Monitoring elderly behavior via indoor position-based stigmergy

In this paper we present a novel approach for monitoring elderly people living alone and independently in their own homes. The proposed system is able to detect behavioral deviations of the routine indoor activities on the basis of a generic indoor localization system and a swarm intelligence method. For this reason, an in-depth study on the error modeling of state-of-the-art indoor localization systems is presented in order to test the proposed system under different conditions in terms of localization error. More specifically, spatiotemporal tracks provided by the indoor localization system are augmented, via marker-based stigmergy, in order to enable their self-organization. This allows a marking structure appearing and staying spontaneously at runtime, when some local dynamism occurs. At a second level of processing, similarity evaluation is performed between stigmergic marks over different time periods in order to assess deviations. The purpose of this approach is to overcome an explicit modeling of user's activities and behaviors that is very inefficient to be managed, as it works only if the user does not stray too far from the conditions under which these explicit representations were formulated. The effectiveness of the proposed system has been experimented on real-world scenarios. The paper includes the problem statement and its characterization in the literature, as well as the proposed solving approach and experimental settings.

[1]  Beatrice Lazzerini,et al.  An adaptive rule-based approach for managing situation-awareness , 2012, Expert Syst. Appl..

[2]  M. Vila,et al.  Correct behavior identification system in a Tagged World , 2009, Expert Syst. Appl..

[3]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[4]  Frank Dürr,et al.  Pervasive and Mobile Computing , 2012 .

[5]  J. Doornik,et al.  An Omnibus Test for Univariate and Multivariate Normality , 2008 .

[6]  Martin Becker,et al.  Rule-based activity recognition framework: Challenges, technique and learning , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[7]  Francesco Marcelloni,et al.  Autonomic tracing of production processes with mobile and agent-based computing , 2011, Inf. Sci..

[8]  Frank Bomarius,et al.  An Event-Driven Approach to Activity Recognition in Ambient Assisted Living , 2009, AmI.

[9]  Stefano Chessa,et al.  Evaluating Ambient Assisted Living Solutions: The Localization Competition , 2013, IEEE Pervasive Computing.

[10]  Witold Pedrycz,et al.  Using multilayer perceptrons as receptive fields in the design of neural networks , 2009, Neurocomputing.

[11]  G. Fasano,et al.  A multidimensional version of the Kolmogorov–Smirnov test , 1987 .

[12]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[13]  Gwenn Englebienne,et al.  Behavior analysis of elderly using topic models , 2014, Pervasive Mob. Comput..

[14]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[15]  Juan A. Botía Blaya,et al.  Ambient Assisted Living system for in-home monitoring of healthy independent elders , 2012, Expert Syst. Appl..

[16]  Paolo Barsocchi,et al.  SALT : Source-Agnostic Localization Technique Based on Context Data from Binary Sensor Networks , 2014, AmI.

[17]  Silvia Coradeschi,et al.  Sensor Network Infrastructure for a Home Care Monitoring System , 2014, Sensors.

[18]  A. Yamaguchi,et al.  Monitoring behavior in the home using positioning sensors , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[19]  Stefano Chessa,et al.  Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 EvAAL competition , 2013, J. Ambient Intell. Smart Environ..

[20]  Marco Avvenuti,et al.  MARS, a Multi-Agent System for Assessing Rowers' Coordination via Motion-Based Stigmergy , 2013, Sensors.

[21]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Andreas Stainer-Hochgatterer,et al.  A modular and flexible system for activity recognition and smart home control based on nonobtrusive sensors , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[23]  Svetha Venkatesh,et al.  Recognition of emergent human behaviour in a smart home: A data mining approach , 2007, Pervasive Mob. Comput..

[24]  N. Noury,et al.  Monitoring behavior in home using a smart fall sensor and position sensors , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[25]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[26]  Manuel P. Cuéllar,et al.  A survey on ontologies for human behavior recognition , 2014, ACM Comput. Surv..

[27]  Vinny Cahill,et al.  Using stigmergy to co-ordinate pervasive computing environments , 2004, Sixth IEEE Workshop on Mobile Computing Systems and Applications.

[28]  Chris D. Nugent,et al.  Ontology-based activity recognition in intelligent pervasive environments , 2009, Int. J. Web Inf. Syst..

[29]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[30]  Alexei V. Soloviev,et al.  RealTrac Technology Overview , 2013, EvAAL.

[31]  Guang-Zhong Yang,et al.  The use of pervasive sensing for behaviour profiling - a survey , 2009, Pervasive Mob. Comput..

[32]  M. Tan,et al.  Testing multivariate normality in incomplete data of small sample size , 2005 .

[33]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[34]  Jit Biswas,et al.  An introduction to ontology-based activity recognition , 2009, MoMM.

[35]  Hongnian Yu,et al.  Elderly activities recognition and classification for applications in assisted living , 2013, Expert Syst. Appl..

[36]  Irfan A. Essa,et al.  A novel sequence representation for unsupervised analysis of human activities , 2009, Artif. Intell..

[37]  Gilles Virone Assessing everyday life behavioral rhythms for the older generation , 2009, Pervasive Mob. Comput..

[38]  Oliver Brdiczka,et al.  Learning Situation Models in a Smart Home , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[39]  Maurizio Bocca,et al.  Radio Tomographic Imaging for Ambient Assisted Living , 2012, EvAAL.

[40]  Paul Müller,et al.  Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces , 2007, HCI.

[41]  Giulio Sandini,et al.  A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents , 2007, IEEE Transactions on Evolutionary Computation.

[42]  Byung-Seo Kim,et al.  Smart Solutions in Elderly Care Facilities with RFID System and Its Integration with Wireless Sensor Networks , 2014, Int. J. Distributed Sens. Networks.

[43]  Liming Chen,et al.  Activity Recognition: Approaches, Practices and Trends , 2011 .

[44]  Stefano Chessa,et al.  Evaluating AAL Systems Through Competitive Benchmarking , 2012, Communications in Computer and Information Science.

[45]  Tetsuya Nakamura,et al.  Behavior-based stigmergic navigation , 2010, UbiComp '10 Adjunct.

[46]  Stathes Hadjiefthymiades,et al.  Advanced fuzzy inference engines in situation aware computing , 2010, Fuzzy Sets Syst..

[47]  Juan M. Corchado,et al.  The n-Core Polaris Real-Time Locating System at the EvAAL Competition , 2011, EvAAL.