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Published in: Wireless Personal Communications 3/2021

08-08-2019

Smart Care Environment with Food Recognition for Personalization Support: A Case Study of Thai Seniors

Authors: Punnarumol Temdee, Surapong Uttama

Published in: Wireless Personal Communications | Issue 3/2021

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Abstract

Like many countries worldwide, Thailand will become complete aging society shortly. The challenging of an aging society in the digital era is to enhance the quality of life for seniors through the employment of advanced and modern technology. This study proposes a smart care environment with food recognition module for personal healthcare purpose. More specifically, it is the mobile application for promoting personalized support for seniors. With context-aware perspective, the proposed environment employs clinical data and personal data for user modeling. It is designed to have the user-friendly interface providing convenient use for the seniors. Additionally, food recognition module is integrated for gathering real-time energy consumption with less distraction to the seniors. It is trained with a set of Thai food images using a convolution neural network. The case study is conducted with 50 Thai seniors in Chiang Rai, Thailand. Overall, the seniors strongly agree on both provided functional and personalized support. Also, they strongly agree that food recognition module can engage them to use this developed care environment.

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Literature
1.
go back to reference Temdee, P., & Prasad, R. (2018). Context-aware communication and computing: applications for smart environment. Berlin: Springer.CrossRef Temdee, P., & Prasad, R. (2018). Context-aware communication and computing: applications for smart environment. Berlin: Springer.CrossRef
2.
go back to reference Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: a patient-centered framework. Journal of General Internal Medicine, 28(3), 660–665.CrossRef Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: a patient-centered framework. Journal of General Internal Medicine, 28(3), 660–665.CrossRef
3.
go back to reference Vashist, S. K., Schneider, E. M., & Luong, J. H. (2014). Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics, 4(3), 104–128.CrossRef Vashist, S. K., Schneider, E. M., & Luong, J. H. (2014). Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics, 4(3), 104–128.CrossRef
4.
go back to reference Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research (Vol. 1, No. 2.1, pp. 2–1). Technical Report TR2000-381, Department of Computer Science, Dartmouth College. Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research (Vol. 1, No. 2.1, pp. 2–1). Technical Report TR2000-381, Department of Computer Science, Dartmouth College.
5.
go back to reference Bricon-Souf, N., Dufresne, E., Beuscart-Zephir, M. C., & Beuscart, R. (2003). Communication of informatiom in the homecare context. In The New Navigators: From Professionals to Pa-tients: Proceedings of MIE2003 (p. 95). Bricon-Souf, N., Dufresne, E., Beuscart-Zephir, M. C., & Beuscart, R. (2003). Communication of informatiom in the homecare context. In The New Navigators: From Professionals to Pa-tients: Proceedings of MIE2003 (p. 95).
6.
go back to reference Gans, D., Kralewski, J., Hammons, T., & Dowd, B. (2005). Medical groups’ adoption of elec-tronic health records and information systems. Health Affairs, 24(5), 1323–1333.CrossRef Gans, D., Kralewski, J., Hammons, T., & Dowd, B. (2005). Medical groups’ adoption of elec-tronic health records and information systems. Health Affairs, 24(5), 1323–1333.CrossRef
7.
go back to reference Bricon-Souf, N., & Newman, C. R. (2007). Context awareness in health care: A review. International Journal of Medical Informatics, 76(1), 2–12.CrossRef Bricon-Souf, N., & Newman, C. R. (2007). Context awareness in health care: A review. International Journal of Medical Informatics, 76(1), 2–12.CrossRef
8.
go back to reference Bricon-Souf, N., Renard, J. M., & Beuscart, R. (1999). Dynamic workflow model for complex activity in intensive care unit. International Journal of Medical Informatics, 53(2), 143–150.CrossRef Bricon-Souf, N., Renard, J. M., & Beuscart, R. (1999). Dynamic workflow model for complex activity in intensive care unit. International Journal of Medical Informatics, 53(2), 143–150.CrossRef
9.
