graphical Widget a b

Extending the Filter Library Implement a FilterRegister a Filter Registrar variables to callibrate Add the Filter to OntoIncense Graphical Widget a b

Extending the Filter Library Implement a FilterRegister a Filter Registrar variables to callibrate Add the Filter to OntoIncense Graphical Widget

Develop a sensing campaign Select/drag Components Add Relationships Calibrate components Participant height

Conclusions and Future work  The ontology acts: As a representational model: Facilitates to understand the implementation model of InCense As a graphical user: Adds flexibilty to InCense Toolkit for customizing a sensing application.  We plan to evaluate InCense