TrailCare: An indoor and outdoor Context-aware system to assist wheelchair users

Abstract This article proposes a computational system to assist wheelchair users, improving accessibility through ubiquitous computing technologies. TrailCare uses indoor and outdoor location information to assist wheelchair users, recording their trails and providing context-aware assistance. Trails are historical records of users’ displacements that can be used to develop strategic accessibility solutions, such as security management and recommendation through inferences on behavior. TrailCare contributions are the indoor/outdoor trail-aware strategy and its application to recommend contextualized accessibility resources. The system was implemented and integrated with a motorized wheelchair manufactured by a Brazilian company. The prototype is a complete and functional system based on one of the most used wheelchair models in Brazil. TrailCare was assessed through three practical experiments involving scenarios in a university campus. The first two experiments aimed to evaluate the system functionalities. They consisted of two scenarios that tested practical situations supported by the technologies of context awareness and indoor/outdoor trail awareness. The third experiment focused on evaluating the user experience with the system. It comprised a scenario that was followed by 10 wheelchair users, who were observed by researchers regarding usability aspects. The users also filled out a survey based on the Technology Acceptance Model (TAM). The survey was composed by 10 sentences and the results of each one are discussed. The experiments allowed us to learn 10 relevant lessons about technological and usability aspects of the TrailCare that are recorded in this article. The results also showed 96% of acceptance regarding perceived ease of use and 98% in perceived usefulness. The results of experiments showed the potential for implementing TrailCare in real-life situations, allowing the use of ubiquitous technologies to support accessibility for wheelchair users.

[1]  Jorge L. V. Barbosa,et al.  MUCS: A model for ubiquitous commerce support , 2011, Electron. Commer. Res. Appl..

[2]  Paolo Bellavista,et al.  A survey of context data distribution for mobile ubiquitous systems , 2012, CSUR.

[3]  Jorge Luis Victória Barbosa,et al.  DeCom: A model for context-aware competence management , 2015, Comput. Ind..

[4]  Jorge L. V. Barbosa,et al.  A Model for Ubiquitous Care of Noncommunicable Diseases , 2014, IEEE Journal of Biomedical and Health Informatics.

[5]  Anthony LaMarca,et al.  Practical Lessons from Place Lab , 2006, IEEE Pervasive Computing.

[6]  Jorge L. V. Barbosa,et al.  Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach , 2015, J. Univers. Comput. Sci..

[7]  Jorge L. V. Barbosa,et al.  An intelligent model for logistics management based on geofencing algorithms and RFID technology , 2015, Expert Syst. Appl..

[8]  Ramón Cáceres,et al.  Ubicomp Systems at 20: Progress, Opportunities, and Challenges , 2012, IEEE Pervasive Computing.

[9]  Débora Nice Ferrari Barbosa,et al.  Content distribution in trail-aware environments , 2010, Journal of the Brazilian Computer Society.

[10]  Pedro Merino,et al.  Mobile Application Profiling for Connected Mobile Devices , 2010, IEEE Pervasive Computing.

[11]  Siobhán Clarke,et al.  An application framework for mobile, context-aware trails , 2008, Pervasive Mob. Comput..

[12]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[13]  Débora Nice Ferrari Barbosa,et al.  A Decentralized Infrastructure for Ubiquitous Learning Environments , 2014, J. Univers. Comput. Sci..

[14]  Andrina Granic,et al.  Technology acceptance model: a literature review from 1986 to 2013 , 2014, Universal Access in the Information Society.

[15]  Ralf Tönjes,et al.  Survey of Context Provisioning Middleware , 2013, IEEE Communications Surveys & Tutorials.

[16]  Peter J. Brown,et al.  Context-aware applications: from the laboratory to the marketplace , 1997, IEEE Wirel. Commun..

[17]  Euiho Suh,et al.  Context-aware system for proactive personalized service based on context history , 2009, Expert Syst. Appl..

[18]  Octavian Postolache,et al.  UbiSmartWheel: a ubiquitous system with unobtrusive services embedded on a wheelchair , 2009, PETRA '09.

[19]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[20]  Jorge L. V. Barbosa,et al.  Hefestos: an intelligent system applied to ubiquitous accessibility , 2016, Universal Access in the Information Society.

[21]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[22]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[23]  Mark Weiser The computer for the 21st century , 1991 .

[24]  Jorge L. V. Barbosa,et al.  A Middleware Architecture for Dynamic Adaptation in Ubiquitous Computing , 2014, J. Univers. Comput. Sci..

[25]  Cristiano André da Costa,et al.  Toward a General Software Infrastructure for Ubiquitous Computing , 2008, IEEE Pervasive Computing.

[26]  Álvaro Marco,et al.  Towards an intelligent and supportive environment for people with physical or cognitive restrictions , 2009, PETRA '09.

[27]  Mark Weiser,et al.  The computer for the 21st Century , 1991, IEEE Pervasive Computing.

[28]  Gregg C. Vanderheiden,et al.  Ubiquitous Accessibility, Common Technology Core, and Micro Assistive Technology: Commentary on “Computers and People with Disabilities” , 2008, TACC.

[29]  Rubén Posada-Gómez,et al.  Obstacle avoidance embedded system for a smart wheelchair with a multimodal navigation interface , 2014, 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[30]  Débora Nice Ferrari Barbosa,et al.  A model for profile management applied to ubiquitous learning environments , 2014, Expert Syst. Appl..

[31]  Mirco Musolesi,et al.  Anticipatory Mobile Computing , 2013, ACM Comput. Surv..

[32]  Aleksandar Milenkovic,et al.  Smartphones for smart wheelchairs , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[33]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[34]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[35]  BettiniClaudio,et al.  A survey of context modelling and reasoning techniques , 2010 .

[36]  Jorge L. V. Barbosa,et al.  A model for learning objects adaptation in light of mobile and context-aware computing , 2016, Personal and Ubiquitous Computing.

[37]  Jorge L. V. Barbosa,et al.  In the Pursuit of Hygge Software , 2017, IEEE Software.

[38]  Bin Wang,et al.  Location-based services deployment and demand: a roadmap model , 2011, Electron. Commer. Res..

[39]  Bill N. Schilit,et al.  Disseminating active map information to mobile hosts , 1994, IEEE Network.

[40]  Eyal de Lara,et al.  Location-Based Services , 2010, IEEE Pervasive Computing.

[41]  Jorge L. V. Barbosa,et al.  ORACON: An adaptive model for context prediction , 2016, Expert Syst. Appl..

[42]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[43]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[44]  Steven J. Vaughan-Nichols,et al.  Will Mobile Computing's Future Be Location, Location, Location? , 2009, Computer.

[45]  Cheolho Yoon,et al.  Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN , 2007, Electron. Commer. Res. Appl..