ABSTRACT
A good performance of motion capture, which belongs to body sensor network, depends on a reasonable design of MAC protocol. The purpose of this study is to design a reliable and highly extensible protocol for applying in motion capture. The proposed MAC protocol can easily be actualized by the timer in the chip. With this MAC protocol, the network would be built quickly. One or more nodes could be added easily in the net or deleted randomly from the net. In order to verify the superiority of this protocol, a series of experiments were designed. The results showed that the mean of simulation receive frames for node1-node7 from each stage were very close to the original frames. In addition, the final Pocket Loss Rates for node1-node7 were 0.081%, 0.175%, 0.143%, 0.249%, 0.248%, 0.044% and 1.897%, which could be in the error-allowed range. Thus, this protocol is stable and reliable, which can be widely used to capture human movement signal.
Subject(s)
Motion , Algorithms , Computer Communication Networks , Wireless TechnologyABSTRACT
Falls are a main cause of trauma and death. The purpose of this study is to adopt unique resultant acceleration and attitude angles to distinguish falls from activities of daily life before impact. In this study, we developed a wearable action recognition system to acquire action data. The moving average filter was employed to deal with raw data, and then complementary filter was adopted to compromise sensor data for attitude angles. The real-time detection algorithm embedded in this device was applied to recognize six actions based on processed data. Eight subjects (five males, three females) participated in the experiment. The optimal features and related thresholds were extracted. In addition, the real-time action detection results indicated that the real-time action recognition model reached an accuracy of 96.25%, with 98% for male and 93.3% for female. Thus, our device potentially achieves a high sensitivity of fall-related actions recognition.
