Human Activity Sensing [electronic resource] : Corpus and Applications / edited by Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, Kristof Van Laerhoven.

1st ed. 2019.
Cham : Springer International Publishing : Imprint: Springer, 2019.
Computer Science (Springer-11645)
Springer Series in Adaptive Environments, 2522-5529
Springer Series in Adaptive Environments, 2522-5529
1 online resource (XII, 250 pages) : 140 illustrations, 98 illustrations in color.
User interfaces (Computer systems)
Data mining.
Application software.
Local subjects:
User Interfaces and Human Computer Interaction.
Data Mining and Knowledge Discovery.
Information Systems Applications (incl. Internet).
Control Structures and Microprogramming.
System Details:
text file PDF
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.
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.
Kawaguchi, Nobuo. editor., Editor,
Nishio, Nobuhiko. editor., Editor,
Roggen, Daniel, editor., Editor,
Inoue, Sozo, editor., Editor,
Pirttikangas, Susanna. editor., Editor,
Laerhoven, Kristof van, editor., Editor,
SpringerLink (Online service)
Contained In:
Springer eBooks
Other format:
Printed edition:
Printed edition:
Printed edition:
9783030130008 (Printed edition)
9783030130022 (Printed edition)
9783030130039 (Printed edition)
Publisher Number:
10.1007/978-3-030-13001-5 doi
Access Restriction:
Restricted for use by site license.
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