SITAR: a system for independent task-oriented assessment and rehabilitation (bibtex)
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Bibtex Entry:
  Title                    = {SITAR: a system for independent task-oriented assessment and rehabilitation},
  Author                   = {Hussain,A and Balasubramanian, S and Roach, N and Klein, J and Jarrassé, N and Mace, M and David, A and Guy, S and Burdet,E},
  Journal                  = {Journal of Rehabilitation and Assistive Technologies Engineering},
  Year                     = {2017},
  Pages                    = {2055668317729637},
  Volume                   = {4},

  Abstract                 = { Introduction: Over recent years, task-oriented training has emerged as a dominant approach in neurorehabilitation. This article presents a novel, sensor-based system for independent task-oriented assessment and rehabilitation (SITAR) of the upper limb.Methods: The SITAR is an ecosystem of interactive devices including a touch and force–sensitive tabletop and a set of intelligent objects enabling functional interaction. In contrast to most existing sensor-based systems, SITAR provides natural training of visuomotor coordination through collocated visual and haptic workspaces alongside multimodal feedback, facilitating learning and its transfer to real tasks. We illustrate the possibilities offered by the SITAR for sensorimotor assessment and therapy through pilot assessment and usability studies.Results: The pilot data from the assessment study demonstrates how the system can be used to assess different aspects of upper limb reaching, pick-and-place and sensory tactile resolution tasks. The pilot usability study indicates that patients are able to train arm-reaching movements independently using the SITAR with minimal involvement of the therapist and that they were motivated to pursue the SITAR-based therapy.Conclusion: SITAR is a versatile, non-robotic tool that can be used to implement a range of therapeutic exercises and assessments for different types of patients, which is particularly well-suited for task-oriented training. },
  Category                 = {ACLI},
  Doi                      = {10.1177/2055668317729637},
  File                     = {:;:http\:// image}
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