A review of applications of artificial intelligence in cardiorespiratory rehabilitation

Abstract

Implementations of artificial intelligence and machine learning are becoming commonplace in multiple application domains. This is in part due to advancements in computing hardware that have helped outsource the computation of resource-intensive mathematics related to artificial intelligence and machine learning to the chips of multi-core and parallel computing architectures. Partly it is due to the widespread appeal of machine learning as a suite of handy tools to fix practical issues. Many fields have become beneficiaries of artificial intelligence and machine learning and cardiorespiratory rehabilitation is no exception.The aim of this paper is to review the current state of the art of the applications of artificial intelligence and machine learning in cardiorespiratory rehabilitation. We have taken a multidimensional view to addressing the needs and utility of artificial intelligence and machine learning in cardiorespiratory rehabilitation. We start with the most primitive applications of machine learning reported in existing literature in making medical devices for analyzing heartbeats and respiratory functions. We then discuss more recent approaches including deep learning to analyze performance or suggest alternative choices for food or exercise. Applications and utility of most recent feats such as explainable artificial intelligence are also discussed and conclusions around the current state of the art and possible future directions are proposed.

Keywords

• Cardiorespiratory rehabilitation
• Artificial intelligence
• Machine learning
• Deep ensembles
• Snapshot ensembles
• Uncertainty modeling