Cooking Method Detection Based on Electronic Nose in Smart Kitchen

被引：0

作者：

Gao Y. ^{[1
,2
]}

Hu Y. ^{[2
]}

Lu Q. ^{[1
,2
]}

机构：

[1] Academy of Art & Design, Tsinghua University, Beijing

[2] The Future Laboratory, Tsinghua University, Beijing

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 02期

关键词：

electronic-nose; interaction design; machine learning; smart kitchen;

D O I：

10.3724/SP.J.1089.2023.20053

中图分类号：

学科分类号：

摘要：

Dietary health study in home environment has been a long-standing research direction, which is mainly focused on dietary habits and nutritional balance, but relatively little research has been conducted on cooking methods. In this paper, an electronic nose-based cooking method was developed in a smart kitchen scenario. The gas sensor array constructed by MOS gas sensors was used to continuously collect the gas generated during the cooking process of 84 dishes, and the classification models based on decision tree and random forest are compared, and the results showed that the average classification accuracy of the latter is 95% for three cooking methods: boiling, frying and deep-frying. In addition, 6 users participated in on-site experience and interviews to evaluate the usability of the method. The potential of the method to help users record their diets and manage their health was demonstrated, and its interactive applications were explored to summarize its future design patterns and interactions in smart kitchens, providing design suggestions and directions for subsequent research. © 2023 Institute of Computing Technology. All rights reserved.

引用

页码：185 / 194

页数：9

共 22 条

[1]

Wolfson J A, Bleich S N., Is cooking at home associated with better diet quality or weight-loss intention?, Public Health Nutrition, 18, 8, pp. 1397-1406, (2015)

[2]

Erlich R, Yngve A, Wahlqvist M L., Cooking as a healthy behaviour, Public Health Nutrition, 15, 7, pp. 1139-1140, (2012)

[3]

Kramer R F, Coutinho A J, Vaeth E, Et al., Healthier home food preparation methods and youth and caregiver psychosocial factors are associated with lower BMI in African American youth, The Journal of Nutrition, 142, 5, pp. 948-954, (2012)

[4]

Baillie L, Benyon D., Place and technology in the home, Computer Supported Cooperative Work, 17, 2, pp. 227-256, (2008)

[5]

Teixeira V, Voci S M, Mendes-Netto R S, Et al., The relative validity of a food record using the smartphone application My-FitnessPal, Nutrition & Dietetics, 75, 2, pp. 219-225, (2018)

[6]

Zhu F Q, Bosch M, Woo I, Et al., The use of mobile devices in aiding dietary assessment and evaluation, IEEE Journal of Selected Topics in Signal Processing, 4, 4, pp. 756-766, (2010)

[7]

Rohrbach M, Amin S, Andriluka M, Et al., A database for fine grained activity detection of cooking activities, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1194-1201, (2012)

[8]

Amft O, Troster G., Methods for detection and classification of normal swallowing from muscle activation and sound, Proceedings of the Pervasive Health Conference and Workshops, pp. 1-10, (2006)

[9]

Bedri A, Li D, Khurana R, Et al., FitByte: automatic diet monitoring in unconstrained situations using multimodal sensing on eyeglasses, Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1-12, (2020)

[10]

Khaloo P, Oubre B, Yang J, Et al., NOSE: a novel odor sensing engine for ambient monitoring of the frying cooking method in kitchen environments, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3, 2, (2019)

← 1 2 3 →