Density map visualization from motion sensors for monitoring activity level

This paper describes ongoing work in capturing and analyzing sensor data logged in the homes of seniors. Sensor networks have been deployed in TigerPlace apartments, to promote aging in place. Here, we introduce a visualization of the sensor data in the form of an activity density map which includes time away from home. The visualization is intended to aid caregivers in understanding the sensor data. In the density map, different colors are used to represent different levels of density in motion sensor data. For evaluating the activity density level accurately, time away from home was determined first using a system of fuzzy rules. Three case studies are included to illustrate how the density map can be used to track general activity level over time. (8 pages)

[1]  A. Glascock,et al.  Behavioral Telemedicine: A New Approach to the Continuous Nonintrusive Monitoring of Activities of Daily Living , 2000 .

[2]  Marilyn J Rantz,et al.  Senior care: making a difference in long-term care of older adults. , 2004, The Journal of nursing education.

[3]  Lotfi A. Zadeh Soft computing, fuzzy logic and recognition technology , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  M. Rantz,et al.  TigerPlace: a new future for older adults. , 2005, Journal of nursing care quality.

[6]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[7]  P. Suratt,et al.  A Passive and Portable System for Monitoring Heart Rate and Detecting Sleep Apnea and Arousals: Preliminary Validation , 2006, 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2..

[8]  Kent Larson,et al.  Using a Live-In Laboratory for Ubiquitous Computing Research , 2006, Pervasive.

[9]  K. Haigh,et al.  The Independent LifeStyle Assistant: Lessons Learned , 2006, Assistive technology : the official journal of RESNA.

[10]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  N. M. Barnes,et al.  Lifestyle monitoring-technology for supported independence , 1998 .

[12]  Gregory D. Abowd,et al.  The Aware Home: A Living Laboratory for Ubiquitous Computing Research , 1999, CoBuild.

[13]  Richard Beckwith,et al.  Designing for Ubiquity: The Perception of Privacy , 2003, IEEE Pervasive Comput..

[14]  Sakuko Otake,et al.  Long-term remote behavioral monitoring of the elderly using sensors installed in domestic houses , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[15]  Steve J. Brown,et al.  Developing a well-being monitoring system - Modeling and data analysis techniques , 2006, Appl. Soft Comput..