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Human Activity Sensing: Corpus and Applications 2019 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 250 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 98 Illustrations, color; 42 Illustrations, black and white; XII, 250 p. 140 illus., 98 illus. in color., 1 Paperback / softback
  • Sari: Springer Series in Adaptive Environments
  • Ilmumisaeg: 24-Sep-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030130037
  • ISBN-13: 9783030130039
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  • Formaat: Paperback / softback, 250 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 98 Illustrations, color; 42 Illustrations, black and white; XII, 250 p. 140 illus., 98 illus. in color., 1 Paperback / softback
  • Sari: Springer Series in Adaptive Environments
  • Ilmumisaeg: 24-Sep-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030130037
  • ISBN-13: 9783030130039

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.