Publications by authors named "Ben W Hallworth"

Article Synopsis
  • Automating animal testing in neuroscience improves efficiency and objectivity, moving away from time-consuming and subjective manual methods.
  • The review covers various motor and non-motor tasks used to study neurological repair, emphasizing how these tasks inform rehabilitative training.
  • Two main automation strategies discussed are the use of devices for task execution and machine learning for unbiased data analysis, along with an evaluation of their pros and cons in behavioral neuroscience research.
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Task specific rehabilitation training is commonly used to treat motor dysfunction after neurological injures such as spinal cord injury (SCI), yet the use of task specific training in preclinical animal studies of SCI is not common. This is due in part to the difficulty in training animals to perform specific motor tasks, but also due to the lack of knowledge about optimal rehabilitation training parameters to maximize recovery. The single pellet reaching, grasping and retrieval (SPRGR) task (a.

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Novel myoelectric control strategies may yield more robust, capable prostheses which improve quality of life for those affected by upper-limb loss; however, the development and translation of such strategies from an experimental setting towards daily use by persons with limb loss is a slow and costly process. Since prosthesis functionality is highly dependent on the physical interface between the user's prosthetic socket and residual limb, assessment of such controllers under realistic (noisy) environmental conditions, integrated into prosthetic sockets, and with participants with amputation is essential for obtaining representative results. Unfortunately, this step is particularly difficult as participant- and control strategy-specific prosthetic sockets must be custom-designed and manufactured.

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