A cognitive-automated ingestive behavior and physical activity monitoring system using IoT
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Keywords
Neural Network, Discrimination of Saliva, Swallowing detection, Chewing analysis, Calorie detection
Abstract
Background and aim: To have a healthy lifestyle and to reduce the risk of chronic diseases, a fully balanced diet with enough quantity of nutrients is required. Maintaining the required levels of consumption of food and regularity in eating habits is important for preserving a healthy life and live without any health-related diseases. In regards to the above statement, the proposed work has been defined to provide novel smart dietary solutions and to monitor and detect the nutrition intake each day. Methods: Comfortable sensors are worn, used to record the observations in the form of signals during the consumption of foods. The data are pre-processed, and models are proposed to identify eating bouts and swallowing events, using the extracted features. The human body also needs physical movements which are recognized from an accelerometer to analyze the physical behavior. On the aggregation of both the eating and physical activity analyses, this paper proposes to monitor the ingestive habits and physical behavior related to health. Results: The detection of chewing occurrences was classified by RMWC, obtaining an accuracy of 97% while the determination of swallowing sounds to obtain accurate chewing rate, was performed by the proposed Temporally Efficient Bidirectional (TEB) algorithm attaining a higher accuracy of 97.51%. Conclusions: In this paper, misclassification of saliva from normal swallowing has been overcome. On the contrary, physical activities have been determined by constructing a hierarchy of clusters. Thus, the proposed paradigm determines the ingestive behavior and the energy balance by generating the daily report for the user in an effective way.
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