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
- 1 September 2010
- journal article
- research article
- Published by Informa UK Limited in Informatics for Health and Social Care
- Vol. 35 (3-4), 144-156
- https://doi.org/10.3109/17538157.2010.528659
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
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