Abstract

Embedded software is a piece of software that is built in a system or hardware component to achieve an objective. Cyber-Physical Systems (CPS) are integrations of computation with physical processes which are monitored and controlled by the embedded systems. CPS has positively affected a number of application areas which include communication, consumer energy, infrastructure, healthcare, manufacturing, military, robotics and transportation. This research paper focuses on CPS with particular application interest on error prediction in smart medical devices SMDs. SMDs are used for healthcare services by medical personnels in such a way that they have to interact with the patients in one form or the other. A number of research works has focused on errors arising from the use of SMDs. Errors arise from readings of the SMDs as a result of usability challenges from embedded software leading to malfunctioning of the devices. This research paper is aimed at providing a model for error verification and prediction in the use of SMDs using Euler’s method of representation with the aims of establishing safety and reliability of the use of SMDs with possible reduction in risk of accidents. In order to achieve the stated objective, a mathematical model using Euler’s method was adopted for its fast convergence rate and simplicity for error prediction. Input data was provided through a critical incident analysis of online database which provide readings from medical experts. These readings were compared to the standard world benchmarks. The difference between the readings and the standard benchmark validates the existence of errors. Due to the complexity of the model, an algorithm was developed to obtain an optimal solution of P1-P5 within an acceptable threshold runtime. An implementation was carried out using Java programming language because of its robustness and cross platform advantages which provides an efficient error estimate and result analysis. The analysis showed that the optimum performances of the SMDs were hindered by errors. The results generated from the use of thermometer for the diagnosis of Malaria predicted 98.1% accuracy in measurement, Upper respiratory tract infection predicted 99.3% accuracy in measurement, Tonsilitis predicted 99.6% accuracy in measurement, Severe head injury predicted 99.9% accuracy in measurement and Septicaemia predicted 99.9% accuracy. These results show that Malaria generated the highest error in the use of thermometer. In conclusion, CPS generally is an active area of research with a number of application domains in particular health care systems. This particular work has provided a model for error verification in the use of SMDs