語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
TAASR : = Temporally-Aware Affective State Recognition With Attention-Augmented CNNs.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
TAASR :/
其他題名:
Temporally-Aware Affective State Recognition With Attention-Augmented CNNs.
作者:
Abramow, Daniel.
面頁冊數:
1 online resource (48 pages)
附註:
Source: Masters Abstracts International, Volume: 84-12.
Contained By:
Masters Abstracts International84-12.
標題:
Information science. -
電子資源:
click for full text (PQDT)
ISBN:
9798379687311
TAASR : = Temporally-Aware Affective State Recognition With Attention-Augmented CNNs.
Abramow, Daniel.
TAASR :
Temporally-Aware Affective State Recognition With Attention-Augmented CNNs. - 1 online resource (48 pages)
Source: Masters Abstracts International, Volume: 84-12.
Thesis (M.S.)--University of Georgia, 2023.
Includes bibliographical references
Affective state recognition (ASR) involves using the body's physiological signals to extract useful information about ones mental state. ASR systems are often implemented in highly controlled environments with cumbersome chest sensors and intrusive facial expression monitoring setups, making it difficult to translate their performance to real environments. Recently, the widespread adoption of wrist-worn wearables has highlighted a need for further research into practical ASR with commercially available devices. In this paper, we propose TAASR, an InceptionTime based end-to-end learning architecture augmented with channel attention and global feature fusion for three-class ASR (baseline vs. stressed vs. amused), and TAASR-MT, a multi-task version of TAASR that uses mental health self-assessments to improve basic ASR performance. For practicality, we train these architectures primarily with wrist-based signals and report a best classification accuracy and F1-score of 81.16% and 70.02, demonstrating noticeable improvements upon InceptionTime and prior works that employ simpler classification approaches.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379687311Subjects--Topical Terms:
561178
Information science.
Subjects--Index Terms:
Affective state recognitionIndex Terms--Genre/Form:
554714
Electronic books.
TAASR : = Temporally-Aware Affective State Recognition With Attention-Augmented CNNs.
LDR
:02524ntm a22004097 4500
001
1149293
005
20241015112503.5
006
m o d
007
cr bn ---uuuuu
008
250605s2023 xx obm 000 0 eng d
020
$a
9798379687311
035
$a
(MiAaPQ)AAI30311305
035
$a
AAI30311305
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Abramow, Daniel.
$3
1475465
245
1 0
$a
TAASR :
$b
Temporally-Aware Affective State Recognition With Attention-Augmented CNNs.
264
0
$c
2023
300
$a
1 online resource (48 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Masters Abstracts International, Volume: 84-12.
500
$a
Advisor: Liu, Tianming.
502
$a
Thesis (M.S.)--University of Georgia, 2023.
504
$a
Includes bibliographical references
520
$a
Affective state recognition (ASR) involves using the body's physiological signals to extract useful information about ones mental state. ASR systems are often implemented in highly controlled environments with cumbersome chest sensors and intrusive facial expression monitoring setups, making it difficult to translate their performance to real environments. Recently, the widespread adoption of wrist-worn wearables has highlighted a need for further research into practical ASR with commercially available devices. In this paper, we propose TAASR, an InceptionTime based end-to-end learning architecture augmented with channel attention and global feature fusion for three-class ASR (baseline vs. stressed vs. amused), and TAASR-MT, a multi-task version of TAASR that uses mental health self-assessments to improve basic ASR performance. For practicality, we train these architectures primarily with wrist-based signals and report a best classification accuracy and F1-score of 81.16% and 70.02, demonstrating noticeable improvements upon InceptionTime and prior works that employ simpler classification approaches.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2024
538
$a
Mode of access: World Wide Web
650
4
$a
Information science.
$3
561178
650
4
$a
Mental health.
$3
564038
653
$a
Affective state recognition
653
$a
Convolutional neural networks
653
$a
Deep learning
653
$a
Time series classification
653
$a
Wearables
653
$a
InceptionTime
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0800
690
$a
0723
690
$a
0347
710
2
$a
University of Georgia.
$b
Artificial Intelligence - MS.
$3
1466144
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
773
0
$t
Masters Abstracts International
$g
84-12.
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30311305
$z
click for full text (PQDT)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入