Diet monitoring and management of diabetic patient using robot assistant based on Internet of Things

Mall, Shreejay; Gupta, Mansi; Chauhan, Rahul

doi:10.1109/icetcct.2017.8280339

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…A robot is intelligent in the sense that it has built-in watching and detecting capabilities, as well as the ability to gather sensor data from various sources and fuse it for the device's “acting” purpose. Mall et al [ 30 ] introduced the e-health mind stage by employing robots that were connected via IoT to provide personalized varied care methods, particularly to diabetes patients. The robot is equipped with sensors that monitor the diabetic's medical and dietary status, providing them with comprehensive multidimensional care.…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy

Dash

Routray

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy

Dash

Routray

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The application of robots should not be limited to stand-alone operation, as this would limit developmental possibilities. Many robot applications combine robots with blood pressure meters, weight scales, and muscle meters to provide multi-dimensional care through the Internet [36]. They also combine social robots with smartphones and smart bracelets to implement a more systematic, integrated approach to disease management [37].…”

Section: Discussionmentioning

confidence: 99%

Robot-enhanced diabetes care for middle-aged and older adults living with diabetes in the community: A small sample size mixed-method evaluation

et al. 2022

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Purpose This study assessed robot-enhanced healthcare in practical settings for the purpose of community diabetes care. Methods A mixed method evaluation collected quantitative and qualitative data on diabetes patients over 45 (N = 30) and community pharmacists (N = 10). It took 15–20 min for the diabetes patients to interact with the robot. Before and after the interaction, questionnaires including a diabetes knowledge test, self-efficacy for diabetes, and feasibility of use of the robot was administered. In-depth interviews with both pharmacists and patients were also conducted. Results After interacting with the robot, a statistically significant improvement in diabetes knowledge (p < .001) and feasibility of the robot (p = .012) was found, but self-efficacy (p = .171) was not significantly improved. Five themes emerged from interviewing the diabetes patients: Theme 1: meets the needs of self-directed learning for the elderly; Theme 2: reduces alertness and creates comfortable interaction; Theme 3: vividness and richness enhance interaction opportunities; Theme 4: Robots are not without disadvantages, and Theme 5: Every person has unique tastes. Three themes emerged from interviewing pharmacists: Theme 1: Technology must meet the real needs of the patient; Theme 2: creates new services, and Theme 3: The use of robots must conform to real-life situations. Conclusions Both the diabetes patients and the pharmacist reported more positive feedback on the robot-enhanced diabetes care than concerns. Self-directed learning, comfortable interaction, and vividness were the most focuses when using robot to enhance self-management for the patients. Pharmacists were most receptive to fit conforming with reality and creating new services.

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“…Neerincx (2019) [26] Intelligent Assistant for a healthy Lifestyle Hybrid AI approach Mall (2017) [27] Diet Monitoring Medical sensors for Blood Glucose monitoring Steinert (2017) [28] Self-monitoring smartphone App…”

Section: Reference Ai System Techniquementioning

confidence: 99%

A Survey on Personalization of Diabetes Treatment using Artificial Intelligence Techniques

Ahmed,

Zaghloul,

Abd El Fattah

2023

Benha Journal of Applied Sciences

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Diabetes mellitus is a disease caused by uncontrolled diabetes that can lead to multiple organ failure in patients. Fortunately, recent advancements in artificial intelligence have made it possible to diagnose and detect diabetic disease early on. Numerous articles are currently being published on the use of artificial intelligence and machine learning techniques for automated detection, diagnosis, and personalized treatment and management of diabetes. This survey examines technologies for personalized diabetes treatment from five distinct perspectives: blood glucose prediction, glycemic variability detection, hyperglycemia detection, insulin controller therapy, and pharmacogenetics. The treatment of diabetes is dependent on various medical, demographic, and lifestyle factors, including diabetes type, age, body weight, duration of diabetes, comorbidities, blood sugar, physical activity, and diet. Artificial intelligence is regarded as a valuable technology to aid in diabetes treatment. This survey offers a comprehensive overview of techniques for diabetes detection and personalized treatment, which may prove beneficial to the scientific community focused on automatic diabetes detection and personalized treatment.This survey furnishes a comprehensive outline of techniques for the detection of diabetes and personalized treatment, which can prove immensely beneficial to the scientific community working on automatic diabetes detection and personalized treatment.

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Diet monitoring and management of diabetic patient using robot assistant based on Internet of Things

Cited by 12 publications

References 8 publications

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Robot-enhanced diabetes care for middle-aged and older adults living with diabetes in the community: A small sample size mixed-method evaluation

A Survey on Personalization of Diabetes Treatment using Artificial Intelligence Techniques

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