| 1 | Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and Univariate Feature Selection显示文摘In this paper,we propose a novel study for gesture identification using surface electromyography(sEMG)signal,and the raw sEMG signal and the sEMG envelope signal are collected by the sensor at the same time.An efficient method of gesture identification based on the combination of two signals using supervised learning and univariate feature selection is implemented.In previous research techniques,researchers tend to use the raw sEMG signal and extract several constant features for classification,which inevitably causes a result of ignoring individual differences.Our experiment shows that both the optimal feature set and redundant feature set are not same for different subjects.In order to address this problem,we extract all the common features from two signals,up to 76 features,most of which has been established as the common EMG-based gesture index.In addition,extracting too many features in an application can reduce operational efficiency,so we apply for feature selection to get the optimal feature set and decrease the number of extracting feature.As a result,the combination of two signals is better than using a single signal.The feature selection can be used to select optimal feature set from all features to achieve the best classification performance for each subject.The experimental results demonstrate that the proposed method achieves the performance with the highest accuracy of 95%for identifying up to nine gestures only using two sensors.Finally,we develop a real-time intelligent sEMG-driven bionic hand system by using the proposed method. | Shili Liang Yansheng Wu Jianfei Chen Ling Zhang Peipei Chen Zongqian Chai Chunlei Cao | 2019 | Journal of Bionic Engineering2019，16，4： | 4 |
| 2 | A decomposition analysis of recent health expenditure growth in China: is population ageing a significant effecting factor?显示文摘Since the new round of health system reform,the annual average growth rate of health expenditure in real term in China was 10.5%,which is much faster than that of any other Asian countries.The aim of this study is to analyze major effect-ing factors include population ageing’s contribution to health expenditure growth,as population ageing is accelerating and considered as a major driver of health expenditure growth in China.A component based health expenditure model was developed in this study and five major factors were employed,namely popula-tion size,population structure,disease prevalence rate,excess health price infla-tion(EHPI)and expenditure per prevalent case.Then Das Gupta’s decomposition method was applied to decompose the health expenditure growth into the five factors.Results shows that expenditure per prevalent case was the major factor,which accounted for 59.6%of the health expenditure growth.21.2%of the health expenditure growth was driven by population ageing,followed by EHPI(11.2%),population growth(5.4%)and disease prevalence rate(2.6%).Population age-ing affected circulatory diseases the most,which caused 5.2%of the difference in health expenditure,followed by neoplasms(2.9%),respiratory diseases(2.0%),digestive diseases(1.8%),and endocrine(1.5%).Our work highlights that meas-ures should be taken to reduce risk factors of major non-communicable disease to promote healthy ageing,and it is fundamental to address growth in expenditure per case,especially for circulatory,respiratory,digestive,genitourinary diseases,and endocrine,nutritional and metabolic to contain the rapid health expenditure growth in China. | Tiemin Zhai Quan Wan Peipei Chai Feng Guo Yan Li Rongrong Wang Chunmei Chen Tao Li Runguo Gao | 2020 | China Population and Development Studies2020，4，4： | 0 |
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