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%
Healthy Participants
25%
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
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%
Computer Science
Machine Learning Approach
100%
Daily Activity
100%
Risk Monitoring
100%
Machine Learning
50%
Learning System
50%
Time Monitoring
50%
Monitoring System
50%
Machine Learning Technique
50%
Collected Data
50%
K-Means Clustering
50%
Supervised Classification
50%
Gradient Boosting
50%
Unsupervised Clustering
50%
Prevention Effort
50%
Physiological Response
50%
Machine Learning Model
50%
Engineering
Learning System
100%
Learning Approach
100%
Heart Rate Variability
100%
Stress Level
25%
Monitoring System
25%
Collected Data
25%
Root Mean Square
25%
Autonomics
25%
Machine Learning Technique
25%
Medicine and Dentistry
Heart Rate Variability
100%
Physiological Response
25%
Biological Marker
25%
Physiological State
25%
Fall Prevention
25%
Berg Balance Scale
25%
Mental Stress
25%
Nursing and Health Professions
Heart Rate Variability
100%
Berg Balance Scale
25%
Biological Marker
25%
Patient Monitor
25%
Biochemistry, Genetics and Molecular Biology
Heart Rate Variability
100%
Mental Stress
25%