東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Robust Signal Processing Techniques ...

Wu, Jian.

FindBook

Google Book

Amazon

博客來

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors./
作者:	Wu, Jian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	158 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13839889
ISBN:	9780438880153

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors.
Wu, Jian.

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 158 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--Texas A&M University, 2018.

This item must not be sold to any third party vendors.

Activity and gesture recognition using wearable motion sensors, also known as inertial measurement units (IMUs), provides important context for many ubiquitous sensing applications including healthcare monitoring, human computer interface and context-aware smart homes and offices. Such systems are gaining popularity due to their minimal cost and ability to provide sensing functionality at any time and place. However, several factors can affect the system performance such as sensor location and orientation displacement, activity and gesture inconsistency, movement speed variation and lack of tiny motion information. This research is focused on developing signal processing solutions to ensure the system robustness with respect to these factors. Firstly, for existing systems which have already been designed to work with certain sensor orientation/location, this research proposes opportunistic calibration algorithms leveraging camera information from the environment to ensure the system performs correctly despite location or orientation displacement of the sensors. The calibration algorithms do not require extra effort from the users and the calibration is done seamlessly when the users present in front of an environmental camera and perform arbitrary movements. Secondly, an orientation independent and speed independent approach is proposed and studied by exploring a novel orientation independent feature set and by intelligently selecting only the relevant and consistent portions of various activities and gestures. Thirdly, in order to address the challenge that the IMU is not able capture tiny motion which is important to some applications, a sensor fusion framework is proposed to fuse the complementary sensor modality in order to enhance the system performance and robustness. For example, American Sign Language has a large vocabulary of signs and a recognition system solely based on IMU sensors would not perform very well. In order to demonstrate the feasibility of sensor fusion techniques, a robust real-time American Sign Language recognition approach is developed using wrist worn IMU and surface electromyography (EMG) sensors.

ISBN: 9780438880153Subjects--Topical Terms:

649834
Electrical engineering.

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors.
LDR:03329nmm a2200337 4500 001 2207868
005 20190923114244.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438880153
035 $a (MiAaPQ)AAI13839889
035 $a (MiAaPQ)0803vireo:21449Wu
035 $a AAI13839889
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wu, Jian. $3 1279362
245 1 0 $a Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 158 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Jafari, Roozbeh.
502 $a Thesis (Ph.D.)--Texas A&M University, 2018.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Activity and gesture recognition using wearable motion sensors, also known as inertial measurement units (IMUs), provides important context for many ubiquitous sensing applications including healthcare monitoring, human computer interface and context-aware smart homes and offices. Such systems are gaining popularity due to their minimal cost and ability to provide sensing functionality at any time and place. However, several factors can affect the system performance such as sensor location and orientation displacement, activity and gesture inconsistency, movement speed variation and lack of tiny motion information. This research is focused on developing signal processing solutions to ensure the system robustness with respect to these factors. Firstly, for existing systems which have already been designed to work with certain sensor orientation/location, this research proposes opportunistic calibration algorithms leveraging camera information from the environment to ensure the system performs correctly despite location or orientation displacement of the sensors. The calibration algorithms do not require extra effort from the users and the calibration is done seamlessly when the users present in front of an environmental camera and perform arbitrary movements. Secondly, an orientation independent and speed independent approach is proposed and studied by exploring a novel orientation independent feature set and by intelligently selecting only the relevant and consistent portions of various activities and gestures. Thirdly, in order to address the challenge that the IMU is not able capture tiny motion which is important to some applications, a sensor fusion framework is proposed to fuse the complementary sensor modality in order to enhance the system performance and robustness. For example, American Sign Language has a large vocabulary of signs and a recognition system solely based on IMU sensors would not perform very well. In order to demonstrate the feasibility of sensor fusion techniques, a robust real-time American Sign Language recognition approach is developed using wrist worn IMU and surface electromyography (EMG) sensors.
590 $a School code: 0803.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
690 $a 0544
690 $a 0984
710 2 $a Texas A&M University. $b Computer Science and Engineering. $3 2094772
773 0 $t Dissertations Abstracts International $g 80-09B.
790 $a 0803
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13839889