國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Human activity sensingcorpus and app...

Kawaguchi, Nobuo.

Human activity sensingcorpus and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Human activity sensingedited by Nobuo Kawaguchi ... [et al.].
其他題名:	corpus and applications /
其他作者:	Kawaguchi, Nobuo.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xii, 250 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Sensor networks.
電子資源:	https://doi.org/10.1007/978-3-030-13001-5
ISBN:	9783030130015$q(electronic bk.)

Human activity sensingcorpus and applications /
Human activity sensingcorpus and applications /[electronic resource] :edited by Nobuo Kawaguchi ... [et al.]. - Cham :Springer International Publishing :2019. - xii, 250 p. :ill., digital ;24 cm. - Springer series in adaptive environments,2522-5529. - Springer series in adaptive environments..

Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording -- Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data -- Compensation Scheme for PDR using Component-wise Error Models -- Towards the Design and Evaluation of Robust Audio-Sensing Systems -- A Wi-Fi Positioning Method Considering Radio Attenuation of Human Body -- Drinking gesture recognition from poorly annotated data: a case study -- Understanding how Non-experts Collect and Annotate Activity Data -- MEASURed: Evaluating Sensor-based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture -- Benchmark performance for the Sussex-Huawei locomotion and transportation recognition challenge 2018 -- Effects of Activity Recognition Window Size and Time Stabilization in the SHL Recognition Challenge.

Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.

ISBN: 9783030130015$q(electronic bk.)

Standard No.: 10.1007/978-3-030-13001-5doiSubjects--Topical Terms:

214195
Sensor networks.

LC Class. No.: TK7872.D48 / H85 2019

Dewey Class. No.: 006.25

Human activity sensingcorpus and applications /
LDR:02566nmm a2200337 a 4500 001 566290
003 DE-He213
005 20190909153540.0
006 m d
007 cr nn 008maaau
008 200429s2019 gw s 0 eng d
020 $a 9783030130015$q(electronic bk.)
020 $a 9783030130008$q(paper)
024 7 $a 10.1007/978-3-030-13001-5 $2 doi
035 $a 978-3-030-13001-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7872.D48 $b H85 2019
072 7 $a UYZG $2 bicssc
072 7 $a COM070000 $2 bisacsh
072 7 $a UYZG $2 thema
082 0 4 $a 006.25 $2 23
090 $a TK7872.D48 $b H918 2019
245 0 0 $a Human activity sensing $h [electronic resource] : $b corpus and applications / $c edited by Nobuo Kawaguchi ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xii, 250 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in adaptive environments, $x 2522-5529
505 0 $a Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording -- Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data -- Compensation Scheme for PDR using Component-wise Error Models -- Towards the Design and Evaluation of Robust Audio-Sensing Systems -- A Wi-Fi Positioning Method Considering Radio Attenuation of Human Body -- Drinking gesture recognition from poorly annotated data: a case study -- Understanding how Non-experts Collect and Annotate Activity Data -- MEASURed: Evaluating Sensor-based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture -- Benchmark performance for the Sussex-Huawei locomotion and transportation recognition challenge 2018 -- Effects of Activity Recognition Window Size and Time Stabilization in the SHL Recognition Challenge.
520 $a Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.
650 0 $a Sensor networks. $3 214195
650 0 $a Internet of things. $3 670954
650 1 4 $a User Interfaces and Human Computer Interaction. $3 274517
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Information Systems Applications (incl. Internet) $3 530743
650 2 4 $a Control Structures and Microprogramming. $3 274663
700 1 $a Kawaguchi, Nobuo. $3 851907
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in adaptive environments. $3 821947
856 4 0 $u https://doi.org/10.1007/978-3-030-13001-5
950 $a Computer Science (Springer-11645)