Real-time error detection but not error correction drives automatic visuomotor adaptation

We investigated the role of visual feedback of task performance in visuomotor adaptation. Participants produced novel two degrees of freedom movements (elbow flexion-extension, forearm pronation-supination) to move a cursor towards visual targets. Following trials with no rotation, participants were exposed to a 60A degrees visuomotor rotation, before returning to the non-rotated condition. A colour cue on each trial permitted identification of the rotated/non-rotated contexts. Participants could not see their arm but received continuous and concurrent visual feedback (CF) of a cursor representing limb position or post-trial visual feedback (PF) representing the movement trajectory. Separate groups of participants who received CF were instructed that online modifications of their movements either were, or were not, permissible as a means of improving performance. Feedforward-mediated performance improvements occurred for both CF and PF groups in the rotated environment. Furthermore, for CF participants this adaptation occurred regardless of whether feedback modifications of motor commands were permissible. Upon re-exposure to the non-rotated environment participants in the CF, but not PF, groups exhibited post-training aftereffects, manifested as greater angular deviations from a straight initial trajectory, with respect to the pre-rotation trials. Accordingly, the nature of the performance improvements that occurred was dependent upon the timing of the visual feedback of task performance. Continuous visual feedback of task performance during task execution appears critical in realising automatic visuomotor adaptation through a recalibration of the visuomotor mapping that transforms visual inputs into appropriate motor commands.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Utility, Caller, and Patient Profile of a Novel Chemotherapy Telephone Helpline Service Within a Regional Cancer Centre in Northern Ireland

Background: The telephone is an accepted and useful means of communication for the management of patient care. The Chemotherapy Telephone Helpline (CTH) service, located in a large inner-city Trust within the United Kingdom, is a unique nurse-led service within Northern Ireland.

Objective: The objective of the study was to investigate the utility, caller, and patient profile of a novel CTH.

Methods: This was a retrospective study of telephone contacts during 2007 to the CTH. Calls were categorized by caller and patient characteristics, reason for call, and subsequent action.

Results: A total of 7498 calls were made to the CTH during 2007. Of these, 25.6% occurred outside 8AM-4PM. Callers included patients (45.8%), lay carers (31%), and health care professionals (20.5%); 35.2% of calls concerned patients with polysymptomatic problems; 36.8% of calls led directly to patients being medically assessed.

Conclusions: The utility of the CTH service confirms the need of this nurse-led service. This service facilitates access to specialist advice and support for patients, their families, and allied health care professionals.

Implications for Practice: The international significance of these findings for practice includes its demonstration of the multifaceted symptom experience of patients receiving chemotherapy and highlights the importance of rapid access to specialist cancer services for patients and their lay and professional carers. In addition, it demonstrates the capacity of helplines to identify gaps in professional skills and training.