| Scope and method of study. In this thesis, we model the series of human activity as Markov process. Hidden Markov Model (HMM) is used for human activities recognition. Acceleration data which are continuously collected from wearable sensor mounted on human body are imported as observation sequence for HMM. HMM is established and applied to recover the hidden states in human activity recognition. In the second part of this thesis, we apply Extended Kalman Filter (EKF) for indoor target localization. We use Received Signal Strength Indicator (RSSI) to measure the direct distance between target node and anchor nodes. The measured distance is used as the measurement function in EKF. Besides, acceleration data of target node is recorded for system input in EKF as well. In this way, we demonstrate that EKF gives reasonably accurate estimation for tracking the target node in indoor environments.;Findings and conclusions. The experimental results show that Hidden Markov Model (HMM) outperforms using acceleration data directly in human activities recognition. We also demonstrate the accuracy of mounting one, two and three sensors on human body and the accuracy of mounting sensors in different parts of human body. Extended Kalman Filter (EKF) obtains reasonable accuracy in tracking target node within a relatively small indoor environment. HMM and EKF methods can be applied to many areas such as patient monitoring, firefighter monitoring and so on. |
