Neural-network based adaptive context prediction model for ambient intelligence

Abstract

To overcome the limitations of the conventional medical service in terms of ageing and chronic diseases, AmI-based precision medicine has drawn particular attention. Precision medicine is a customized personal medical service using various information technologies such as personal health device, AI algorithm, image recognition, voice recognition, and natural language processing. In particular, the information technologies for follow-up care services for patients, such as context awareness, context information, and inference rules, are required. In PHD, contexts such as variable data include blood pressure, BMI, blood sugar, weather, and food. It has time-series characteristics, meaning that it changes often with time. Other kinds of health-related information, such as age, family history, smoking, and residential area, are intermittently changed. For inference that is highly related to a user, the context collected through AmI is presented with ontology. Ontology consists of a user’s ambient data, weather data, and lifelog. Context is changed along with a user’s ambient conditions and time. An inference engine is used to create the knowledge base and predict a change. This study proposes a neural-network based adaptive context prediction model for ambient intelligence. This is a learning model using neural network to calculate the similarity for recommendation in a mining lifecare platform. In a conventional prediction procedure, an error is used to update a weight. The proposed model learns the similarity weight of the users to become adapted to the user’s ambient. Based on the knowledge base, user clustering and deviation from mean are applied to calculate the similarity weight. Collaborative filtering technology is used to predict a user’s context and learn the similarity weight repeatedly using a neural network. According to the performance evaluation, the proposed neural-network based similarity weight method had the highest accuracy of prediction when the learning rate was 0.001. Consequently, we found that AmI is a new added-value technology to maintain a healthy lifestyle and contributes to developing the healthcare industry and improving the quality of life.