| Description |
1 online resource (251 pages) |
| Series |
Springer Series in Adaptive Environments Ser |
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Springer Series in Adaptive Environments
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| Contents |
Intro; Preface; Contents; Contributors; Part I Modalities and Applications; 1 Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording; 1.1 Introduction; 1.2 Related Work; 1.3 System Configuration for Experiment; 1.3.1 System Overview; 1.3.2 Wearable Sensor; 1.4 Experiment; 1.4.1 Data Collection in Experiment; 1.4.2 Feature Values; 1.4.3 The Number of Data Instances; 1.4.4 Classifier; 1.4.5 Evaluation Method; 1.5 Results and Discussion; 1.5.1 Classifier; 1.5.2 Number of Sensors; 1.5.3 Combination of Sensor Positions; 1.6 Conclusion; References |
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2 Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data2.1 Introduction; 2.2 Related Work; 2.3 Datasets; 2.4 Pre-processing and Analysis of Sensor Data Properties; 2.4.1 Detecting the Gyroscope Data; 2.4.2 Detecting the Accelerometer Data; 2.4.3 Detecting the Magnetometer Data; 2.4.4 Distinguishing Acceleration Versus Magnetometer Data; 2.5 Identification Ruleset; 2.6 A Discussion of Results and Limitations of Our Approach; 2.7 Conclusions; References; 3 Compensation Scheme for PDR Using Component-Wise Error Models; 3.1 Introduction; 3.2 Related Work |
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3.3 Compensation Scheme Proposal3.3.1 Moving Distance Error Model; 3.3.2 Error Model of Orientation Changing; 3.3.3 Drift Angle Error Model; 3.4 Evaluation; 3.4.1 Post-compensation; 3.4.2 Real-Time Compensation; 3.4.3 Discussion About the Moving Distance Error Model; 3.4.4 Discussion About Error Model of Orientation; 3.5 Conclusion; References; 4 Towards the Design and Evaluation of Robust Audio-Sensing Systems; 4.1 Introduction; 4.2 Methodology; 4.3 Results; 4.4 Discussion and Future Directions; 4.5 Conclusions; References |
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5 A Wi-Fi Positioning Method Considering Radio Attenuation of Human Body5.1 Introduction; 5.1.1 Background and Purpose; 5.1.2 Related Work; 5.1.3 Preliminary Experiment; 5.2 Approach and Evaluation; 5.2.1 Proposed Method; 5.2.2 Evaluation; 5.3 Conclusion; References; Part II Data Collection and Corpus Construction; 6 Drinking Gesture Recognition from Poorly Annotated Data: A Case Study; 6.1 Introduction; 6.2 Related Work; 6.3 Dataset; 6.4 User Annotation Analysis; 6.5 Gesture Classification; 6.5.1 Data Processing and Training Set Selection; 6.5.2 Template Matching Using WLCSS |
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6.5.3 WLCSS Optimization Using Evolutionary Algorithm6.5.4 Confidence Computation; 6.5.5 Evaluation; 6.6 Unsupervised Learning; 6.6.1 K-Means with WLCSS; 6.6.2 Evaluation; 6.7 Discussion; 6.8 Conclusion; References; 7 Understanding How Non-experts Collect and Annotate Activity Data; 7.1 Introduction; 7.2 Related Work; 7.2.1 Interactive Physical Devices; 7.2.2 Event Recognizers and Interaction; 7.2.3 Hidden Markov Models, ASR and Other Activity Models; 7.3 Building an Event Recognizer with VAT; 7.3.1 Define Event Pieces; 7.3.2 Attach Data Logger; 7.3.3 Record and Synchronize Video and Data |
| Summary |
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 |
| Notes |
7.3.4 Label Events in VAT |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Sensor networks.
|
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Internet of things.
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Internet of things
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Sensor networks
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| Form |
Electronic book
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| Author |
Kawaguchi, Nobuo
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Nishio, Nobuhiko
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Roggen, Daniel.
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Inoue, Sozo
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Pirttikangas, Susanna
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Laerhoven, Kristof van.
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| ISBN |
9783030130015 |
|
3030130010 |
|
