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The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living

Research output: Contribution to journalArticleScientificpeer-review

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The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living. / Iosifidis, A.; Marami, E.; Tefas, A.; Pitas, I.; Lyroudia, K.

In: Journal of Information Hiding and Multimedia Signal Processing, Vol. 6, No. 2, 01.03.2015, p. 254-273.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Iosifidis, A, Marami, E, Tefas, A, Pitas, I & Lyroudia, K 2015, 'The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living', Journal of Information Hiding and Multimedia Signal Processing, vol. 6, no. 2, pp. 254-273.

APA

Iosifidis, A., Marami, E., Tefas, A., Pitas, I., & Lyroudia, K. (2015). The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living. Journal of Information Hiding and Multimedia Signal Processing, 6(2), 254-273.

Vancouver

Iosifidis A, Marami E, Tefas A, Pitas I, Lyroudia K. The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living. Journal of Information Hiding and Multimedia Signal Processing. 2015 Mar 1;6(2):254-273.

Author

Iosifidis, A. ; Marami, E. ; Tefas, A. ; Pitas, I. ; Lyroudia, K. / The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living. In: Journal of Information Hiding and Multimedia Signal Processing. 2015 ; Vol. 6, No. 2. pp. 254-273.

Bibtex - Download

@article{224b18a6a67f4b549301e9991609f0b0,
title = "The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living",
abstract = "Assisted living has a particular social importance in most developed societies, due to the increased life expectancy of the general population and the ensuing ageing problems. It has also importance for the provision of improved home care in cases of disabled persons or persons suffering from certain diseases that have high social impact. In this context, the development of computer vision systems capable to identify human eating and drinking activity can be really useful in order to prevent undernourishment/malnutrition and dehydration in a smart home environment targeting to extend independent living of older persons in the early stage of dementia. In this paper, we first describe the human centered interface specifications and implementations for such a system, which can be supported by ambient intelligence and robotic technologies. We, subsequently, describe a multi-view eating and drinking activity recognition database that has been created in order to facilitate research towards this direction. The database has been created by using four cameras in order to produce multi-view videos, each depicting one of twelve persons having a meal, resulting to a database size equal to 59.68 hours in total. Various types of meals have been recorded, i.e., breakfast, lunch and fast food. Moreover, the persons have different sizes, clothing and are of different sex. The database has been annotated in a frame base in terms of person ID and activity class. We hope that such a database will serve as a benchmark data set for computer vision researchers in order to devise methods targeting to this important application.",
keywords = "Activity recognition, Multiview video database, Nutrition assistance, Smart home environment",
author = "A. Iosifidis and E. Marami and A. Tefas and I. Pitas and K. Lyroudia",
year = "2015",
month = "3",
day = "1",
language = "English",
volume = "6",
pages = "254--273",
journal = "Journal of Information Hiding and Multimedia Signal Processing",
issn = "2073-4212",
publisher = "National Kaohsiung University of Applied Sciences",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The MOBISERV-AIIA eating and drinking multi-view database for vision-based assisted living

AU - Iosifidis, A.

AU - Marami, E.

AU - Tefas, A.

AU - Pitas, I.

AU - Lyroudia, K.

PY - 2015/3/1

Y1 - 2015/3/1

N2 - Assisted living has a particular social importance in most developed societies, due to the increased life expectancy of the general population and the ensuing ageing problems. It has also importance for the provision of improved home care in cases of disabled persons or persons suffering from certain diseases that have high social impact. In this context, the development of computer vision systems capable to identify human eating and drinking activity can be really useful in order to prevent undernourishment/malnutrition and dehydration in a smart home environment targeting to extend independent living of older persons in the early stage of dementia. In this paper, we first describe the human centered interface specifications and implementations for such a system, which can be supported by ambient intelligence and robotic technologies. We, subsequently, describe a multi-view eating and drinking activity recognition database that has been created in order to facilitate research towards this direction. The database has been created by using four cameras in order to produce multi-view videos, each depicting one of twelve persons having a meal, resulting to a database size equal to 59.68 hours in total. Various types of meals have been recorded, i.e., breakfast, lunch and fast food. Moreover, the persons have different sizes, clothing and are of different sex. The database has been annotated in a frame base in terms of person ID and activity class. We hope that such a database will serve as a benchmark data set for computer vision researchers in order to devise methods targeting to this important application.

AB - Assisted living has a particular social importance in most developed societies, due to the increased life expectancy of the general population and the ensuing ageing problems. It has also importance for the provision of improved home care in cases of disabled persons or persons suffering from certain diseases that have high social impact. In this context, the development of computer vision systems capable to identify human eating and drinking activity can be really useful in order to prevent undernourishment/malnutrition and dehydration in a smart home environment targeting to extend independent living of older persons in the early stage of dementia. In this paper, we first describe the human centered interface specifications and implementations for such a system, which can be supported by ambient intelligence and robotic technologies. We, subsequently, describe a multi-view eating and drinking activity recognition database that has been created in order to facilitate research towards this direction. The database has been created by using four cameras in order to produce multi-view videos, each depicting one of twelve persons having a meal, resulting to a database size equal to 59.68 hours in total. Various types of meals have been recorded, i.e., breakfast, lunch and fast food. Moreover, the persons have different sizes, clothing and are of different sex. The database has been annotated in a frame base in terms of person ID and activity class. We hope that such a database will serve as a benchmark data set for computer vision researchers in order to devise methods targeting to this important application.

KW - Activity recognition

KW - Multiview video database

KW - Nutrition assistance

KW - Smart home environment

M3 - Article

VL - 6

SP - 254

EP - 273

JO - Journal of Information Hiding and Multimedia Signal Processing

JF - Journal of Information Hiding and Multimedia Signal Processing

SN - 2073-4212

IS - 2

ER -