Conference PaperPDF Available

A Unified Blockchain Schema for Chronic Diet Management

May 2022

DOI:10.1109/CSCWD54268.2022.9776136

Conference: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Authors:

Usharani Hareesh Govindarajan

University of Shanghai for Science and Technology

Gagan Narang

Marche Polytechnic University

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Data, information, and technology are improving healthcare aspects such as fitness tracking, patient-doctor communication, and access to health records, etc. Diet management approaches based on a functional estimation involve clinical and computer-supported methods that are utilized in the strategy of treatment. Clinical methods such as dietary records, 24-hour recall, and food frequency are common in practice and majorly rely on a calorie-conscious intake, which though with a cautionary proceeding works as expected, but inherently lags a focus on the processing levels of the ingredients such as the quantifiable content of natural or processed food. Calories consumed from natural sources share contrast to processed food. The exploration, hence, needs a hybrid approach that combines the computer-supported systems to build on a processed food proportion estimation methodology. Emerging augmented reality technologies seamlessly merge real-world environments with computer-supported perceptual information have the potential to solve the problem enabling user decision support through a unified schema of nutritional information. This research presents (i) a blueprint for a unified schema that merges clinical and computer-supported methods that underlie the viable standard for a host of multi-point chronic diet management applications, followed by (ii) a work- in-progress outline of the essential function blocks, resources, clinical considerations, and integrations that form a technology stack for the estimation of processed and non-processed food towards a clinical conscious diet intake.

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A Unified Blockchain Schema for Chronic Diet

Management

Dali Zhang a, b

a) Shenzhen Research Institute, Shanghai

Jiao Tong University, Shenzhen, China

b) Sino-US Global Logistics Institute, Antai

College of Economics and Management,

Shanghai Jiao Tong University,

Shanghai,

zhangdl@sjtu.edu.cn

Gagan Narang

Institute of Informatics and

Communication,

University of Delhi,

New Delhi, India

gagan.narang@iic.ac.in

Usharani Hareesh Govindarajan*









hareesh.pillai@sjtu.edu.cn

Xiaojun Lu

Business School,

University of Shanghai for Science and

Technology,

Shanghai, China

171910077@st.usst.edu.cn

Yongmei Shi

Department of Clinical Nutrition,

School of Medicine, Ruijin Hospital

Affiliated to Shanghai Jiao Tong

University,

Shanghai, China

shi.yongmei@163.com

Abstract— Data, information, and technology are improving

healthcare aspects such as fitness tracking, patient-doctor

communication, and access to health records, etc. Diet

management approaches based on a functional estimation

involve clinical and computer-supported methods that are

utilized in the strategy of treatment. Clinical methods such as

dietary records, 24-hour recall, and food frequency are common

in practice and majorly rely on a calorie-conscious intake, which

though with a cautionary proceeding works as expected, but

inherently lags a focus on the processing levels of the ingredients

such as the quantifiable content of natural or processed food.

Calories consumed from natural sources share contrast to

processed food. The exploration, hence, needs a hybrid

approach that combines the computer-supported systems to

build on a processed food proportion estimation methodology.

Emerging augmented reality technologies seamlessly merge

real-world environments with computer-supported perceptual

information have the potential to solve the problem enabling

user decision support through a unified schema of nutritional

information. This research presents (i) a blueprint for a unified

schema that merges clinical and computer-supported methods

that underlie the viable standard for a host of multi-point

chronic diet management applications, followed by (ii) a work-

in-progress outline of the essential function blocks, resources,

clinical considerations, and integrations that form a technology

stack for the estimation of processed and non-processed food

towards a clinical conscious diet intake.

Keywords— Chronic diet management, common data model,

medical analytics pipeline, three-dimensional data visualization,

extended reality

I. INTRODUCTION

Diet management is an essential factor to lead a healthy

life. The growing knowledge and awareness towards the

effects of lifestyle, food choices, health and organ efficiency

have made people conscious of food and nutrition intake.

Thus, diet management and nutrition management have

gained enormous significance. Diet management is not just a

trend but an indispensable aspect in the health care sector.

