Heterogeneous multimodal sensors based activity recognition system

Activity recognition system is the key part in E-Health field. Traditional system needs more labeled training data to meet higher recognition accuracy. This means more calibration effort and time consumption. In this paper, with collaboration of heterogeneous multimodal sensors like a microphone, a camera and an accelerometer etc, we propose to design and implement a system to reduce the required amount of labeled data as well as achieve even better performance than traditional systems. The system consists of three phases: collaborative data collection, collaborative classifier training and collaborative classifier combination. The experimental results validate that with only 9% labeled data, our system can obtain as high accuracy as other systems which use 100% unimodal labeled data.