Dissertation/Thesis Abstract

Human activities and status recognition using depth data
by Fu, Jiajun, M.S.E.C.E., Purdue University, 2015, 98; 1602904
Abstract (Summary)

Human activities and status recognition is taking a more and more significant role in the healthcare area. Recognition systems can be based on many methods, such as video, motion sensor, accelerometer, etc. Depth data of Kinect sensor is a novel data type with 25 joints of information, which is popular in motion sensing games. The 25 joints include the head, neck, shoulders, elbows, wrists, hands, spine, hips, knees, ankles, and feet. This paper presents a recognition system based on these depth data. Depending on the characteristic from 5 kinds of statuses, a local coordinate frame was established and partial Euler angles were extracted as features. These Euler Angles can accurately describe the joints distribution in 3 dimensional space. With this kind of feature, a neural network model was built using 50000 sets of data to classify the daily activities into these 5 statuses. Experimental results of 10 volunteers’ action sequences in the same environment showed the accuracy was up to 97.96 percent. The recognition in dark environment had more than 90 percent accuracy.

Indexing (document details)
Advisor: Chen, Bin
Commitee: Chen, Bin, Gopalan, Kaliappan, Kozel, David
School: Purdue University
Department: Electrical and Computer Engineering
School Location: United States -- Indiana
Source: MAI 55/02M(E), Masters Abstracts International
Subjects: Electrical engineering
Keywords: Depth data, Euler angles, Human activities recognition, Kinect, Neural network
Publication Number: 1602904
ISBN: 978-1-339-19062-4
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