Accidental falls are common causes of serious injury and health threats in the elder population. To deliver adequate medical support, the robust and immediate falls detection is important. Since the fall detection in the elderly remains a major challenge in the public health domain, effective fall-detection will provide urgent support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system placing an accelerometer on the head level and using an algorithm to distinguish between falls and daily activities. The experimental results have demonstrated the proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effectively but also potable. It fulfills the requirements of fall detection.

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Laparoscopic-assisted surgery and renal function affected the baseline trajectory of IVPCA demand over time, but had no significant effect on its shape.

Abstract. The use of information technologies in various business areas is emerging in recent years. With the development of information technology, how to find useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully applied to many fields and analytic tools. Clustering analysis which tries to segment data into homogeneous clusters is one of the most useful technologies in data mining methods. Market segmentation is among the important issue of most companies. Market segmentation relies on the data clustering in a huge data set. In this study, we propose a clustering system which integrated particle swarm optimization and honey bee mating optimization methods. Simulations for a benchmark test functions show that our proposed method possesses better ability to find the global optimum than other well-known clustering algorithms. The results show that system through PSHBMO can effectively find the global optimum solution, and extend the application of market segmentation to solve the RFM model.

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