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

Abstract

BackgroundChronic 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 ResultsTo demonstrate the utility of our method we present examples of 2D performance maps produced from the kinetic and kinematics data 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (10.1186/s12984-017-0335-x) contains supplementary material, which is available to authorized users.

Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study

“…2 b) and attempted to perform 5 cm point-to-point reaching movements within an individually-set allotted time. Assistive and guiding forces were provided as needed by the vBot throughout the movement, using an algorithm adapted from [ 16 ] and described elsewhere [ 22 ]. Briefly, the assisting force ( Assist ) was provided in the direction towards the target and depended on the momentary speed towards the target relative to the speed expected in a minimum jerk trajectory [ 33 ], at the particular time point given the allotted movement time (which was set individually).…”

“…During tuning, the allotted movement time and stiffness of the vBot’s guiding force were individually adjusted based on a revised version of [ 16 ] and the final adjusted values were then fixed for each participant throughout the rest of the robot-assisted sessions [ 22 ].…”

“…Details of the procedure of mapping performance across movements can be found elsewhere [ 22 ]. Briefly, the mapping was based on performance measures (PM) adopted from [ 16 ] of ability to move (PM2) and to aim towards the target (PM3) relative to individually-set performance criterion.…”

“…Three measures of performance at the robot-assisted task (collected during the mapping sessions) served as secondary task-related outcome measures additional to the above-mentioned secondary clinical outcome measures. These included the overall level of the forces applied by the robot ( Assist and Guide ; [ 22 ]; averaged across 5 repetitions of each of the 64 assessed movements) and the mean movement end errors (the average distance of the final hand position from the target). Note that values of these measures can only indicate level of impaired performance (as the values converge to zero force (or error) for well-executed movements).…”

“…To apply our method we first developed a novel principle of mapping of robot-assisted reaching performance across two dimensions of target location and movement direction [ 22 ], informing us about postural and movement-related aspects of motor control, respectively—key factors in the planning and execution of reaching movements [ 23 – 25 ]. The performance maps then served to select movement sets for training, based on our “steepest gradients” principle.…”