Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone

Recently, activity recognition using built-in sensors in a mobile phone becomes an important issue. It can help us to provide context-aware services: health care, suitable content recommendation for a user’s activity, and user adaptive interface. This paper proposes a layered hidden Markov model (HMM) to recognize both short-term activity and long-term activity in real time. The first layer of HMMs detects short, primitive activities with acceleration, magnetic field, and orientation data, while the second layer exploits the inference of the previous layer and other sensor values to recognize goal-oriented activities of longer time period. Experimental results demonstrate the superior performance of the proposed method over the alternatives in classifying long-term activities as well as short-term activities. The performance improvement is up to 10 % in the experiments, depending on the models compared.