Jamsri, Pornsuree
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Jamsri, Pornsuree
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pornsuree.ja@kmitl.ac.th
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Item type:Publication, A Personalized Food Recommendation Chatbot System for Diabetes Patients(2020-01-01); Diabetes is a disorder of the body that is unable to produce enough insulin. Diabetes causes the body to improperly burn sugar, which affects the blood sugar level leaving a sugar residue. Diabetes is related to genes, body weight, lack of exercise and aging. When patients with diabetes neglect good nutrition this can cause many health problems. This research, therefore, develops a chatbot named “Waan-Noy” to recommend a diet suitable for individuals with diabetes and build a cooperative health society. Our chatbot recommends personalized eating. It is suitable for use by diabetes patients as indicated by their evaluations. Through use of nutrition therapy controls, Waan-Noy recommends specific foods. The user’s evaluation is divided into 3 areas: content, design, and implementation to determine user degree of satisfaction with Waan-Noy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Differentiative Feature-based Fall Detection System(2021-01-01); Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a machine learning-based algorithm can increase effectiveness of fall detection model into device's effectiveness. The available technology is embedded accelerometer and gyroscope sensor into a smartphone provide benefit dataset. These data can be used for reducing and managing serious injury and caregiver can assist on time. The leverage performance of a Smart Steps application by including the essence of machine learning algorithm and 5-fold cross validation rises accuracy in fall detection. Thus, this paper proposed a novel method of 4 binary classification-Decision Tree, SVM, K-Nearest Neighbors, and Gradient Boosting. The focusing on acceleration magnitude, angular velocity magnitude, and difference between pre-current, current-post values are taken into account in the study. The opened dataset, MobiFall, are split into 2 groups 1) train group 80% and 2) test group 20% for gathering effectiveness result. The model's assessment measures in 4-dimension 1) accuracy, 2) precision, 3) recall and, 4) F1-Score. The results demonstrates increasing values that 95.65% of accuracy, 91.20% precision, 90.86% recall and, 91.03% F1-Score. The fall detection of the study can conclude that the machine learning-based algorithm offers more accuracy and effectively than threshold-based algorithm.