Subscribe to RSS
Generating Sensor Data Summaries to Communicate Change in Elder’s Health Status
12 July 2013
accepted: 26 January 2013
20 December 2017 (online)
Background: Sensor systems detect critical health changes of frail residents in the community. However, sensor systems alone may not allow users to identify data trends fast enough. Linguistic summaries of sensor data describing elder activity in their apartment provide a useful solution so clinicians can respond quicker.
Objectives: This paper describes two case studies of independent elders living with sensors in their assisted living apartment. Residents experienced declining health status and activity level over a period of approximately 24 months. Linguistic summaries were assessed iteratively by engineers and nurses working with the sensor system.
Methods: We created summaries of activity data collected from sensors located in resident apartments during a period of health status change. Engineers distilled information from heterogeneous data sources including bedroom motion and bed restlessness sensors during the summarization process. Engineers used fuzzy measures to compare two different periods of nighttime activity. Using iterative approaches a registered nurse worked with the team to develop algorithms and short phrases that appropriately capture and describe changes in activity levels.
Results: Total activity levels captured by sensors were graphed for two elderly residents experiencing health problems over a period of months. In the first case study (resident 3004), an elderly resident had knee surgery and onset of backspasms postoperatively. Graphed dissimilar measures show changes from baseline when backspasms occur. In the second case study (resident 3003), there were increased periods of bed restlessness before and after a resident had a major surgical procedure. During these periods, graphs of dissimilarity measures indicate that there were changes from usual baseline periods of restlessness postoperatively indicating the health problems were persisting. Nurse care coordination notes indicate these episodes were related to poor pain control.
Conclusions: Summaries of activity change are useful for care coordinators to detect resident health status for community dwelling residents.
Citation: Alexander GL, Wilbik A, Keller JM, Musterman K. Generating sensor data summaries to communicate change in elder’s health status. Appl Clin Inf 2014; 5: 73–84 http://dx.doi.org/10.4338/ACI-2013-07-RA-0050
- 1 Pruchno R. Not your mothers old age: Baby boomers at age 65. The Gerontologist 2012; 52: 149-152.
- 2 Skubic M, Alexander GL, Popescu M, Rantz MJ, Keller J. A smart home application to eldercare: Current status and lessons learned. Technology and Health Care 2009; 17: 183-201.
- 3 Alexander GL, Rantz MJ, Skubic M, Aud MA, Wakefield B, Florea E, Paul A. Sensor systems for monitoring functional status in assisted living residents. Research in Gerontological Nursing 2008; 1: 238-244.
- 4 Alexander GL, Wakefield BJ, Rantz MJ, Aud MA, Skubic M, Erdelez S. Evaluation of a passive sensor technology interface to assess elder activity in independent living. Nursing Research 2011; 60: 318-325.
- 5 Alexander GL, Rantz MJ, Skubic M, Koopman R, Phillips LJ, Guevara RD, Miller SJ. Evolution of an early illness warning system to monitor frail elders in independent living. Journal of Healthcare Engineering 2011; 2: 259-286.
- 6 Rantz MJ, Skubic M, Miller S, Galambos C, Alexander GL, Keller J, Popescu M. Sensor technology to support Aging in Place. Journal of the American Medical Informatics Association 2013; 14: 386-391.
- 7 Anderson D, Luke R, Keller J, Skubic M, Rantz M, Aud M. Modeling human activity from voxel person using fuzzy logic. IEEE Transactions on Fuzzy Systems 2008; 17: 39-49.
- 8 Ros M, Anderson D, Keller J, Pegalajar M, Delgado M, Vila A, Popescu M. Linguistic summarization of long-term trends for understanding change in human behavior. Edited by IEEE. Taipei, Taiwan: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); 2011: 2080-2080.
- 9 Kacprzyk J, Yager RR. Linguistic summaries of data using fuzzy logic. International Journal of General Systems 2001; 30: 133-154.
- 10 Rantz MJ, Skubic M, Miller S, Galambos C, Alexander GL, Keller J, Popescu M. Sensor technology to support aging in place. Journal of the American Medical Directors Association. 2013 , [EPub Ahead of Print].
- 11 Rosales L, Skubic M, Heise D, Devaney MJ, Schaumburg M. Heartbeat detection from a hydraulic bed sensor using a clustering approach. Edited by IEEE. San Diego:: Proceedings, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2012: 2383-2387.
- 12 Rantz MJ, Skubic M, Koopman RJ, Alexander GL, Phillips L, Musterman K, Back J, Aud MA, Galambos C, Guevara RD, Miller SJ. Automated technology to speed recognition of signs of illness in older adults. Journal of Gerontological Nursing 2012; 38: 18-23.
- 13 Anderson D, Luke R, Keller J, Skubic M, Rantz M, Aud M. Linguistic summarization of activities from video for fall detection using voxel person and fuzzy logic. Computer Vision and Image Understanding 2009; 113: 80-89.
- 14 Polit DF, Tatano-Beck C. Qualitative Research Design and Approaches. In Nursing Research: Principles and Methods. 7th edition. Philadelphia:: Lippincott; 2004: 245-268.
- 15 Rantz MJ, Skubic M., Koopman RJ, Phillips L, Alexander GL, Miller SJ. Using sensor networks to detect urinary tract infections in older adults. Columbia MO:: 13th IEEE International Conference on e-Health Networking, Application, & Services;; 2011: 142-142.
- 16 Wilbik A, Keller JM. A fuzzy measure similarity between sets of linguistic summaries. IEEE Transactions on Fuzzy Systems 2013; 21: 183-189.
- 17 Wilbik A, Keller JM. A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems 2012; 208: 79-94.
- 18 Wilbik A, Keller JM, Alexander GL. Similarity Evaluation of Sets of Linguistic Summaries. International Journal of Intelligent Systems 2012, 27: 926-938.
- 19 Wilbik A, Keller JM, Alexander GL. Similarity evaluation of sets of linguistic summaries. Journal of Intelligent Systems 2012, 27: 926-938.
- 20 Demiris G, Hensel BK, Skubic M, Rantz MJ. ’Senior residents’ perceived need of and preferences for „smart home” sensor technologies. International Journal of Technology Assessment in Health Care 2008, 24: 120-124.
- 21 Rantz MJ, Skubic M, Alexander GL, Aud M, Wakefield B, Galambos C, Koopman R, Miller S. Improving nurse care coordination with technology. Computers Informatics Nursing 2010; 28: 325-332.
- 22 Rantz MJ, Skubic M, Abbott C, Galambos C, Pak Y, Ho D, Stone E, Rui L, Back J, Miller SJ. In-home fall risk assessment and detection sensor system. Journal of Gerontological Nursing 2013; 39: 18-22.