Mapping upper-limb motor performance after stroke - a novel method with utility for individualized motor training

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Background: Chronic upper limb motor impairment is a common outcome of stroke. Therapeutic training can reduce motor impairment. Recently, a growing interest in evaluating motor training provided by robotic assistive devices has emerged. Robot-assisted therapy is attractive because it provides a means of increasing practice intensity without increasing the workload of physical therapists. However, movements practised through robotic assistive devices are commonly pre-defined and fixed across individuals. More optimal training may result from individualizing the selection of the trained movements based on the individual’s impairment profile. This requires quantitative assessment of the degree of the motor impairment prior to training, in relevant movement tasks.
However, standard clinical measures for profiling motor impairment after stroke are often subjective and lack precision. We have developed a novel robot-mediated method for systematic and fine-grained mapping (or profiling) of individual performance across a wide range of planar arm reaching movements. Here we describe and demonstrate this mapping method and its utilization for individualized training. We also present a novel principle for the individualized selection of training movements based on the performance maps.
Methods and Results: To demonstrate the utility of our method we present examples of 2D performance maps produced from the kinetic and kinematics ata of two individuals with stroke-related upper limb hemiparesis. The maps outline distinct regions of high motor impairment. The procedure of map-based selection of training movements and the change in motor performance following training is demonstrated for one participant.
Conclusions: The performance mapping method is feasible to produce (online or offline). The 2D maps are easy to interpret and to be utilized for selecting individual performance-based training. Different performance maps can be easily compared within and between individuals, which potentially has diagnostic utility.

Impaired upper-limb (UL) function is one of the most
common consequences of stroke [1–3], which can severely
hamper activities of daily living and reduce quality
of life. Certain intervention methods can promote some
recovery of UL motor function though their outcome
shows high variability and depends on the intensity
(repetition) of the intervention [4–9].
Robotic assistive technologies can be beneficial for improving
clinical scores of UL motor impairment [9, 10], by
allowing intensive training [9, 11–14]. However, currently
there is no consistent evidence for the effectiveness of robotassisted
UL therapy for improving daily living activity [15].
One possibility is that the tasks performed with robotic
assistance do not generalise to everyday tasks. Another
possibility is that the tasks are not optimised for the trained
individuals. Currently, in robot-assisted therapy the set of
practiced movements are usually pre-determined, with limited
regard to the motor profile of the individual (e.g. ‘centre-out’
point-to-point reaches, or forearm pronation/supination, wrist
extension/flexion [16–18]). However, the effectiveness of training
for motor recovery is likely to depend on the difficulty to
perform the task due to motor impairment [19]. For example,
training focused on unimpaired movements or on tasks that
* Correspondence:
1School of Psychology, University of Birmingham, B15 2TT, Birmingham, UK
Full list of author information is available at the end of the article
© The


Original languageEnglish
Article number127
JournalJournal of neuroengineering and rehabilitation
Publication statusPublished - 6 Dec 2017


  • Reaching task, rehabilitation, Stroke, Motor assessment, Robot-assisted therapy, Upper-limb movements