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Assessment and identification of human activity and posture with triaxial accelerometer can provide information about the health state and the chronic care. This paper proposes a human state recognition algorithm based on Kalman filter (SRKF), which could identify steady state and state transition in real time. In this study, an automatic state-recognition system consisting of a Bluetooth module and a smart phone is developed. The Bluetooth module with a triaxial accelerometer was placed on the body and the raw sensor data was transported to and processed on the smart phone. Associated kinematics characteristic of human activity and the accelerometer signal, the results of amplitude function of vector CSVM were processed by Kalman filter to identify human state. Data were collected from ten adults in unsupervised environment. Experiment result shows that the algorithm has achieved better performance on the smart phone with limited computing and storage capacity.

A Real-time Algorithm Based on Triaxial Accelerometer for the Detection of Human Activity State