Classification of Daily Life Activities by Decision Level Fusion of Inertial Sensor Data

The fusion of inertial sensor data is heavily used for the classification of daily life activities. The knowledge about the performed daily life activities is mandatory to give physically inactive people feedback about their individual quality of life. In this paper, four inertial sensors were placed on wrist, chest, hip and ankle of 19 subjects, which had to perform seven daily life activities. Each sensor node separately performed preprocessing, feature extraction and classification. In the final step, the classifier decisions of the sensor nodes were fused and a single activity was predicted by majority voting. The proposed classification system obtained an overall mean classification rate of 93.9 % and was robust against defect sensors. The system allows an easy integration of new sensors without retraining of the complete system, which is an advantage over commonly used feature level fusion approaches.