IoT Technologies for Health Care: 8th EAI International Conference, HealthyIoT 2021, Virtual Event, November 24-26, 2021, Proceedings (Lecture Notes ... and Telecommunications Engineering)

Preface\nOrganization\nContents\nSecurity and Privacy - Software and Application Security\nNon-intrusive and Privacy Preserving Activity Recognition System for Infants Exploiting Smart Toys\n	1 Introduction\n	2 Related Work\n	3 The Dataset\n		3.1 Synthetic Dataset: In-Lab Data Collection\n		3.2 The AutoPlay Dataset\n		3.3 Annotation\n	4 Predictive System\n	5 Results\n		5.1 Results Based on the In-Lab Collected Dataset\n		5.2 Validation of the In-Lab Data Base Classification Models\n		5.3 Validation on Real Use-Case Scenarios\n	6 Conclusion\n	References\nHuman-Centered Computing - Ubiquitous and Mobile Computing\nCo-design the Acceptability of Wearables in the Healthcare Field\n	1 Introduction\n	2 Reflecting on the Contribution of Design to Enhance the Wearables\' Acceptability\n		2.1 An Empirical Case Study to Design an Acceptable Wearable PSS\n		2.2 Improve Wearable Acceptability Through UX and UI Design\n	3 Conclusion\n	References\nEvaluations on Pending Regulation on Ethical Review Measures for Biomedical Research Involving Human Subjects and Artificial Intelligence\n	1 Introduction\n	2 Methods\n	3 Results and Discussion\n		3.1 Ethics Review Committees in Three Tiers (Institutional, Provincial, and National) Should Be Established in China While Identifying the Clear, Precise Scope of Review Authorities for Each Level\n		3.2 The Guidance About Data Security in Ethics Review Should Be Clearly Descripted in the Draft\n		3.3 Transparency and Responsibility Should Be Supplemented into the Principles of Ethics Review\n	4 Conclusions\n	References\nIntegration of Wearable, Persuasive, and Multimedia Design Principles in Enhancing Depression Awareness: A Conceptual Model\n	1 Introduction\n		1.1 Related Works\n		1.2 Using Content Analysis in Model Development\n	2 The Conceptual Model: Development Process\n		2.1 Analyze: Common Components\n		2.2 Define: Elements for Each Sub-component\n		2.3 Ideate: Components Integration\n		2.4 Validate: Multidisciplinary Expert Reviews\n	3 The Conceptual Model: Result and Discussion\n	4 Conclusion\n	References\nInformation Systems – Information Retrieval\nA Comparative Study of Data Mining Techniques Applied to Renal-Cell Carcinomas\n	1 Introduction\n	2 Related Work\n	3 Materials and Methods\n		3.1 Data Preprocessing\n		3.2 Data Cleansing\n		3.3 Modeling and Validation\n	4 Results and Discussion\n	5 Conclusions and Future Work\n	References\nPredicting Diabetes Disease in the Female Adult Population, Using Data Mining\n	1 Introduction\n	2 Background and Related Work\n		2.1 Diabetes\n		2.2 Related Work\n	3 Methodology\n		3.1 Business Understanding\n		3.2 Data Understanding\n		3.3 Data Preparation\n		3.4 Modeling\n		3.5 Evaluation\n	4 Discussion\n	5 Conclusions and Future Work\n	References\nNot Just a Matter of Accuracy: A fNIRS Pilot Study into Discrepancy Between Sleep Data and Subjective Sleep Experience in Quantified-Self Sleep Tracking\n	1 Introduction\n	2 Related Work\n	3 Measuring Devices and Instruments\n		3.1 Wearable fNIRS System\n		3.2 Fitbit Sense and Sleep Rating\n	4 Data Collection Protocol\n	5 Data Analysis Protocol\n	6 Results\n	7 Discussions\n	References\nDetection of Diabetic Retinopathy Using CNN\n	1 Introduction\n	2 Background\n		2.1 Diabetic Retinopathy\n		2.2 Deep Learning Techniques\n		2.3 Image Processing\n	3 Related Work\n	4 Methodology\n		4.1 Data Collection\n		4.2 Preprocessing\n		4.3 Feature Extraction and Classification Using Deep Learning\n	5 Results and Discussion\n	6 Conclusion\n	References\nAutomatic Classification of Diabetic Retinopathy Through