Abstract
Activity recognition is an important subject with many applications in health care, emergency care, and assisted living. Nowadays, activity information can be acquired using small accelerometers connected to the body, including the ones available in smartphones. In this study, we assessed the influence of autoregressive model parameters or features on activity detection or classification. Our results indicate that, compared to relatively simple features such as first order statistics, autoregressive model features have rather low impact in determining or improving performance of automatic activity detection using machine intelligence.
Keywords
Autoregressive model
Computer science
Accelerometer
Artificial intelligence
Machine learning
Nonlinear autoregressive exogenous model
Data mining
Statistical model
Pattern recognition (psychology)
Statistics
Artificial neural network
Mathematics
Subject Areas
Context-Aware Activity Recognition Systems
·Computer Vision and Pattern Recognition ·Physical Sciences
Non-Invasive Vital Sign Monitoring
·Biomedical Engineering ·Physical Sciences
Human Mobility and Location-Based Analysis
·Transportation ·Social Sciences
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