Hybridization of standard and fuzzified neural networks from MEMS-based human condition monitoring data for estimating heart rate

Common underlying risk factors for chronic diseases include physical inactivity accompanying modern sedentary lifestyle, unhealthy eating habits, and tobacco use. Interestingly, these prominent risk factors fall under what is referred to as modifiable behavioral risk factors, emphasizing the importance of self-care to improve wellness and prevent the onset of many debilitating conditions. In that regard, advances in wearable devices capable of pervasively collecting data about oneself coupled with the analytic capability provided by artificial intelligence and machine learning can potentially upend how we care for ourselves. This chapter aims to assess the current state and future implications of using big data and artificial intelligence in wearables for health and wellbeing. The results of the systematic review capture key developments and emphasize the potential for leveraging AI and wearables for inducing a paradigm shift in improving health and wellbeing.

show abstract

Hybridization of standard and fuzzified neural networks from MEMS-based human condition monitoring data for estimating heart rate

Cited by 3 publications

References 11 publications

Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression

Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression

A Deep Belief Network Model for Automatic Atrial Fibrillation Detection

Wearables, Artificial intelligence, and the Future of Healthcare

Contact Info

Product

Resources

About