Pervasive Eating Habits Monitoring Through An Acoustic Wearable Sensor

Nutrition-related diseases are nowadays a main threat to human health and pose great challenges to medical care.A crucial step to solve the problems is to monitor the daily food intake of a person precisely and conveniently.For this purpose,we present AutoDietary,a wearable system to monitor and recognize food intakes in daily life.An embedded hardware prototype is developed to collect food intake sensor data,which is highlighted by a high-fidelity microphone worn on the subject’s neck to precisely record acoustic signals during eating in a non-invasive manner.The acoustic data are pre-processed and then sent to a smartphone via bluetooth,where food types are recognized.Specifically,we use hidden Markov models to identify chewing or swallowing events,which are then processed to extract their time/frequency-domain and non-linear features.A light-weight decision tree based algorithm is adopted to recognize the type of food intake.We also developed an application on the smartphone which aggregates the food intake recognition results in a userfriendly way and provides suggestions on healthier eating,such as better eating habits or nutrition balance.Intensive experiments show that the accuracy of food type recognition by AutoDietary is 84.9%,and those to classify liquid and solid food intakes are up to 97.6%and 99.7%.respectively.