A comparative evaluation of time-delay, deep learning and echo state neural networks when used as simulated transhumeral prosthesis controllers

ACKNOWLEDGMENT The authors are grateful to ten anonymous, able-bodied, human participants who participated in the recording of all of the datasets used to train and test the above neural networks.Postprin

Feasibility of using combined EMG and kinematic signals for prosthesis control : A simulation study using a virtual reality environment

Acknowledgment This study was partly supported by a UK Medical Research Council Centenary Award to Keele University.Peer reviewedPublisher PD

A Real-Time, 3-D Musculoskeletal Model for Dynamic Simulation of Arm Movements

Neuroprostheses can be used to restore movement of the upper limb in individuals with high-level spinal cord injury. Development and evaluation of command and control schemes for such devices typically require real-time, ldquopatient-in-the-looprdquo experimentation. A real-time, 3-D, musculoskeletal model of the upper limb has been developed for use in a simulation environment to allow such testing to be carried out noninvasively. The model provides real-time feedback of human arm dynamics that can be displayed to the user in a virtual reality environment. The model has a 3-DOF glenohumeral joint as well as elbow flexion/extension and pronation/supination and contains 22 muscles of the shoulder and elbow divided into multiple elements. The model is able to run in real time on modest desktop hardware and demonstrates that a large-scale, 3-D model can be made to run in real time. This is a prerequisite for a real-time, whole-arm model that will form part of a dynamic arm simulator for use in the development, testing, and user training of neural prosthesis systems

Healthcare applications of single camera markerless motion capture: a scoping review

Funding This work was funded by a University of Aberdeen Elphinstone PhD scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Model-based control of individual finger movements for prosthetic hand function

The authors gratefully acknowledge the support of the Engineering and Physical Sciences Research Council (EP/M025977/1) and the National Institutes of Health (NIH5R01EB011615) in this research.Peer reviewedPostprin

Improving predictor selection for injury modelling methods in male footballers

This study evaluated whether combining existing methods of Elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real life applicability of injury prediction models in football. Predictor selection and model development was conducted on a pre-existing dataset, from a single English football teams’ 2015/2016 season. The Elastic Net for zero-inflated Poisson penalty method was successful shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, i.e. mass and body fat content, training type, duration and surface, fitness levels, normalised period of “no-play” and time in competition could contribute to the probability of acquiring a time loss injury. Furthermore, prolonged series of match play and increased in-season injury reduced the probability of not sustaining an injury. For predictor selection, the Elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time loss injuries have been identified appropriate methods for improving real life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.</p

The clinically extremely vulnerable to COVID: Identification and changes in healthcare while self-isolating (shielding) during the coronavirus pandemic.

Objective In March 2020, Scottish government identified people clinically extremely vulnerable to COVID due to pre-existing health conditions. These people were advised to strictly self-isolate (shield) at home. We examined who was identified as clinically extremely vulnerable, how their healthcare changed during isolation, and whether this process exacerbated healthcare inequalities. Approach We linked all individuals on the shielding register in NHS Grampian to their in-patient and out-patient healthcare records from 2015 through 2020. We analysed the method of patients’ identification as clinically extremely vulnerable (via an algorithmic NHS record scan or designated ad hoc by their care-providers). We measured out-patient, in-patient, and emergency healthcare attendances, and compared use rates between two 3-month periods before and during the first strict isolation period. We evaluated changes in care use between those shielding and the general non-shielding population, and differences between shielding sub-populations (by clinical reason for shielding, age, sex, and socio-economic deprivation). Results The shielding register included 16,092 people (3% of the population). 42% of people on the register were not identified by national healthcare record screening, including the majority of cancer and immunocompromised patients. People added to the register by their care-providers were more likely to be young and less economically-deprived. Shielders’ healthcare use decreased during isolation (rate compared to pre-isolation: 0.65 out-patient, 0.54 scheduled in-patient; 0.75 emergency in-patient; 0.71 A&E). However, people shielding had better maintained care than the non-shielding population (e.g. RR 2.9 for scheduled in-patient care). There were inequalities in whose scheduled care was maintained while shielding: younger people and those with cancer had significantly higher visit rates. However, there were no differences in care-preservation between men and women or between socioeconomic deprivation levels. Conclusions The reliance on emergency care while shielding indicates that, overall, continuity of care for existing conditions was not optimal. However, there was notable success in maintaining care for cancer. We suggest that integrating demographic and primary care data would improve identification of the clinically vulnerable and help equitably prioritise care