Human Activity Sensing: Corpus and Applications by Nobuo Kawaguchi (English) Pap

Description: Human Activity Sensing by Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, Kristof Van Laerhoven 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. Table of Contents 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. Feature The first collection of best practices and methodologies for activity recognition, by some of the most experienced activity recognition experts A community-based approach to assemble study results and data set collections from common human activities Latest trends and challenge definitions in the area of context and activity recognition Details ISBN3030130037 Pages 250 Year 2020 ISBN-10 3030130037 ISBN-13 9783030130039 Publication Date 2020-09-24 Short Title Human Activity Sensing Language English Format Paperback Subtitle Corpus and Applications UK Release Date 2020-09-24 Edition 1st Place of Publication Cham Country of Publication Switzerland Author Kristof Van Laerhoven Publisher Springer Nature Switzerland AG Edition Description 1st ed. 2019 Series Springer Series in Adaptive Environments Imprint Springer Nature Switzerland AG Alternative 9783030130008 Edited by Kristof Van Laerhoven DEWEY 005.18 Audience Professional & Vocational Illustrations 98 Illustrations, color; 42 Illustrations, black and white; XII, 250 p. 140 illus., 98 illus. in color. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:129895921;

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