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Human Activity Sensing Corpus and Applications

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 t...

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Bibliographic Details
Other Authors: Kawaguchi, Nobuo (Editor), Nishio, Nobuhiko (Editor), Roggen, Daniel (Editor), Inoue, Sozo (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2019, 2019
Edition:1st ed. 2019
Series:Springer Series in Adaptive Environments
Subjects:
Microprogramming 
User Interfaces (computer Systems)
Data Mining
Application Software
Control Structures And Microprogramming
Data Mining And Knowledge Discovery
Computer And Information Systems Applications
User Interfaces And Human Computer Interaction
Human-computer Interaction
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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100 1 |a Kawaguchi, Nobuo  |e [editor] 
245 0 0 |a Human Activity Sensing  |h Elektronische Ressource  |b Corpus and Applications  |c edited by Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, Kristof Van Laerhoven 
250 |a 1st ed. 2019 
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300 |a XII, 250 p. 140 illus., 98 illus. in color  |b online resource 
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 
653 |a Microprogramming  
653 |a User interfaces (Computer systems) 
653 |a Data mining 
653 |a Application software 
653 |a Control Structures and Microprogramming 
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653 |a Computer and Information Systems Applications 
653 |a User Interfaces and Human Computer Interaction 
653 |a Human-computer interaction 
700 1 |a Nishio, Nobuhiko  |e [editor] 
700 1 |a Roggen, Daniel  |e [editor] 
700 1 |a Inoue, Sozo  |e [editor] 
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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 

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