A robotic object hitting task to quantify sensorimotor impairments in participants with stroke.

BACKGROUND: Existing clinical scores of upper limb function often use observer-based ordinal scales that are subjective and commonly have floor and ceiling effects. The purpose of the present study was to develop an upper limb motor task to assess objectively the ability of participants to select and engage motor actions with both hands. METHODS: A bilateral robotic system was used to quantify upper limb sensorimotor function of participants with stroke. Participants performed an object hit task that required them to hit virtual balls moving towards them in the workspace with virtual paddles attached to each hand. Task difficulty was initially low, but increased with time by increasing the speed and number of balls in the workspace. Data were collected from 262 control participants and 154 participants with recent stroke. RESULTS: Control participants hit ~60 to 90% of the 300 balls with relatively symmetric performance for the two arms. Participants with recent stroke performed the task with most participants hitting fewer balls than 95% of healthy controls (67% of right-affected and 87% of left-affected strokes). Additionally, nearly all participants (97%) identified with visuospatial neglect hit fewer balls than healthy controls. More detailed analyses demonstrated that most participants with stroke displayed asymmetric performance between their affected and non-affected limbs with regards to number of balls hit, workspace area covered by the limb and hand speed. Inter-rater reliability of task parameters was high with half of the correlations above 0.90. Significant correlations were observed between many of the task parameters and the Functional Independence Measure and/or the Behavioural Inattention Test. CONCLUSIONS: As this object hit task requires just over two minutes to complete, it provides an objective and easy approach to quantify upper limb motor function and visuospatial skills following stroke.

Prediction of stroke-related diagnostic and prognostic measures using robot-based evaluation.

Traditional clinical scores for assessment of impairments resulting from stroke are inherently subjective and limited by inter-rater and intra-rater reliability. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of motor performance of stroke subjects. Although use of robotic technologies has been widely suggested in the literature, they are not an established tool and their relationship to traditional clinical scales for stroke diagnosis and prognosis is mostly unknown. In this study we propose the application of two non-linear system identification methods, Parallel Cascade Identification and Fast Orthogonal Search, for prediction of stroke-related clinical scores using robot-based metrics. We show the suitability of these two methods for prediction of both diagnostic and prognostic scores. We compare our results with a previously applied approach based on linear regression and show the superiority of our modeling approach. Our results also underscore the importance of quantifying proprioceptive deficits in the prediction of motor-related prognosis scores.

Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching.

OBJECTIVE: Using robotic technology, we examined the ability of a visually guided reaching task to assess the sensorimotor function of patients with stroke. METHODS: Ninety-one healthy participants and 52 with subacute stroke of mild to moderate severity (26 with left- and 26 with right-affected body sides) performed an unassisted reaching task using the KINARM robot. Each participant was assessed using 12 movement parameters that were grouped into 5 attributes of sensorimotor control. RESULTS: A number of movement parameters individually identified a large number of stroke participants as being different from 95% of the controls-most notably initial direction error, which identified 81% of left-affected patients. We also found interlimb differences in performance between the arms of those with stroke compared with controls. For example, whereas only 31% of left-affected participants showed differences in reaction time with their affected arm, 54% showed abnormal interlimb differences in reaction time. Good interrater reliability (r > 0.7) was observed for 9 of the 12 movement parameters. Finally, many stroke patients deemed impaired on the reaching task had been scored 6 or less on the arm portion of the Chedoke-McMaster Stroke Assessment Scale, but some who scored a normal 7 were also deemed impaired in reaching. CONCLUSIONS: Robotic technology using a visually guided reaching task can provide reliable information with greater sensitivity about a patient's sensorimotor impairments following stroke than a standard clinical assessment scale.

Quantitative assessment of limb position sense following stroke.

BACKGROUND: Impairment of position sense of the upper extremity (UE) may impede activities of daily living and limit motor gains after stroke. Most clinical assessments of position sense rely on categorical or ordinal ratings by clinicians that lack sensitivity to change or the ability to discriminate subtle deficits. OBJECTIVE: Use robotic technology to develop a reliable, quantitative technique with a continuous scale to assess UE position sense following stroke. METHODS: Forty-five patients recruited from an inpatient stroke rehabilitation service and 65 age-matched healthy controls performed an arm position matching task. Each UE was fitted in the exoskeleton of a KINARM device. One UE was passively placed in one of 9 positions, and the subject was told to match his or her position with the other UE. Patients were compared with statistical distributions of control data to identify those with deficits in UE position sense. Test-retest sessions using 2 raters established interrater reliability. RESULTS: Two thirds of left hemiparetic and one third of right hemiparetic patients had deficits in limb position sense. Left-affected stroke subjects demonstrated significantly more trial-to-trial variability than right-affected or control subjects. The robotic assessment technique demonstrated good interrater reliability but limited agreement with the clinical thumb localizing test. CONCLUSIONS: Robotic technology can provide a reliable quantitative means to assess deficits in limb position sense following stroke.

Recombination of common sensory-motor impairment evaluation techniques using a committee of classifiers.

Conventional methods for assessing levels of sensory-motor impairment in stroke patients are inherently subjective and dependent upon the clinician's own observations and opinions. In this study, 93 control and 63 stroke subjects underwent robotic assessment to gauge sensory-motor impairment. Multiple statistical data normalization and dimensionality-reduction measures were evaluated, using four different classifier types, in order to derive an optimal feature vector for the purpose of distinguishing stroke from control subjects. The optimal feature vector was then utilized to train a committee of classifiers for the purpose of recombining data from several traditional sensory-motor assessment scores into a KINARM specific assessment metric. We were able to create a training vector capable of distinguishing between stroke and control subjects with high accuracy, and demonstrated that the committee of classifiers assigned consistent scores to patients of similar levels of impairment.

Comparing protein contact maps via Universal Similarity Metric: an improvement in the noise-tolerance.

Comparing protein structures based on their contact maps similarity is an important problem in molecular biology. One motivation to seek fast algorithms for comparing contact maps is devising systems for reconstructing three-dimensional structure of proteins from their predicted contact maps. In this paper, we propose an algorithm to apply the Universal Similarity Metric (USM) to contact map comparison problem in a two-dimensional space. The major advantage of this algorithm is the highly improved noise-tolerance of the metric in comparison with its previous one-dimensional implementations. This is the first successful attempt to apply the USM to two-dimensional objects, without reducing their dimensionality.