Earticle

초록

영어

Falls in elderly is a very serious health problem. For these years, the wearable devices based on tri-axial accelerator has been proven to be an effective way to fall detection. Most current methods for fall detection are based on threshold and machine learning. A approach based on statistical features was proposed to distinguish falls and normal activities of daily living(ADL) in this paper. What is worth mentioning is that Kernel Principal component analysis(KPCA) is firstly used to extract the statistical features from the original 3D data of acceleration, we don’t need to design features specially. The support vector machine (SVM) algorithm and K-Nearest Neighbor(KNN) algorithm are combined for prediction. Finally the validation of the prediction is done to improve the accuracy. Algorithm is mainly conducted on the public databases(UCI). And our method obtained the result is proved to be better compared with the other literature based on this public databases.

목차

Abstract
 1. Introduction
 2. Method
  2.1. Data Acquisition
  2.2. Preliminary Prediction
  2.3. Statistical Feature Extraction
  2.4. Classifier
  2.5. Validation
 3. Experiment and Result
 4. Conclusion
 References

키워드

저자

• Hui Wang [ school of electronic and information engineering, soochow university, china ]
• Xiaohe Chen [ suzhou institute of biomedical engineering and technology chinese academy of sciences ]
• Xingyu Wu [ school of electronic and information engineering, soochow university, china ]
• XinjianChen [ school of electronic and information engineering, soochow university, china ]
• Lirong Wang [ school of electronic and information engineering, soochow university, china ] corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605