TY - JOUR
T1 - LIDS
T2 - Mobile System to Monitor Type and Volume of Liquid Intake
AU - Pedram, Mahdi
AU - Mirzadeh, Seyed Iman
AU - Rokni, Seyed Ali
AU - Fallahzadeh, Ramin
AU - Woodbridge, Diane Myung Kyung
AU - Lee, Sunghoon Ivan
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2021/9/15
Y1 - 2021/9/15
N2 - Fluid intake tracking is crucial in providing interventions that assist individuals to stay hydrated by maintaining an adequate amount of fluid. It also helps to manage calorie intake by accounting for the amount of calorie consumed from beverages. While staying hydrated and controlling calorie intake is critical in both physical wellness and cognitive health, existing technologies do not provide a solution for monitoring both fluid type and fluid volume. To address this limitation, we present the design, implementation, and validation of Liquid Intake Detection System (LIDS) for real-time tracking of fluid intake type and volume. The system devises a sensing module that is composed of ultrasonic, RGB color, temperature, and accelerometer sensors as well as a computational framework for machine-learning-based fluid intake type classification, volume estimation, and bottle-state-recognition. The developed sensing unit is small and light-weight that can be mounted, from inside, on the lid of a drinking bottle. We conduct extensive experiments to collect data in a variety of bottles and environmental settings. The results show that the accuracy of fluid type detection ranges from 74.93% to 94.98% while trying to detect the fluid of an unseen bottle. Our results for volume estimation show that the regression-based volume estimation supports a root-relative-squared-error that ranges from 1.12% to 13.36%.
AB - Fluid intake tracking is crucial in providing interventions that assist individuals to stay hydrated by maintaining an adequate amount of fluid. It also helps to manage calorie intake by accounting for the amount of calorie consumed from beverages. While staying hydrated and controlling calorie intake is critical in both physical wellness and cognitive health, existing technologies do not provide a solution for monitoring both fluid type and fluid volume. To address this limitation, we present the design, implementation, and validation of Liquid Intake Detection System (LIDS) for real-time tracking of fluid intake type and volume. The system devises a sensing module that is composed of ultrasonic, RGB color, temperature, and accelerometer sensors as well as a computational framework for machine-learning-based fluid intake type classification, volume estimation, and bottle-state-recognition. The developed sensing unit is small and light-weight that can be mounted, from inside, on the lid of a drinking bottle. We conduct extensive experiments to collect data in a variety of bottles and environmental settings. The results show that the accuracy of fluid type detection ranges from 74.93% to 94.98% while trying to detect the fluid of an unseen bottle. Our results for volume estimation show that the regression-based volume estimation supports a root-relative-squared-error that ranges from 1.12% to 13.36%.
KW - Wearable sensors
KW - activity recognition
KW - machine learning
KW - power consumption
UR - http://www.scopus.com/inward/record.url?scp=85107211545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107211545&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2021.3081012
DO - 10.1109/JSEN.2021.3081012
M3 - Article
AN - SCOPUS:85107211545
SN - 1530-437X
VL - 21
SP - 20750
EP - 20763
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 18
ER -