Segmentation Using CNN\n	1 Introduction\n	2 Literature Review\n	3 Methodology\n		3.1 Proposed Method\n		3.2 Deep Learning\n	4 Results\n	5 Conclusion and Future Work\n	References\nPulp Stone Detection Using Deep Learning Techniques\n	1 Introduction\n	2 Background\n		2.1 Convolutional Neural Networks\n		2.2 Deep Learning in Dentistry\n		2.3 The Importance of Pulp Stone Detection\n	3 Methodology\n		3.1 Dataset\n		3.2 The Pre-processing Stage\n		3.3 Feature Extraction\n		3.4 The Classification Stage\n	4 Experimentation\n	5 Results and Discussion\n	6 Conclusion\n	7 Future Work\n	References\nIdentification of Drug-Drug Interactions Using OCR\n	1 Introduction\n	2 Related Works\n		2.1 Drug Label Identification Through Image and Text Embedding Model\n		2.2 ST-Med-Box\n		2.3 LSTM-CRF\n		2.4 Syntax Convolutional Neural Network (SCNN)\n		2.5 Position-Aware Deep Multi-task Learning Approach for Extracting DDIs from Biomedical Texts\n		2.6 Recognition Medicine Name from Doctor’s Prescription\n		2.7 DDIs from Structured Product Labels\n		2.8 The Automated Drug Detection and Location Identification\n		2.9 Drug-Drug Interaction Extraction\n		2.10 A Label Propagation Method with Linear Neighborhood Information\n	3 Methodology\n		3.1 Dataset\n		3.2 The Proposed System\n	4 Results and Discussion\n	5 Conclusion\n	References\nApplied Computing - Physical Sciences and Engineering\nPatients Behaviour Monitoring Inside a Hospital Garden: Comparison Between RADAR and GPS Solutions\n	1 Introduction\n	2 The Considered Technologies\n		2.1 Radar Systems\n		2.2 Smartwatch with GPS\n	3 Measurement Setup and Radar Signal Processing\n		3.1 Measurement Area and Systems Configuration\n		3.2 Radar Signal Processing\n	4 Experimental Tests and Results\n	5 Conclusions\n	References\nIoT-Enabled Analysis of Subjective Sound Quality Perception Based on Out-of-Lab Physiological Measurements\n	1 Introduction\n	2 Materials and Methods\n		2.1 Measuring Device\n		2.2 Data Acquisition Protocol\n	3 Data Processing\n	4 Results and Discussions\n	5 Conclusion\n	References\nCS-Based Decomposition of Acoustic Stimuli-Driven GSR Peaks Sensed by an IoT-Enabled Wearable Device\n	1 Introduction\n	2 Background\n		2.1 GSR Signal\n		2.2 GSR Signal Analysis in Time Domain\n	3 Materials and Methods\n		3.1 The IoT-Enabled Sensing Device\n		3.2 Data Collection\n	4 Data Processing\n		4.1 Synthethic GSR Signals\n	5 Test Implementation and Results\n		5.1 Preliminary Analysis on Synthetic GSR Signals\n		5.2 Test on Real GSR Signals\n	6 Conclusion\n	References\nGAIToe: Gait Analysis Utilizing an IMU for Toe Walking Detection and Intervention\n	1 Introduction\n	2 Related Works\n	3 GAIToe System Specification\n		3.1 Hardware Implementation\n		3.2 Power Consumption\n		3.3 Android Application Development\n		3.4 Activity Recognition Algorithm\n	4 Experiments\n	5 Future Work\n	6 Conclusion\n	References\nApplied Computing – Life and Medical Sciences\nPrediction of Conversion to Alzheimer’s Disease Using 3D-DWT and PCA\n	1 Introduction\n	2 Prior Study\n	3 Proposed Method\n		3.1 3D-Discrete Wavelet Transform\n		3.2 Principal Component Analysis\n		3.3 Support Vector Machine\n	4 Implementation and Evaluation\n		4.1 Dataset Acquisition\n		4.2 K-Fold Cross-Validation\n		4.3 Evaluation Metric\n	5 Results and Discussion\n	6 Conclusion\n	References\nDIY Wrist-Worn Device for Physiological Monitoring: Metrological Evaluation at Different Band Tightening Levels\n	1 Introduction\n	2 Materials and Methods\n		2.1 Wrist-Worn Acquisition Device\n		2.2 Data Acquisition Protocol\n		2.3 Data Processing\n	3 Results and Discussions\n		3.1 BP and HR Data Measured with the Oscillometric Method\n		3.2 Results from Wrist-Worn PPG Sensor and Load Cell\n	4 Conclusion\n	References\nAuthor Index