Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies
Automatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating. The images may be used to recognize foods being eaten, eating environment, and ot...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9222156/ |
| _version_ | 1818616675375775744 |
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| author | Mohamed Abul Hassan Edward Sazonov |
| author_facet | Mohamed Abul Hassan Edward Sazonov |
| author_sort | Mohamed Abul Hassan |
| collection | DOAJ |
| description | Automatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating. The images may be used to recognize foods being eaten, eating environment, and other behaviors and daily activities. At the same time, captured images may carry privacy concerning content such as (1) people in social eating and/or bystanders (i.e., bystander privacy); (2) sensitive documents that may appear on a computer screen in the view of AIM-2 (i.e., context privacy). In this paper, we propose a novel approach based on automatic, image redaction for privacy protection by selective content removal by semantic segmentation using a deep learning neural network. The proposed method reported a bystander privacy removal with precision of 0.87 and recall of 0.94 and reported context privacy removal by precision and recall of 0.97 and 0.98. The results of the study showed that selective content removal using deep learning neural network is a much more desirable approach to address privacy concerns for an egocentric wearable camera for nutritional studies. |
| first_indexed | 2024-12-16T16:53:34Z |
| format | Article |
| id | doaj.art-4ccae66609684447ac22e907825a7d99 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:53:34Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ccae66609684447ac22e907825a7d992022-12-21T22:23:56ZengIEEEIEEE Access2169-35362020-01-01819861519862310.1109/ACCESS.2020.30307239222156Selective Content Removal for Egocentric Wearable Camera in Nutritional StudiesMohamed Abul Hassan0https://orcid.org/0000-0002-3076-8075Edward Sazonov1https://orcid.org/0000-0001-7792-4234Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, Al, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, Al, USAAutomatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating. The images may be used to recognize foods being eaten, eating environment, and other behaviors and daily activities. At the same time, captured images may carry privacy concerning content such as (1) people in social eating and/or bystanders (i.e., bystander privacy); (2) sensitive documents that may appear on a computer screen in the view of AIM-2 (i.e., context privacy). In this paper, we propose a novel approach based on automatic, image redaction for privacy protection by selective content removal by semantic segmentation using a deep learning neural network. The proposed method reported a bystander privacy removal with precision of 0.87 and recall of 0.94 and reported context privacy removal by precision and recall of 0.97 and 0.98. The results of the study showed that selective content removal using deep learning neural network is a much more desirable approach to address privacy concerns for an egocentric wearable camera for nutritional studies.https://ieeexplore.ieee.org/document/9222156/Privacyegocentric wearable camerabystander privacycontext privacylifeloggingmonitoring of ingestive behavior |
| spellingShingle | Mohamed Abul Hassan Edward Sazonov Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies IEEE Access Privacy egocentric wearable camera bystander privacy context privacy lifelogging monitoring of ingestive behavior |
| title | Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies |
| title_full | Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies |
| title_fullStr | Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies |
| title_full_unstemmed | Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies |
| title_short | Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies |
| title_sort | selective content removal for egocentric wearable camera in nutritional studies |
| topic | Privacy egocentric wearable camera bystander privacy context privacy lifelogging monitoring of ingestive behavior |
| url | https://ieeexplore.ieee.org/document/9222156/ |
| work_keys_str_mv | AT mohamedabulhassan selectivecontentremovalforegocentricwearablecamerainnutritionalstudies AT edwardsazonov selectivecontentremovalforegocentricwearablecamerainnutritionalstudies |