Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.
Daily tasks of nurses include manual handling to assist patients. Repetitive manual handling leads to high risk of injuries due to the loads on nurses’ bodies. Nurses, in hospitals and care homes, can benefit from the advances in exoskeleton technology assisting their manual handling tasks. There are already exoskeletons both in the market and in the research area made to assist physical workers to handle heavy loads. However, those exoskeletons are mostly designed for men, as most physical workers are men, whereas most nurses are women. In the case of nurses, they handle patients, a more delicate task than handling objects, and any such device used by nurses should easily be disinfected. In this study, the needs of nurses are examined, and a review of the state-of-the-art exoskeletons is conducted from the perspective of to what extent the existing technologies address the needs of nurses. Possible solutions and technologies and particularly the needs that have not been addressed by the existing technologies are discussed.
BackgroundHypertrophic cardiomyopathy (HC) is characterized by left ventricular (LV) hypertrophy and associated with papillary muscle (PM) abnormalities. The aim of this study was to evaluate the utility of three-dimensional echocardiography (3DE) for the geometric assessment of LV hypertrophy and PM morphology.MethodsThe study included 24 patients with an established diagnosis of HC and 31 healthy controls. 3DE was performed using an iE33 or EPIQ 7C ultrasound system with an X5-1 transducer. QLAB software was used for the 3D analysis of LV wall thickness (LVWT) and PM morphology and hypertrophy; the number and cross-sectional area (CSA) of anterolateral and posteromedial PMs; and the presence of bifid or accessory PMs.ResultsPatients with HC had a larger LVWT compared to controls in all segments (p < 0.001), and LVWT was largest in the midventricular septal segment (2.12 ± 0.68 cm). The maximum LVWT followed a spiral pattern from the LV base to the apex. The CSA of both anterolateral and posteromedial PMs was larger in patients with HC than in controls (1.92 vs. 1.15 cm2; p = 0.001 and 1.46 vs. 1.08 cm2; p = 0.033, respectively). The CSA of the posteromedial PM was larger in patients with LVOT obstruction than in those without (2.64 vs 1.16 cm2, p = 0.021).Conclusions3DE allows the assessment of LV geometry and PM abnormalities in patients with HC. 3DE demonstrated that the maximum hypertrophy was variable and generally located in a spiral from the LV base to the apex.
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