國立虎尾科技大學 |

Signal Processing and Machine Learning Methods for Human Motion Recognition.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Signal Processing and Machine Learning Methods for Human Motion Recognition./
作者:	Jokanovic, Branka.
面頁冊數:	1 online resource (119 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355859522

Signal Processing and Machine Learning Methods for Human Motion Recognition.
Jokanovic, Branka.

Signal Processing and Machine Learning Methods for Human Motion Recognition. - 1 online resource (119 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Villanova University, 2017.

Includes bibliographical references

Human motion recognition is one of the key issues in health care monitoring. Short-term health monitoring is typical for rehabilitation services, while long-term monitoring is common in residential care facilities for the elderly. Although this work gives particular attention to fall detection because of its significance within public health problems, the methods in this thesis can be easily applied to other types of motion.

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

Mode of access: World Wide Web

ISBN: 9780355859522Subjects--Topical Terms:

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

554714
Electronic books.

Signal Processing and Machine Learning Methods for Human Motion Recognition.
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245 1 0 $a Signal Processing and Machine Learning Methods for Human Motion Recognition.
264 0 $c 2017
300 $a 1 online resource (119 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Moeness Amin.
502 $a Thesis (Ph.D.)--Villanova University, 2017.
504 $a Includes bibliographical references
520 $a Human motion recognition is one of the key issues in health care monitoring. Short-term health monitoring is typical for rehabilitation services, while long-term monitoring is common in residential care facilities for the elderly. Although this work gives particular attention to fall detection because of its significance within public health problems, the methods in this thesis can be easily applied to other types of motion.
520 $a In this work, the signals corresponding to human motions are captured by a radar system. Radar is a remote sensor that has been proven successful in human motion recognition. Radar signal returns, corresponding to human gross-motor activities, are nonstationary in nature. For these signals, the time-frequency domain is typically used, attributing to its ability to reveal velocities, accelerations, and higher-order Doppler terms of limbs and various human body parts in motion. The range is another important information which can be obtained from multi-frequency or wideband radar returns. It may be used to reveal human location vs. time, and with fine range resolution, it can tag the body main scatterers to their respective Doppler signatures. In general, velocity and range are considered fundamental to human motion classification.
520 $a This thesis consists of two parts. In the first part, new forms of time-frequency signal representations based on interpolation and compressive sensing are introduced. These representations provide proper signatures of the incomplete nonstationary data. Incomplete data, i.e., missing observations or random sampling can be due to various reasons, including the discard of noisy measurements, hardware simplification and logistical restrictions on data collection/storage. Once the proper time-frequency signature is obtained, the next step is the classification.
520 $a The issue of classification is investigated in the second part of the thesis. New deep learning schemes for classifying radar returns are described. Deep learning has emerged as the key part in the field of artificial intelligence due to its powerful brain-mimicking neural network structures. These complex structures allow an automated way of learning and capturing the intricate properties of the human motion signatures in different domains. Experimental results demonstrate that the proposed methods provide superior results compared to traditional techniques. Finally, the presented work could be used to pave the way for a successful real-time implementation of a radar-based human motion detector.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Villanova University. $b College of Engineering. $3 1180374
773 0 $t Dissertation Abstracts International $g 79-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681582 $z click for full text (PQDT)