![]() The finite-state machine did not improve the results. The highest F$_1$-score found using only machine learning algorithms is 0.82 for the classification of eating gestures, 0.94 for chewing food and 0.53 for swallowing food. In addition, a finite-state machine is implemented to capture the sequential nature of eating by filtering false positive eating event classifications. Different machine learning configurations are implemented to determine the optimal feature vector length, sensor combination and to test the generalisability of the model. These features are fed into the classification algorithms to classify the windows as eating events or non-eating events. Distinguishing characteristics of the windows are captured by creating features. The sensor data is filtered, transformed and then split into windows. ![]() The sensor data of different sensors is combined to find out how much they complement each other.Īn experiment was conducted in which six participants are asked to eat a croissant and a bowl of yogurt with pieces of apple. The respiratory inductance plethysmography sensor consists of two bands that measure lung volume change to detect swallowing food. A piezoelectric sensor worn on the jaw is used to recognise chewing food. The smartwatch worn on the wrist consists of a gyroscope and an accelerometer in three axes to detect eating gestures. This leads to the introduction of automatic dietary monitoring, which aims to objectively measure on the consumption of food by determining the timing of food consumption and the quantity and type of food consumed.Ī sensing system consisting of a smartwatch, a piezoelectric sensor and a respiratory inductance plethysmography sensor has been introduced to detect eating events. Food consumption can be registered using a food diary, but entries are often inconsistent by over- or underestimating the amount of food consumed or by forgetting to register consuming food at all. The most important principle is to burn more calories than you consume. ![]() People are advised to exercise more, eat less but more regularly and avoid unhealthy food to lose weight. Overweight and obesity are a large and increasing problem worldwide and have been associated with a range of diseases. A wearable sensor system for eating event recognition using accelerometer, gyroscope, piezoelectric and lung volume sensors.
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