國立虎尾科技大學 |

Microsoft Kinect Based Real-Time Segmentation and Recognition for Human Activity Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Microsoft Kinect Based Real-Time Segmentation and Recognition for Human Activity Learning./
作者:	Kadiwal, Sanobar.
面頁冊數:	1 online resource (65 pages)
附註:	Source: Masters Abstracts International, Volume: 57-04.
Contained By:	Masters Abstracts International57-04(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355651928

Microsoft Kinect Based Real-Time Segmentation and Recognition for Human Activity Learning.
Kadiwal, Sanobar.

Microsoft Kinect Based Real-Time Segmentation and Recognition for Human Activity Learning. - 1 online resource (65 pages)

Source: Masters Abstracts International, Volume: 57-04.

Thesis (M.S.)--University of Houston-Clear Lake, 2017.

Includes bibliographical references

Lower body pain and injury have become common in this technical world, especially in elderly people. It is quite difficult to recover from these injuries leading to problems in performing daily routine activities like walking, running, sitting etc. Although there are many activity recognition models present today, there has been relatively little multiple activities recognition study of lower limbs. Most of the previous researchers focused on single activity recognition using various machine learning algorithms. Researchers have evolved with the learning of gait using different methods and techniques for upper and lower body using the sensors and different camera systems. This research has two main sections, one is for segmenting the motion and another is recognizing those movements. In this research, multiple activities were performed by the patients in a random manner without stopping and these activities were recognized in different groups stating the performed activity if the part of the multiple activities is walking or running or leg raising activity. The first goal of this dissertation is to plot the human gaits as a skeleton using MATLAB with a camera sensor second goal is to segment those derived gaits using the on-line aligned cluster analysis and dynamic time alignment kernel method and the last goal is to recognize the segmented gaits using the support vector machine algorithm. This is done by tracking and learning the person's lower limb data points and finding the exact action performed by a dynamic time alignment Kernel method for segmentation and comparison of different algorithms like Support Vector Machine, K-Nearest neighbors for recognition. The experimental results collected in this research show that the Support Vector Machine performs higher recognition accuracy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355651928Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Microsoft Kinect Based Real-Time Segmentation and Recognition for Human Activity Learning.
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504 $a Includes bibliographical references
520 $a Lower body pain and injury have become common in this technical world, especially in elderly people. It is quite difficult to recover from these injuries leading to problems in performing daily routine activities like walking, running, sitting etc. Although there are many activity recognition models present today, there has been relatively little multiple activities recognition study of lower limbs. Most of the previous researchers focused on single activity recognition using various machine learning algorithms. Researchers have evolved with the learning of gait using different methods and techniques for upper and lower body using the sensors and different camera systems. This research has two main sections, one is for segmenting the motion and another is recognizing those movements. In this research, multiple activities were performed by the patients in a random manner without stopping and these activities were recognized in different groups stating the performed activity if the part of the multiple activities is walking or running or leg raising activity. The first goal of this dissertation is to plot the human gaits as a skeleton using MATLAB with a camera sensor second goal is to segment those derived gaits using the on-line aligned cluster analysis and dynamic time alignment kernel method and the last goal is to recognize the segmented gaits using the support vector machine algorithm. This is done by tracking and learning the person's lower limb data points and finding the exact action performed by a dynamic time alignment Kernel method for segmentation and comparison of different algorithms like Support Vector Machine, K-Nearest neighbors for recognition. The experimental results collected in this research show that the Support Vector Machine performs higher recognition accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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