Full Description
Activity, Behavior, and Healthcare Computing relates to the fields of vision and sensor-based human action or activity and behavior analysis and recognition. As well as a series of methodologies, the book includes original methods, exploration of new applications, excellent survey papers, presentations on relevant datasets, challenging applications, ideas and future scopes with guidelines. Featuring contributions from top experts and top research groups globally related to this domain, the book covers action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, healthcare, dementia, nursing, Parkinson's disease, and related areas. It addresses various challenges and aspects of human activity recognition - both in sensor-based and vision-based domains. This is a unique edited book covering both domains in the field of activity and behavior.
Contents
Foreword
Preface
Acknowledgments
About the Editors
Part 1: Activity and Behavior
Chapter 1: PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D Simulated Pressure Maps
Chapter 2: SIMUAug: Variability-aware Data Augmentation for Wearable IMU using Physics Simulation
Chapter 3: Estimation of Muscle Activation during Complex Movement using Unsupervised Motion Primitives Decomposition of Limb Kinematics
Chapter 4: Pitcher Identification Method using an Accelerometer and Gyroscope Embedded in a Baseball
Chapter 5: Design and Implementation of a Long-Casting Support System for Lure Fishing using an Accelerometer
Chapter 6: Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR
Chapter 7: Estimation Method of Doneness for Boiled Eggs and Diced Steaks using Active Acoustic Sensing
Part 2: Healthcare
Chapter 8: Older Adults Daily Mobility and Its Connection to DEMMI
Chapter 9: Subjective Stress and Heart Rate Variability Patterns: A Study on Harassment Detection
Chapter 10: Analysis of Physiological Variances in Thermal Comfort among Individuals
Chapter 11: Personal Thermal Assessment using Feature Reduction and Machine Learning Techniques
Chapter 12: Analysis of Personal Thermal State using Machine Learning Algorithms to Prevent Heatstroke
Chapter 13: Ensemble Learning Models-Based Prediction of Personal Thermal Assessment Aimed at Heatstroke Prevention
Chapter 14: Predicting Heatstroke Risk and Preventing Health Complications: An Innovative Approach Using Machine Learning and Physiological Data
Chapter 15: Predictive Modeling for Heatstroke Risk Forecasting Integrating Physiological Features Using Ensemble Classifier
Chapter 16: Clustering-Based Feature Selection and Stacked Generalization Method to Offset Imbalanced Data for Thermal Stress Assessment
Chapter 17: Enhancing Personalized Heatstroke Prevention: Forecasting Thermal Comfort Sensations through Data-Driven Models
Chapter 18: Advancing Heatstroke Prevention: Integrating Physiological Data for Enhanced Thermal Comfort Forecasting
Chapter 19: Intrapatient Forecasting of Parkinson's Wearing-Off by Analyzing Data from Wrist-Worn Fitness Tracker and Smartphone
Chapter 20: Foreseeing Wearing-Off State in Parkinson's Disease Patients: A Multimodal Approach with the Usage of Machine Learning and Wearables
Chapter 21: Wearable Technology-Enabled Prediction of Wearing-Off Phenomenon in Parkinson's Disease: A Personalized Approach Using LSTM-Based Time-Series Analysis
Chapter 22: Forecasting Parkinson's Patient's Wearing-Off Periods by Employing Stacked Super Learner
Chapter 23: Forecasting Wearing-Off in Parkinson's Disease: An Ensemble Learning Approach Using Wearable Data
Chapter 24: Forecasting the Wearing-Off Phenomenon in Parkinson's Disease: Summarized Approaches and Insights
