The objective of this study is to validate reduced graphene oxide (RGO)-based volatile organic compounds (VOC) sensors, assembled by simple and low-cost manufacturing, for the detection of disease-related VOCs in human breath using machine learning (ML) algorithms. RGO films were functionalized by four different metalloporphryins to assemble cross-sensitive chemiresistive sensors with different sensing properties. This work demonstrated how different ML algorithms affect the discrimination capabilities of RGO–based VOC sensors. In addition, an ML-based disease classifier was derived to discriminate healthy vs. unhealthy individuals based on breath sample data. The results show that our ML models could predict the presence of disease-related VOC compounds of interest with a minimum accuracy and F1-score of 91.7% and 83.3%, respectively, and discriminate chronic kidney disease breath with a high accuracy, 91.7%.
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IoT-Enabled Smart Mobility Devices for Aging and Rehabilitation
Many elderly individuals have physical restrictions that require the use of a walker to maintain stability while walking. In addition, many of these individuals also have age-related visual impairments that make it difficult to avoid obstacles in unfamiliar environments. To help such users navigate their environment faster, safer and more easily, we propose a smart walker augmented with a collection of ultrasonic sensors as well as a camera. The data collected by the sensors is processed using echo-location based obstacle detection algorithms and deep neural networks based object detection algorithms, respectively. The system alerts the user to obstacles and guides her on a safe path through audio and haptic signals.
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- Award ID(s):
- 1852002
- PAR ID:
- 10225521
- Date Published:
- Journal Name:
- IEEE International Conference on Communications (ICC) 2020
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC40277.2020.9149442
