Health monitoring using gait phase effects

The need to monitor patients after they leave the hospital or clinics is of growing concern and doctors may need the facility to monitor certain patients more than others. For example patients with high blood pressure are sometimes fitted with a mobile monitor which can be used to track the patients blood pressure over time. Patients suffering from depression, however, may also need to be monitored to ensure that they are in a happy emotional state. In this paper we introduce an alternative approach to mood detection and tracking based on built-in accelerometer sensors found in common mobile phones. Our method can be seen to compliment the need to monitor such patients allowing for doctors to get in touch with them when their mood has altered. We build a system based on neural networks which takes the gait information and learns the associated mood of the user. This trained model is then used to detect the mood of the individuals. We demonstrate preliminary results on mood detection using a mobile prototype system.