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Human activity sensing = corpus and ...

Kawaguchi, Nobuo.

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Human activity sensing = corpus and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human activity sensing/ edited by Nobuo Kawaguchi ... [et al.].
Reminder of title:	corpus and applications /
other author:	Kawaguchi, Nobuo.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xii, 250 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Sensor networks. -
Online resource:	https://doi.org/10.1007/978-3-030-13001-5
ISBN:	9783030130015

Human activity sensing = corpus 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

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

581965
Sensor networks.

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

Dewey Class. No.: 006.25

Human activity sensing = corpus and applications /
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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 581965
650 0 $a Internet of things. $3 2057703
650 1 4 $a User Interfaces and Human Computer Interaction. $3 892554
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Information Systems Applications (incl. Internet) $3 1565452
650 2 4 $a Control Structures and Microprogramming. $3 895886
700 1 $a Kawaguchi, Nobuo. $3 3413993
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer series in adaptive environments. $3 3413994
856 4 0 $u https://doi.org/10.1007/978-3-030-13001-5
950 $a Computer Science (Springer-11645)