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Keyphrases
Fall Risk
100%
Machine Learning Approach
100%
Heart Rate Variability
100%
Daily Activities
100%
Risk Monitoring
100%
Stress Detection
100%
Detection Risk
100%
Older Adults
50%
HRV Features
50%
Machine Learning Models
25%
Machine Learning
25%
Stress Level
25%
State-based
25%
Machine Learning Techniques
25%
Physiological Condition
25%
Over 40
25%
K-means Clustering
25%
Physiological Response
25%
Cluster Classification
25%
Root Mean Square of Successive Differences (RMSSD)
25%
Gradient Boosting
25%
Health Anxiety
25%
Stress Loss
25%
Potential Biomarkers
25%
Unsupervised Clustering
25%
Supervised Classification
25%
Berg Balance Scale
25%
Balanced Loss Function
25%
Fall Prevention
25%
Heart Rate Monitoring
25%
Frequency Band Power
25%
Functional Balance
25%
Cluster Label
25%
Everyday Settings
25%
Real-time Monitoring System
25%
Polar H10
25%
40 Years Old
25%
Stress Indicators
25%
Mental Stress
25%
Impaired Balance
25%
Fall Risk Classification
25%
Prevention Efforts
25%
Healthy Participants
25%
Social Sciences
Machine Learning
100%
Learning System
100%
Older Adults
66%
Learning Method
33%
Mental Stress
33%
Machine Learning Model
33%
Biological Marker
33%
Nursing and Health Professions
Heart Rate Variability
100%
Berg Balance Scale
25%
Biological Marker
25%
Patient Monitor
25%