A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking
Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/18/4008 |
| _version_ | 1798038744468553728 |
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| author | Henry Griffith Yan Shi Subir Biswas |
| author_facet | Henry Griffith Yan Shi Subir Biswas |
| author_sort | Henry Griffith |
| collection | DOAJ |
| description | Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature. |
| first_indexed | 2024-04-11T21:44:27Z |
| format | Article |
| id | doaj.art-ef09d1737cc74f6e814fb587d61263d0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:44:27Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ef09d1737cc74f6e814fb587d61263d02022-12-22T04:01:28ZengMDPI AGSensors1424-82202019-09-011918400810.3390/s19184008s19184008A Container-Attachable Inertial Sensor for Real-Time Hydration TrackingHenry Griffith0Yan Shi1Subir Biswas2Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USADepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USADepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USAVarious sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature.https://www.mdpi.com/1424-8220/19/18/4008non-wearable health monitoring sensorsautomatic fluid intake monitoringinertial sensors |
| spellingShingle | Henry Griffith Yan Shi Subir Biswas A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking Sensors non-wearable health monitoring sensors automatic fluid intake monitoring inertial sensors |
| title | A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking |
| title_full | A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking |
| title_fullStr | A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking |
| title_full_unstemmed | A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking |
| title_short | A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking |
| title_sort | container attachable inertial sensor for real time hydration tracking |
| topic | non-wearable health monitoring sensors automatic fluid intake monitoring inertial sensors |
| url | https://www.mdpi.com/1424-8220/19/18/4008 |
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