Many people belonging to the vulnerable group in the

healthcare sector, such as older people, pregnant ladies, people

suffering from chronic diseases, etc. depend deeply on diet

monitoring. The authors covered chronic diseases and their

treatment and wellness and post-traumatic stress disorders

using emerging technologies [1]. In continuation to the work,

covering lifestyle-based diseases, the group require great

attention when it comes to diet management. Lifestyle

diseases share risk factors like prolonged exposure to three

modifiable behaviors including smoking, unhealthy diet, and

physical inactivity. Unmonitored consumption of processed

food can lead to heart disease, stroke, diabetes, obesity,

metabolic syndrome, chronic obstructive pulmonary disease,

some types of cancer and psychological disorders. The

combination of four healthy lifestyle factors – maintaining a

healthy weight, exercising regularly, eating a healthy diet, and

not smoking – appears to be associated with an 80% reduction

in the risk of developing the most common and deadly chronic

diseases [2]. The intake of fried food, oil, red meat and equally

important the quantity of processed or unprocessed material

increasingly become the agent of a segment of non-

communicable lifestyle-based diseases. Fig. 1 shows the

analysis of stakeholders in chronic disease diet management

usually done when a person is admitted to a hospital or is

under the supervision of medical personnel. The cycle

involves clinical monitoring of the diet by a dietician and

assigned physician, The monitoring needed is constant in the

case of vulnerable groups.

Fig. 1. Stakeholders in diet management of a chronic disease patient.

Thus, the self-monitoring method for diet management

becomes very important. Real-time object detection

techniques in recent years work as the core techniques of a lot

of vital applications, for example, facial feature detections [3].

Mobile object detection has further enlarged the scope for its

applications [4]. These recent technological innovations can

leverage both the advantages of AI in object detection and AR

in information presentation [5]. Such explorations are

iteratively developed for instance Virtual Reality Exposure

1384

Proceedin

s of the 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Desi

Authorized licensed use limited to: Universita' Politecnica delle Marche. Downloaded on December 09,2024 at 07:24:29 UTC from IEEE Xplore. Restrictions apply.

Full paper: www.doi.org/10.1109/CSCWD54268.2022.9776136

or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective

works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Full paper: www.doi.org/10.1109/CSCWD54268.2022.9776136

Therapy (VRET) is used as psychotherapy for phobias and

Post Traumatic Stress Disorder (PTSD) [16]. In VRET, a

patient is immersed in a programmed computer-generated

virtual environment that helps the person directly confront

feared situations or locations that may not be safe to encounter

in real life. There is strong evidence that shows that VRET is

useful for treating several different phobias and social anxiety.

The implementation, however, requires complex immersive

technology integration that necessitates the know-how of a

host of technologies. The use case however in case of diet

management is considerably narrow which can be attained

using a mobile application and hence does not considerably

add up to the operational costs as showcased through the scope

of this investigation. This research is organized into five

sections as follows. Section 2 presents the essential

background to understanding clinical approaches and

computer-supported approaches for chronic diet management.

Section 3 outlines a discussion on a blueprint for a unified

schema that merges clinical and computer-supported methods

that underlie the viable standard for a host of multi-point

chronic diet management applications. Section 4 describes the

methodology and the required functional blocks, resources,

integrations that effectively results in the processed food

estimation. Section 5, as the conclusion, presents the future

direction and the work in progress prototype design.

II. BACKGROUND

This section presents essential background related to

chronic diet management that is organized in two major

directions. Fig. 2 presents clinical approaches and computer-

supported two major approaches and methods along with

their functional classifications.

Fig. 2. Chronic diet management approaches.

Chronic diet management methods currently adopted by

medical practitioners in the clinical approaches are mostly

manual methods, for instance, Dietary Records (DR) useful

in obtaining detailed information about all foods and

beverages consumed over a one- or more-day period [6]. The

24-hour recall method consists of a structured questionnaire

that helps participants to recall the food and drinks consumed

in the previous day [7]. Food frequency is used to obtain

information on the frequency and, in some cases, the portion

size of food and beverage consumption over a specified

period, typically the previous month or year [8]. These

methods have been prevalent in the healthcare society for a

long time, but these methods have a scope of error. So, more

accurate and automatic methods for diet monitoring are being

investigated and developed to minimize the scope of human

error. Technological advancements in healthcare systems

have impacted the medical infrastructure holistically. Data,

information, and technology are improving healthcare

aspects such as planning in diet management, patient-doctor

communication, and access to health records.

