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With the increasing popularity of wristbands and their applications on the health monitoring, the recognition of various activities has become a significant feature which helps the users to assess and review their daily practices [1]. As a result, it can improve their health via the recorded information. The core task of the activity recognition is to automatically classify a large amount of motion data recorded by the accelerometers to one of the predefined activity classes such as eating, walking, etc. In contrast to the conventional recognition approaches [2], the algorithms are required to be simple, efficient and accurate as the battery powers and the allowable computational powers of the wristbands are very limited. Some approaches directly transfer the motion data to the mobile phones by the bluetooth so that more sophisticated computations can be performed. Nevertheless, the transmission of the raw digital motion data can consume a lot of power and occupy a considerable bandwidth. This paper proposes an algorithm that highly reduces the communications between the wristbands and the mobile phones by firstly preprocessing and labeling the raw motion data based on their characteristics. Only the labels and some encoding values are transferred to the mobile phones for performing the classification. In order to achieve the high accuracy, the classification is performed by combining different approaches such as the histogram approach and the Bayesian classifier approach. The obtained results show that the proposed approach achieves a good accuracy, which just uses some labels and the encoding value information.

Two series of chalcogenide glasses in Ge-Sb-S ternary system were synthesized with melt-quenching method. The phycochemical properties and spectral characteristic of glasses with different content of Ge and Sb were obtained with a series of measurements, and the systematic analysis on the change of optical properties was conducted in terms of microstructure of glasses combined with the Raman spectra. With constraint theory based on mean coordination number (Z), we described the variation trend of network structure directly. It was found that as long as the value of Z reaches 2.6, new vibration peaks would be formed in the Raman spectra indicating the presence of threshold behavior as well as the change of the network structure of the Ge-Sb-S glasses which could be expressed in a specific varition in the number of metal bonds and the nonmetal bonds. The appearance of new functional groups in the network have changed the total bond energy of glasses, and then affected the energy band structure of glasses representing the corresponding threshold behavior of the value of optical band gap (Eopg).