Scott V. Notley

Validation and Application of a Computational Model for Wrist and Hand Movements Using Surface Markers

A kinematic model is presented based on surface marker placement generating wrist, metacarpal arch, fingers and thumb movements. Standard calculations are used throughout the model and then applied to the specified marker placement. A static trial involving eight unimpaired participants was carried out to assess inter-rater reliability. The standard deviations across the data were comparable to manual goniometers. In addition, a test-retest trial of ten unimpaired participants is also reported to illustrate the variability of movement at the wrist joint, metacarpal arch, and index finger as an example of model output when repeating the same task many times. Light and heavyweight versions of the tasks are assessed and characteristics of individual movement strategies presented. The participant trial showed moderate correlation in radial/ulnar deviation of the wrist (r = 0.65), and strong correlation in both metacarpal arch joints (r = 0.75 and r = 0.85), the MCP (r = 0.79), and PIP (r = 0.87) joints of the index finger. The results indicate that individuals use repeated strategies of movement when lifting light and heavyweight versions of the same object, but showed no obvious repeated pattern of movement across the population.

Reliability and Sensitivity of a Wrist Rig to Measure Motor Control and Spasticity in Poststroke Hemiplegia

Background. Objective assessment of impairments after stroke is vital for evidence-based therapy and progress monitoring. Objective. This study determines the utility of outcome measures obtained from an instrumented wrist rig for future rehabilitation trials. The tests undertaken were evaluated in terms of sensitivity to detect differences between normal and impaired participants, test-retest repeatability (repeatability coefficient and intraclass correlation coefficient [ICC]), and interrater agreement (Bland and Altman limits of agreement). Methods . Twelve participants with chronic poststroke hemiparesis (mean 5.6 years); and 12 unimpaired volunteers performed a series of tasks in the rig. The hemiparetic arm (impaired group) and dominant arm (unimpaired group) were tested in 3 sessions on the same day by 2 assessors. Signals were analyzed to derive a tracking index (motor control), stretch index (spasticity), flexor modulation index (FMI) (muscle activation), force angle index (FAI) (stiffness), range of movement, and isometric force. Results and Conclusions. The means of all tests differed between impaired and unimpaired participants except for range of movement into flexion, the FAI, and the FMI. Repeatability coefficients for each test are presented as benchmark values for use in future trials in which the wrist rig tests may be used to detect change. Test-retest reliability was excellent in the impaired group (ICC = 0.88-0.98) and poor to excellent in the unimpaired group (ICC = 0.06-0.89). The Bland-Altman ranges showed no bias between assessors, and that the interassessor variability was similar to that between repeats by the same assessor for most tests.

The relationship between upper limb activity and impairment in post-stroke hemiplegia

Purpose. To investigate the relationship between upper limb impairments and activity limitation. Method. A cross sectional, single assessment observational study in which people with hemiplegia as a result of a stroke underwent a testing procedure in an instrumented wrist rig in which the following measures of impairment were recorded: Spasticity; motor control (ability to track a moving target); muscle activation patterns during tracking; stiffness; range of active movement and isometric muscle strength. Participants also performed clinical tests of upper limb activity (Action Research Arm Test) and hyper-tonicity (Modified Ashworth Scale). Results. Seventeen people with hemiplegia whose mean age was 57 (SD 13.4) took part. Their mean upper limb activity, measured by the Action Research Arm Test, was 19.3 (SD 11.2). Statistically significant positive relationships between level of activity and the negative features of the upper motor neuron syndrome such as motor control r = 0.710 (p = 0.003), active range of movement r = 0.540 (p = 0.025) and strength into flexion r = 0.515 (p = 0.034) and extension r = 0.575 (p = 0.016) were identified, but not with the positive features, such as spasticity or the secondary features such as stiffness. Conclusions. The negative features of the upper motor neuron syndrome appear more likely to affect upper limb activity than the positive or secondary features, but findings need confirming in different study populations, preferably with larger samples.

Efficient estimation of a time-varying dimension parameter and its application to EEG analysis

Considers the problem of estimating the dimension of nonstationary electroencephalogram (EEG) signals and describes the implementation of an efficient algorithm to calculate a time-varying dimension estimate. The algorithm allows the practical calculation of a dimension estimate and its statistical significance over large data sets with a high temporal resolution. The method is applied to EEG recordings from patients with temporal lobe epilepsy and in one case the results of the analysis are compared with those obtained from an existing method of computing the correlation density.

Modified Kinematic Technique for Measuring Pathological Hyperextension and Hypermobility of the Interphalangeal Joints

Dynamic finger joint motion is difficult to measure using optical motion analysis techniques due to the limited surface area allowed for adequate marker placement. This paper describes an extension of a previously validated kinematic measurement technique using a reduced surface marker set and outlines the required calculations based on a specific surface marker placement to calculate flexion/extension and hyperextension of the metacarpophalangeal, proximal interphalangeal, and distal interphalangeal joints. The modified technique has been assessed for accuracy using a series of static reference frames (absolute residual error = ±3.7°, cross correlation between new method and reference frames; r = 0.99). The method was then applied to a small group of participants with rheumatoid arthritis (seven females, one male; mean age = 62.8 years ± 12.04) and illustrated congruent strategies of movement for a participant and a large range of finger joint movement over the sample (5.8-71.1°, smallest to largest active range of motion). This method used alongside the previous paper provides a comprehensive, validated method for calculating 3-D wrist, hand, fingers, and thumb kinematics to date and provides a valuable measurement tool for clinical research.

Optimal nonlinear filter to remove random impulses from Gaussian noise

This paper investigates the problem of removing random impulse noise from a white signal of Gaussian distribution. A nonlinear polynomial filter is used, whose coefficients are optimised using an exact least squares method. The method relies on exploiting the differing probability distributions of the impulsive noise and the Gaussian signal. The paper then looks at the effect of both the polynomial order and the normalised spike amplitude on the mean squared error and signal to noise ratio. The results are compared to the results found using a simple clipping filter. The results show that the optimal filter gives a much improved performance over the simple clipping filter in reducing the mean square error.