The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Background: Infusion Pumps (IP) are medical devices that were developed in the 1960s and generate fluid flow at pressures higher than that of normal blood pressure. Various hospital sectors make use of them, and they have become indispensable in therapies requiring continuity and precision in the administration of medication and/or food. As they are classified Class III (high risk) equipment, their maintenance is crucial for proper performance of the device, as well as patient and operator safety. The principal consideration of the pump is the volume infused, and the device demands great attention to detail when being calibrated. A lack of necessary care with this equipment can lead to uncertainty in volume and precision during the administration of substances. Because of this, it is essential to evaluate its reliability, to prevent possible failures at time of execution. This control aims at the quality of the intended infusion result, becoming an indication of quality. Methods: This systematic review summarizes studies done over the last 10 years (2011 to December 2021) that address the reliability and accuracy of hospital infusion pumps, in order to identify planning of maintenance and/or other techniques used in management of the equipment. The Prisma method was applied and the databases utilized were Embase, MEDLINE/Pubmed, Web of Science, Scopus, IEEE Xplore, and Science Direct. In addition, similar reviews were studied in Prospero and the Cochrane Library. For data analysis, softwares such as Mendeley, Excel, RStudio, and VOSviewer were used, and Robvis helped in plotting risk of bias results for studies performed with Cochrane tools. Results: The six databases selected produced 824 studies. After applying eligibility criteria (inclusion and exclusion), removing duplicates, and applying filters 1 and 2, 15 studies were included in the present review. It was found that the most relevant sources came from the Institute of Electrical and Electronics Engineers (IEEE) and that the most relevant keywords revolved around the terms (“device failure”, “infusion pumps”, “adverse effects”, “complications”, etc.). These results made clear that there remains substantial room for improvement as it relates to the study of accuracy and reliability of infusion. Conclusions: We verified that the reliability and precision analysis of hospital infusion pumps need to be performed in a more detailed and consistent way. New developments, considering the model and IP specification, are intended, clearly explaining the adopted methodology.