| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === Because of the serious aging population in these few year.How to efficient and automated record the elderly daily life and their movement situation already become a very important issue. In this paper we build a system on Raspberry Pi it can accurate reception of inertial information(accelerometer, angular velocity )from various parts of the body at the same time, we put the inertial sensors at arm, wrist, chest, waist, thigh and ankle,we use the inertial data collected by system and machine learning technique(Support Vector Machine)to do activity recognition such as Stand, Sit, Lay, Walk, Run, UpStairs, DownStairs, Drink. We segment data as windowSize 1 second(140 piece of data),then take the segmented raw data to do feature selection.Then we get the time do- main feature and feature domain feature ,We will discuss this matter in a little more detail as tje follow paper. After feature extraction we take the feature data to do feature selection (Relief Feature Selection)because we want to know which feature is relevant feature and which is irrelevant, it can smartly reduce calculating time. At experimental result part we compare the relation of sensor number and accuracy. Finally our system can accurately recognize user activity in real-time(recognize every second).This information can also be provided to professional medical staff for reference to develop better medical care.
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