Drinking Event Detection and Episode Identification Using 3D-Printed Smart Cup

Insufficient fluid intake is becoming an actual concern and problem of the elderly with dramatic increase of aging population. Poor fluid intake habits and Dehydration can cause long-term negative effects in mental and physical capabilities, and even death. Typical approaches to monitor individual f...

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Bibliographic Details
Published in:IEEE sensors journal 2020-11, Vol.20 (22), p.13743-13751
Main Authors: Liu, Kai-Chun, Hsieh, Chia-Yeh, Huang, Hsiang-Yun, Chiu, Li-Tzu, Hsu, Steen Jun-Ping, Chan, Chia-Tai
Format: Article
Language:English
Subjects:
3D-printing technology
Accelerometers
Algorithms
Containers
Dehydration
Drinking
drinking episode identification
Drinking event detection
Fluids
Intelligent sensors
machine learning
Monitoring
Prototypes
smart cup
Three dimensional printing
Ubiquitous computing
Wireless sensor networks
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Summary:Insufficient fluid intake is becoming an actual concern and problem of the elderly with dramatic increase of aging population. Poor fluid intake habits and Dehydration can cause long-term negative effects in mental and physical capabilities, and even death. Typical approaches to monitor individual fluid intake behavior and consumption of fluid quantities mainly rely on self-report, such as questionnaires. However, these typical approaches suffer issues in manual errors and cause additional burdens on the user. In recent years, advantages of wireless sensor networks and pervasive computing technologies have provided the opportunities to create automatic, objective and low-intrusive fluid intake monitoring systems. The aim of this work is to design a 3D-printed smart cup attached with a single accelerometer and drinking event detection algorithm that is feasible to spot fluid intake gestures, especially for detecting drinking events and recognizing complete periods of drinking. Such drinking information about timing and the frequency of fluid intake can reveal fluid intake habits and that is important for clinical assessment and intervention. The finding shows that the best system performance using k-nearest neighbors (k-NN) model with window size of 4s achieves 89.92% and 85.88% F-measure in event-defined and frame-defined evaluation approaches, respectively.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3004051