Clinical methods majorly focus on a calorie-conscious

intake, which though with a cautionary proceeding works

greatly, but inherently lags a focus on the ingredients of the

consumed food. Another popular approach utilizes a popular

system to classify foods based on the quantifiable amount of

processed food. Food processing is a spectrum that ranges

from basic technologies like freezing or milling, to the

incorporation of additives that promote shelf stability or

increase palatability. The NOVA classification categorizes it

as unprocessed, processed culinary ingredients, processed

foods, and ultra-processed foods categories detailing the

degree to which a food is processed [9]. Generally,

emphasizing unprocessed or minimally processed foods in

the daily diet is optimal. Open and collaborative projects such

as Open Food Facts is a food product database that includes

ingredients, allergens, nutrition facts, and any other

information that can be found on product labels that can be

used on a commercial and non-commercial project, that

additionally provides Application Programming Interface

(API) for allowing developers to use this database in their

personalized programs to utilize the database [10]. The Siga

classification assigns food grades to ingredients based on the

degree of food processing. The Siga evaluation method works

by downgrading the ingredients and processes used in food

formulation [11]. ScanUp has created a database from the

area of its presence with the French food products,

aggregating nutritional and environmental indicators such as

nutri-score, degree of processing, eco-score to use this data to

inform consumers about the quality of their products via a

mobile application [12]. Simultaneously, assisting the food

manufacturers and distributors in their market knowledge and

innovation development by conducting studies.

However, the computer-supported methods have a data

aggregation problem. The mentioned methods have good

individual performance and are automated. The community,

in general, has seen increased adoption of medical sensors

that include blood pressure, electroencephalogram (EEG),

oxygen saturation, heart rate, magnetic field, temperature and

so on. The data generated with these individual sensors when

coupled with clinical approaches and the computer-supported

methods results in a hybrid approach that effectively merges

the qualities of both the approaches such as continuous

monitoring of the quantity and quality of the calories that are

consumed. Mobile object detection has broadened the scope

of its applications even further. An innovative augmented

reality object detection system that incorporates visual

Artificial Intelligence (AI) can take advantage of both the

advantages of AI in object detection and AR in information

presentation. This is attained through a unified schema that

merges clinical and computer-supported methods that

underlie the viable standard for a host of multi-point chronic

diet management applications. The consolidated diet

management approaches are tabulated and presented in Table

1 below. Additionally, the key characteristics of diet

management approaches are summarized.

Chronic diet

management

Clinical

approaches

Dietary Records

24-hour recall

Food frequency

Computer

supported

methods

NOVA

classification

Open Food Facts

Siga

Classification

ScanUp

1385

Authorized licensed use limited to: Universita' Politecnica delle Marche. Downloaded on December 09,2024 at 07:24:29 UTC from IEEE Xplore. Restrictions apply.

Full paper: www.doi.org/10.1109/CSCWD54268.2022.9776136

or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective

works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective

works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Bitcoin has been attracted attention to issue digital currency and also to manage transactions in a decentralized and trustless way without assuming any reliable third-party organization. To enhance the potential of Bitcoin, blockchain technologies have been studied to find more applications to smart contract, identification, and certificate which is valid even after the original issuer disappears. On the other hand, mobile crowdsensing is known as a method to collectively gather and share local data with the help of many and unspecified participants having their portable devices with sensors like smartphones to extract useful information from them. Expectation-Maximization (EM) algorithm is considered to estimate true information from task results from many independent workers and to evaluate workers’ scores. By the combination of blockchain and mobile crowdsensing technologies, a open data platform to save true information into the blockchain and issue currency to workers based on their reliability scores. In this research, we explain how to create this open data platform to generate Big and Long-time Open Data (BaLOD) to certify events that occurred in reality and everyone can validate the events.

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The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis. An artificial intelligence-powered microscope able to detect tumor cells in histopathology slides holds promise for accelerating pathology workflows for cancer diagnosis

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