Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study

Abstract

Repeated endurance training – supervised by an expert – is one of the most effective rehabilitation methods for patients with chronic obstructive pulmonary disease (COPD) to improve physical function. Monitoring of vital signs in combination with an automatic intelligent training control and emergency detection facilitates supervised training without the physical presence of an expert as well as training optimisation through individualisation. The aim of this study is the development of a suitable analysis and control method for this purpose. Healthy volunteers and patients with COPD were equipped with body sensors during ergometer training to enable measuring their vital signs continuously. Depending on these values, the exercise load of the ergometer was controlled automatically using a Bayesian network. The network, trained with expert knowledge and training data, is embedded in our system by using Java application programming interface. Extensive tests in a laboratory setting have proved safe usage of our prototype. In a case study, evaluation during training sessions with patients with COPD took place. Due to the automatic control the patients' vital signs ranged inside the predefined optimal thresholds for at least 95% of the time. Furthermore, our results suggest an increase of the training efficiency compared with the conventional method (constant exercise load).

Keywords::

• COPD
• monitoring
• training control
• decision support system
• Bayesian network
• health care

Acknowledgements

We express our gratitude to the company Bayesia (http://www.bayesia.com) for their generous support regarding their modelling software ‘BayesiaLab’ and application programme interface ‘Bayesia Engines’, both of which have been made available for our research project. The work has been performed as part of the German Lower Saxony Research Network ‘Design of Environments for Ageing’ (www.altersgerechte-lebenswelten.de), which is supported by the Lower Saxony Ministry of Science and Culture through the ‘Niedersächsisches Vorab’ grant programme (VWZN 2420). Ethical approval for the clinical study was obtained from the ethics commission at Hannover Medical School.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.