Making Sense out of Activity Sensing in Eldercare

With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by the sensors, it takes a lot of effort and time for the clinicians to makes sense of this data. In this talk, I will survey two connected approaches to provide explanations of these complex sensor patterns as they relate to senior health. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. In seven eldercare facilities around Columbia, MO operated by Americare, we have deployed an intelligent elderly monitoring system with summarization and symptom suggesting capabilities for 3 years.

The first procedure starts by identifying important attributes in the sensor data that are relevant to the health of the elderly. We then develop algorithms to extract these important health related features from the sensor parameters and summarize them in natural language, with methods grounded in fuzzy set theory. We focus on making the natural language summaries to be informative, accurate and concise, and have conducted numerous surveys of experts to validate our choices. While our initial focus is on producing summaries that are informative to healthcare personnel, a recent grant centers on providing feedback to the elders and their families. The Amazon Echo Show is used as the communication device to provide simplified graphics and linguistic health messages.

The second approach is a framework for detecting health patterns utilizing sensor sequence similarity and natural language processing (NLP). A context preserving representation of daily activities is used to measure the similarity between the sensor sequences of different days. Medical concepts are extracted from nursing notes that allows us to impute potential reasons for health alerts based on the activity similarity. Joining these two approaches provide a powerful XAI description of early illness recognition for elders.

James M. Keller received the Ph.D. in Mathematics in 1978. He is now the Curators’ Distinguished Professor Emeritus in the Electrical Engineering and Computer Science Department at the University of Missouri. Jim is an Honorary Proferssor at the University of Nottingham. His research interests center on computational intelligence: fuzzy set theory and fuzzy logic, neural networks, and evolutionary computation with a focus on problems in computer vision, pattern recognition, and information fusion including bioinformatics, spatial reasoning in robotics, geospatial intelligence, sensor and information analysis in technology for eldercare, and landmine detection.

His industrial and government funding sources include the Electronics and Space Corporation, Union Electric, Geo-Centers, National Science Foundation, the Administration on Aging, The National Institutes of Health, NASA/JSC, the Air Force Office of Scientific Research, the Army Research Office, the Office of Naval Research, the National Geospatial Intelligence Agency, the U.S. Army Engineer Research and Development Center, the Leonard Wood Institute, and the Army Night Vision and Electronic Sensors Directorate. Professor Keller has coauthored over 500 technical publications.

Jim is a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the International Fuzzy Systems Association (IFSA), and a past President of the North American Fuzzy Information Processing Society (NAFIPS). He received the 2007 Fuzzy Systems Pioneer Award and the 2010 Meritorious Service Award from the IEEE Computational Intelligence Society (CIS). He has been a distinguished lecturer for the IEEE CIS and the ACM. Jim finished a full six year term as Editor-in-Chief of the IEEE Transactions on Fuzzy Systems, followed by being the Vice President for Publications of the IEEE Computational Intelligence Society from 2005-2008, then as an elected CIS Adcom member, and is in another term as VP Pubs (2017-2020). He was the IEEE TAB Transactions Chair as a member of the IEEE Periodicals Committee, and is a member of the IEEE Publication Review and Advisory Committee from 2010 to 2017. Among many conference duties over the years, Jim was the general chair of the 1991 NAFIPS Workshop, the 2003 IEEE International Conference on Fuzzy Systems, and co-general chair of the 2019 IEEE International Conference on Fuzzy Systems.