An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data

Computer Science

• Convolutional Neural Network 100%
• Accelerometer Data 100%
• Supervised Method 100%
• Hybrid Approach 100%
• Representation Learning 66%
• Machine Learning Technique 33%
• Support Vector Machine 33%
• Unsupervised Method 33%
• Mental Distress 33%
• Collected Data 33%

Medicine and Dentistry

• Accelerometer 100%
• Volunteer 33%
• Fear of Falling 33%
• Wearable Sensor 33%
• Detection Technique 33%