go back to reference Reddy, M., Pratt, W., Dourish, P., & Shabot, M. M. (2002). Sociotechnical requirements analysis for clinical systems. Methods of Information in Medicine, 42(4), 437–444. Reddy, M., Pratt, W., Dourish, P., & Shabot, M. M. (2002). Sociotechnical requirements analysis for clinical systems. Methods of Information in Medicine, 42(4), 437–444.
10.
go back to reference Coiera, E. (2000). When conversation is better than computation. Journal of the American Medical Informatics Association, 7(3), 277–286.CrossRef Coiera, E. (2000). When conversation is better than computation. Journal of the American Medical Informatics Association, 7(3), 277–286.CrossRef
11.
go back to reference Spencer, R., & Logan, P. (2002). Role-based communication patterns within an emergency department setting. In HIC 2002: Proceedings: Improving Quality by Lowering Barriers (p. 166). Spencer, R., & Logan, P. (2002). Role-based communication patterns within an emergency department setting. In HIC 2002: Proceedings: Improving Quality by Lowering Barriers (p. 166).
12.
go back to reference Kim, J., Lee, D., & Chung, K. Y. (2014). Item recommendation based on context-aware model for personalized u-healthcare service. Multimedia Tools and Applications, 71(2), 855–872.CrossRef Kim, J., Lee, D., & Chung, K. Y. (2014). Item recommendation based on context-aware model for personalized u-healthcare service. Multimedia Tools and Applications, 71(2), 855–872.CrossRef
13.
go back to reference Chondamrongkul, N. (2017, March). Personalized healthcare with context-awareness platform. In International conference on digital arts, media and technology (ICDAMT) (pp. 427–431). IEEE. Chondamrongkul, N. (2017, March). Personalized healthcare with context-awareness platform. In International conference on digital arts, media and technology (ICDAMT) (pp. 427–431). IEEE.
14.
go back to reference Yang, S., Chen, M., Pomerleau, D., & Sukthankar, R. (2010, June). Food recognition using statistics of pairwise local features. In 2010 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2249–2256). IEEE. Yang, S., Chen, M., Pomerleau, D., & Sukthankar, R. (2010, June). Food recognition using statistics of pairwise local features. In 2010 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2249–2256). IEEE.
15.
go back to reference Farinella, G. M., Allegra, D., Moltisanti, M., Stanco, F., & Battiato, S. (2016). Retrieval and classification of food images. Computers in Biology and Medicine, 77, 23–39.CrossRef Farinella, G. M., Allegra, D., Moltisanti, M., Stanco, F., & Battiato, S. (2016). Retrieval and classification of food images. Computers in Biology and Medicine, 77, 23–39.CrossRef
16.
go back to reference Kong, F., He, H., Raynor, H. A., & Tan, J. (2015). DietCam: Multi-view regular shape food recognition with a camera phone. Pervasive and Mobile Computing, 19, 108–121.CrossRef Kong, F., He, H., Raynor, H. A., & Tan, J. (2015). DietCam: Multi-view regular shape food recognition with a camera phone. Pervasive and Mobile Computing, 19, 108–121.CrossRef
17.
go back to reference Anthimopoulos, M. M., Gianola, L., Scarnato, L., Diem, P., & Mougiakakou, S. G. (2014). A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE Journal of Biomedical and Health Informatics, 18(4), 1261–1271.CrossRef Anthimopoulos, M. M., Gianola, L., Scarnato, L., Diem, P., & Mougiakakou, S. G. (2014). A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE Journal of Biomedical and Health Informatics, 18(4), 1261–1271.CrossRef
18.
go back to reference Martinel, N., Piciarelli, C., & Micheloni, C. (2016). A supervised extreme learning committee for food recognition. Computer Vision and Image Understanding, 148, 67–86.CrossRef Martinel, N., Piciarelli, C., & Micheloni, C. (2016). A supervised extreme learning committee for food recognition. Computer Vision and Image Understanding, 148, 67–86.CrossRef
19.
go back to reference He, H., Kong, F., & Tan, J. (2016). Dietcam: Multiview food recognition using a multikernel svm. IEEE Journal of Biomedical and Health Informatics, 20(3), 848–855.CrossRef He, H., Kong, F., & Tan, J. (2016). Dietcam: Multiview food recognition using a multikernel svm. IEEE Journal of Biomedical and Health Informatics, 20(3), 848–855.CrossRef
20.
go back to reference Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., et al. (2018). A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Transactions on Services Computing, 11(2), 249–261.CrossRef Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., et al. (2018). A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Transactions on Services Computing, 11(2), 249–261.CrossRef
