A Novel Energy-Efficient Approach for Human Activity Recognition.

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.

Hemoglobinopathy: molecular epidemiological characteristics and health effects on Hakka people in the Meizhou region, southern China.

Background: Hemoglobinopathies are the most common inherited diseases in southern China. However, there have been only a few epidemiological studies of hemoglobinopathies in Guangdong province.

Materials And Methods: Peripheral blood samples were collected from 15299 "healthy" unrelated subjects of dominantly ethnic Hakka in the Meizhou region, on which hemoglobin electrophoresis and routine blood tests were performed.