Contactless Drink Intake Monitoring Using Depth Data
It is important for humans to remain hydrated, particularly for older adults who are at a greater risk of dehydration and may forget to drink. Monitoring liquid intake and getting reminders to drink throughout the day is a useful solution to increase hydration levels. The objective of this paper is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10035006/ |
| _version_ | 1811167523612131328 |
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| author | Rachel Cohen Geoff Fernie Atena Roshan Fekr |
| author_facet | Rachel Cohen Geoff Fernie Atena Roshan Fekr |
| author_sort | Rachel Cohen |
| collection | DOAJ |
| description | It is important for humans to remain hydrated, particularly for older adults who are at a greater risk of dehydration and may forget to drink. Monitoring liquid intake and getting reminders to drink throughout the day is a useful solution to increase hydration levels. The objective of this paper is to automatically detect drink events from multiple containers in a simulated home environment using a vision-based approach. The proposed work compares the use of depth and RGB (red, green, blue) cameras for this task. In this paper, we compared 2D and 3D Convolutional Neural Networks (CNN) using RGB and depth cameras. We collected data from nine participants performing drinking, eating and other Activities of Daily Living (ADL) in a simulated home environment. We found that for the 3D models, the RGB and depth camera inputs provided very similar F1-scores for both 10-Fold (94.3% vs 93.9%, respectively) and Leave-One-Subject-Out (LOSO) cross validation (84.2% vs 86.2%, respectively). This is a promising result as depth cameras also mitigate the challenges to privacy of RGB-based models. The 3D CNN models outperformed the 2D models, thereby creating a more robust system. Depth cameras are a useful alternative to RGB cameras with equal performance in identifying drinking events. |
| first_indexed | 2024-04-10T16:11:54Z |
| format | Article |
| id | doaj.art-c0476458b311497ab11c715f78835e0d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T16:11:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c0476458b311497ab11c715f78835e0d2023-02-10T00:00:51ZengIEEEIEEE Access2169-35362023-01-0111122181222510.1109/ACCESS.2023.324183510035006Contactless Drink Intake Monitoring Using Depth DataRachel Cohen0https://orcid.org/0000-0003-1327-680XGeoff Fernie1Atena Roshan Fekr2KITE—Toronto Rehabilitation Institute, UHN, Toronto, CanadaKITE—Toronto Rehabilitation Institute, UHN, Toronto, CanadaKITE—Toronto Rehabilitation Institute, UHN, Toronto, CanadaIt is important for humans to remain hydrated, particularly for older adults who are at a greater risk of dehydration and may forget to drink. Monitoring liquid intake and getting reminders to drink throughout the day is a useful solution to increase hydration levels. The objective of this paper is to automatically detect drink events from multiple containers in a simulated home environment using a vision-based approach. The proposed work compares the use of depth and RGB (red, green, blue) cameras for this task. In this paper, we compared 2D and 3D Convolutional Neural Networks (CNN) using RGB and depth cameras. We collected data from nine participants performing drinking, eating and other Activities of Daily Living (ADL) in a simulated home environment. We found that for the 3D models, the RGB and depth camera inputs provided very similar F1-scores for both 10-Fold (94.3% vs 93.9%, respectively) and Leave-One-Subject-Out (LOSO) cross validation (84.2% vs 86.2%, respectively). This is a promising result as depth cameras also mitigate the challenges to privacy of RGB-based models. The 3D CNN models outperformed the 2D models, thereby creating a more robust system. Depth cameras are a useful alternative to RGB cameras with equal performance in identifying drinking events.https://ieeexplore.ieee.org/document/10035006/Artificial neural networkscomputer visiondepth camerasfluid intake monitoringimage recognitionintake gesture detection |
| spellingShingle | Rachel Cohen Geoff Fernie Atena Roshan Fekr Contactless Drink Intake Monitoring Using Depth Data IEEE Access Artificial neural networks computer vision depth cameras fluid intake monitoring image recognition intake gesture detection |
| title | Contactless Drink Intake Monitoring Using Depth Data |
| title_full | Contactless Drink Intake Monitoring Using Depth Data |
| title_fullStr | Contactless Drink Intake Monitoring Using Depth Data |
| title_full_unstemmed | Contactless Drink Intake Monitoring Using Depth Data |
| title_short | Contactless Drink Intake Monitoring Using Depth Data |
| title_sort | contactless drink intake monitoring using depth data |
| topic | Artificial neural networks computer vision depth cameras fluid intake monitoring image recognition intake gesture detection |
| url | https://ieeexplore.ieee.org/document/10035006/ |
| work_keys_str_mv | AT rachelcohen contactlessdrinkintakemonitoringusingdepthdata AT geofffernie contactlessdrinkintakemonitoringusingdepthdata AT atenaroshanfekr contactlessdrinkintakemonitoringusingdepthdata |