21.
go back to reference Zhang, W., Zhao, D., Gong, W., Li, Z., Lu, Q., & Yang, S. (2015). Food image recognition with convolutional neural networks. In 2015 IEEE 12th international conference on ubiquitous intelligence and computing and 2015 (UIC-ATC-ScalCom) (pp. 690–693). Zhang, W., Zhao, D., Gong, W., Li, Z., Lu, Q., & Yang, S. (2015). Food image recognition with convolutional neural networks. In 2015 IEEE 12th international conference on ubiquitous intelligence and computing and 2015 (UIC-ATC-ScalCom) (pp. 690–693).
22.
go back to reference Yanai, K., & Kawano, Y. (2015). Food image recognition using deep convolutional network with pre-training and fine-tuning. In 2015 IEEE international conference on multimedia expo workshops (ICMEW) (pp 1–6). Yanai, K., & Kawano, Y. (2015). Food image recognition using deep convolutional network with pre-training and fine-tuning. In 2015 IEEE international conference on multimedia expo workshops (ICMEW) (pp 1–6).
23.
go back to reference Qayyum, O., & Şah, M. (2018). Ios mobile application for food and location image prediction using convolutional neural networks. In 2018 IEEE 5th international conference on engineering technologies and applied sciences (ICETAS) (pp. 1–6). Qayyum, O., & Şah, M. (2018). Ios mobile application for food and location image prediction using convolutional neural networks. In 2018 IEEE 5th international conference on engineering technologies and applied sciences (ICETAS) (pp. 1–6).
24.
go back to reference Hnoohom, N., & Yuenyong, S. (2018). Thai fast food image classification using deep learning. In 2018 International ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI-NCON) (pp. 116–119). Hnoohom, N., & Yuenyong, S. (2018). Thai fast food image classification using deep learning. In 2018 International ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI-NCON) (pp. 116–119).
25.
go back to reference Termritthikun, C., & Kanprachar, S. (2017). Accuracy improvement of Thai food image recognition using deep convolutional neural networks. In 2017 international electrical engineering congress (IEECON) (pp. 1–4). Termritthikun, C., & Kanprachar, S. (2017). Accuracy improvement of Thai food image recognition using deep convolutional neural networks. In 2017 international electrical engineering congress (IEECON) (pp. 1–4).
26.
go back to reference Temdee, P., & Uttama, S. (2017, October). Food recognition on smartphone using transfer learning of convolution neural network. Global Wireless Summit 2017 (GWS2018). Temdee, P., & Uttama, S. (2017, October). Food recognition on smartphone using transfer learning of convolution neural network. Global Wireless Summit 2017 (GWS2018).
27.
go back to reference Subhi, M. A., & Ali, S. M. (2018). A deep convolutional neural network for food detection and recognition. In 2018 IEEE-EMBS conference on biomedical engineering and sciences (IECBES) (pp 284–287). Subhi, M. A., & Ali, S. M. (2018). A deep convolutional neural network for food detection and recognition. In 2018 IEEE-EMBS conference on biomedical engineering and sciences (IECBES) (pp 284–287).
28.
go back to reference Fadhilah, H., Djamal, E. C., Ilyas, R., & Najmurrokhman, A. (2018). Non-halal ingredients detection of food packaging image using convolutional neural networks. In 2018 International symposium on advanced intelligent informatics (SAIN) (pp. 131–136). Fadhilah, H., Djamal, E. C., Ilyas, R., & Najmurrokhman, A. (2018). Non-halal ingredients detection of food packaging image using convolutional neural networks. In 2018 International symposium on advanced intelligent informatics (SAIN) (pp. 131–136).
29.
go back to reference Pandey, P., Deepthi, A., Mandal, B., & Puhan, N. B. (2017). Foodnet: Recognizing foods using ensemble of deep networks. IEEE Signal Processing Letters, 24(12), 1758–1762.CrossRef Pandey, P., Deepthi, A., Mandal, B., & Puhan, N. B. (2017). Foodnet: Recognizing foods using ensemble of deep networks. IEEE Signal Processing Letters, 24(12), 1758–1762.CrossRef
30.
go back to reference Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826). Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
Metadata
Title
Smart Care Environment with Food Recognition for Personalization Support: A Case Study of Thai Seniors
Authors
Punnarumol Temdee
Surapong Uttama
Publication date
08-08-2019
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2021
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06636-z

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