Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data

As the number of smartphone users has grown recently, many context-aware services have been studied and launched. Activity recognition becomes one of the important issues for user adaptive services on the mobile phones. Even though many researchers have attempted to recognize a user's activities on a mobile device, it is still difficult to infer human activities from uncertain, incomplete and insufficient mobile sensor data. We present a method to recognize a person's activities from sensors in a mobile phone using mixture-of-experts (ME) model. In order to train the ME model, we have applied global-local co-training (GLCT) algorithm with both labeled and unlabeled data to improve the performance. The GLCT is a variation of co-training that uses a global model and a local model together. To evaluate the usefulness of the proposed method, we have conducted experiments using real datasets collected from Google Android smartphones. This paper is a revised and extended version of a paper that was presented at HAIS 2011.