Persuasive and Pervasive Sensing: A New Frontier to Monitor, Track and Assist Older Adults Suffering from Type-2 Diabetes
Abstract
California like the entire nation is aging. There are 4.3 million Californians 65 and older accounting for about 11% of the total state population. We also find that 58% of older adults have high blood pressure; about 21% have been told that they have diabetes. California in particular has the highest incidence of new diabetes cases and nearly 4 million people (diagnosed and undiagnosed) are estimated to be suffering from the disease. Diabetes is a chronic disease, which if unchecked leads to acute and long-term complications and ultimately death. Our older adult population often lacks the cognitive resources to deal with the daily self-management regimens. Many unpaid family members are caring for them today but this is unsustainable. In this paper, we discuss the design and implementation of a wireless sensor network system within the home environment that captures activity of daily living. We mine the data and provide feedback via SMS/text (daily) and tailored newsletter (weekly). We introduce a novel idea called "persuasive sensing" and report results from two home implementations that are showing exciting promise. Moreover we show that with the help of an Artificial Neural Network, we can predict blood-glucose levels for the next day from accumulated data with an accuracy of 94%. The predictive model presented here is a break-through in at-home sensing research.
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The higher prediction percentage for heart rate and blood glucose is most likely because these parameters are associated with monitoring specific diseases such as diabetes where prediction is important.
“This is most likely due to the fact that these parameters are associated with monitoring of specific diseases e.g., diabetes whereby prediction is an important component [23].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [23]
Predictive models in healthcare include blood glucose level prediction, mortality prediction by clustering electronic health data, and a predictive decision making system for dialysis patients.
“A similar example of using predictive models in healthcare are: blood glucose level prediction [23], mortality prediction by clustering electronic health data [40], and a predictive decision making system for dialysis patients [41].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [23]
Blood glucose monitoring is an example of long term data analysis for the sake of right decision making.
“Blood glucose monitoring is an example of long term data analysis for the sake of right decision making [23,28].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [23]
Usually single sensor data have been used for specific analysis on individual physiological data such as ECG signal analysis or blood glucose monitoring.
“Usually single sensor data have been used for specific analysis on individual physiological data such as ECG signal analysis [32,54,92] or blood glucose monitoring [23,28].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [23]
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References 13 · Cited by 28
- Authors
- Chatterjee, S., Dutta, K., Xie, H. (., Byun, J., Pottathil, A., & Moore, M.
- Affiliations
- Claremont Graduate University (US)
- National University of Singapore (SG)
- Amazon (United States) (US)
- Venue
- 2013 46th Hawaii International Conference on System Sciences
- Publisher
- IEEE
- Published
- 2013-01
- Pages
- 2636-2645
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W2047647548
- Cited by
- 28
- References
- 13
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 1 · 2013: 1
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- type 2 diabetes, older adults, home monitoring, wireless sensor networks, activities of daily living, blood glucose prediction, artificial neural networks, self-management, pervasive sensing
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