Posts Tagged Motor assessment

[Abstract] The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study

Abstract

Musical sonification therapy is a new technique that can reinforce conventional rehabilitation treatments by increasing therapy intensity and engagement through challenging and motivating exercises. Aim of this study is to evaluate the feasibility and validity of the SonicHand protocol, a new training and assessment method for the rehabilitation of hand function. The study was conducted in 15 healthy individuals and 15 stroke patients. The feasibility of implementation of the training protocol was tested in stroke patients only, who practiced a series of exercises concurrently to music sequences produced by specific movements. The assessment protocol evaluated hand motor performance during pronation/supination, wrist horizontal flexion/extension and hand grasp without sonification. From hand position data, 15 quantitative parameters were computed evaluating mean velocity, movement smoothness and angular excursions of hand/fingers. We validated this assessment in terms of its ability to discriminate between patients and healthy subjects, test-retest reliability and concurrent validity with the upper limb section of the Fugl-Meyer scale (FM), the Functional Independence Measure (FIM) and the Box & Block Test (BBT). All patients showed good understanding of the assigned tasks and were able to correctly execute the proposed training protocol, confirming its feasibility. A moderate-to-excellent intraclass correlation coefficient was found in 8/15 computed parameters. Moderate-to-strong correlation was found between the measured parameters and the clinical scales. The SonicHand training protocol is feasible and the assessment protocol showed good to excellent between-group discrimination ability, reliability and concurrent validity, thus enabling the implementation of new personalized and motivating training programs employing sonification for the rehabilitation of hand function.

via The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study – IEEE Journals & Magazine

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[ARTICLE] Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training – Full Text

Abstract

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 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.

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.

Background

Impaired upper-limb (UL) function is one of the most common consequences of stroke [123], 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 [456789].

Robotic assistive technologies can be beneficial for improving clinical scores of UL motor impairment [910], by allowing intensive training [911121314]. However, currently there is no consistent evidence for the effectiveness of robot-assisted 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 [161718]). 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 are either too easy or too difficult is likely to contribute relatively little to motor learning and recovery [192021]. An advantage of the robot-mediated approach is that it allows the collection of various accurate and real-time data about motor performance that would be potentially useful for individualized adjustments of the therapy; e.g. selection of training tasks based on the profile of motor performance. Yet, prescribing training conditions based on a motor performance profile requires characterising motor performance across a range of movement conditions for each individual. Here we present a novel computerised method for systematically mapping individuals’ UL motor performance (or impairment) across a wide range of robot-mediated reaching movements. The map can then serve as a basis for individualised and performance-based selection of training movements.

For optimal utilization of a motor performance map, the mapped metrics should reflect basic components of sensorimotor control, so that the map can be directly linked to processes underlying the movements (e.g. muscle activity and movement representation). Continuous metrics, allowing smoothing and interpolation from tested movements to neighbouring untested regions are also valuable. Accordingly, our mapping of reaching performance is done across the two dimensions of target location (in angular coordinates relative to a central position) and of prescribed starting location (again in angular coordinates relative to the selected target, which indicates the dictated movement direction). The range of target and start locations tests both postural and movement-related aspects of motor control, respectively. Importantly, muscle activation patterns and population neural activity in the motor-related cortices show tuning to one or both task dimensions [22232425], and behavioural studies support the essential underlying role of these parameters in planning of reaching movements [2627].

Of course, the usefulness of a motor performance map for prescribing performance-based training also depends on an appropriate principle for the selection of movements to be practiced. Here we demonstrate the utility of our mapping method for individualized task selection based on a principle which we term “steepest gradients” (SG), although the motor performance map can be the basis for alternative task selection principles. The SG principle is founded on the idea that training with tasks performed with an intermediate range of difficulty would allow more improvement and learning-induced plasticity, compared to training with very difficult or easy tasks [1928] .

Here we report the details of the mapping methods, and show its efficacy in portraying relevant motor impairment patterns for individual subjects. We also briefly demonstrate its utility for individually-tailored selection of practiced movement using the SG principle. However, our evidence for the utility and benefit of the mapping method for individualizing UL robot-mediated rehabilitation after stroke will be reported in subsequent publications.[…]

 

Continue —> Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1Schematic description of the experimental setting (top view). a The participant held the handle of a robotic manipulandum (indicated onscreen by a red disc; not shown), which allowed planar reaching movements from a start position (white onscreen disc (here gray) to a target position (blue onscreen disc; here black) and provided assisting and guiding forces as needed. Hand’s grip was maintained via a special glove and the forearm was supported against gravity (not shown). The participant leaned his/her head against a headrest, maintaining upright seating posture (ensured using a harness). The horizontal display occluded the hand and the manipulandum from vision. The start-to-target axis (y) and its perpendicular axis (x) correspond to the axes of the assisting and guiding forces, respectively, which were provided during the arm movement as needed by the robot. Adapted from Howard et al. (2009). b The reaching workspace used for mapping performance. The locations of the 8 targets, used in the mapping sessions, are indicated by small open circles. An example of the arm posture when the hand located at the 90o target is shown. Participants were tested with 5cm reaches to each target from 8 start locations (indicated, for the example target, by small black dots). The dashed circle indicates the extent of the mapped workspace. The drawing reflects the actual relationship of target and start locations and arm posture, based on a photograph taken with a healthy participant

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