CAJ | 학술논문

为提高基于加速度传感器的人体行为识别率，提出2种新的加速度数据特征。一种通过计算加速度矢量与重力方向夹角的小波能量来揭示加速度方向变化的本质，从时频分析的角度区分不同行为；另一种提取加速度数据重排后的关键点连线斜率，突出数据的差异和分布特点。将上述2种特征与常用的6种特征相结合，训练基于支持向量机的多类分类器，对7种日常行为进行识别。检测结果表明，独立检测法和留一交叉检测法对7种行为的平均识别率分别可达92.70%和95.08%。
위제고기우가속도전감기적인체행위식별솔，제출2충신적가속도수거특정。일충통과계산가속도시량여중력방향협각적소파능량래게시가속도방향변화적본질，종시빈분석적각도구분불동행위；령일충제취가속도수거중배후적관건점련선사솔，돌출수거적차이화분포특점。장상술2충특정여상용적6충특정상결합，훈련기우지지향량궤적다류분류기，대7충일상행위진행식별。검측결과표명，독립검측법화류일교차검측법대7충행위적평균식별솔분별가체92.70%화95.08%。
Two novel features for acceleration data are applied to improve recognition accuracy of human activities. One feature uncovers the essential of acceleration direction by calculating the Wavelet Energy(WE) of angle between acceleration vector and gravity direction, and distinguishes different activities from time-frequency analysis. The other feature extracts from the slope of key points connection after acceleration data are rearranged, which highlights the difference and distribution of acceleration data. The two novel features can be combined with the six traditional widely used features to constitute feature sets, which allows to train the multi-class classifier based on Support Vector Machine(SVM), and to identify seven Activities of Daily Living(ADL). Two test results show that the average recognition accuracy of independent test method and leave one out cross test method can reach 92.70%and 95.08%respectively.