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Challenges and Trends in Multimodal Fall Detection for Healthcare

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Table of Contents

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    Book Overview
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    Chapter 1 Open Source Implementation for Fall Classification and Fall Detection Systems
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    Chapter 2 Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study
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    Chapter 3 Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning
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    Chapter 4 Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognition
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    Chapter 6 Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition
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    Chapter 7 Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches
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    Chapter 8 An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models
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    Chapter 10 A Novel Approach for Human Fall Detection and Fall Risk Assessment
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