| Summary: | 碩士 === 國立中央大學 === 系統生物與生物資訊研究所 === 104 === The thesis proposes an unsupervised leaning algorithm for human posture pattern recognition constructed in the framework of Sensor Fusion. The framework included a small wearable biomedical electronic sensing device and a corresponding analytical technique of machine learning. By means of the proposed sensing device, the sensed electronic signals would be transmitted to the personal electronic carriers through the cloud service. Meanwhile, the proposed algorithm would analyze the cluster behavior so that the personal database and posture patterns would then be constructed wherein. The algorithm was based on the density functional theory in the Quantum Mechanics. Using the Hamiltonian density functional and the Lagrangian density functional, the cluster number and the corresponding boundary of each cluster of a specific data probability density function I can be estimated. The proposed algorithm can not only dramatically reduce the computational complexity and reinforce the reliability and the efficiency, but also estimate the significance and the connectivity within data points to find the boundaries of clusters for further data clustering. For long-term observations, the proposed technique can be used to construct a customized database for sensing and analyzing the personal posture patterns, and also to classify each posture frequency by updating the database. Under this scenario, the possible lesions might be specified, such as the scoliosis and so forth. For short-term observations, it can be used for analysis of accident events and alert notification, such as elderly falls, children turn around, and so forth. Therefore, the proposed technique can save unnecessary costs caused from human interventions, material resources, and the occupation of storage. It also has higher credibility and objectivity, and even for the further commercialization.
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