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 ( = 0 65), and strong correlation in both metacarpal arch joints ( = 075 and = 085), the MCP ( = 079), and PIP ( = 087) 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

Enforcing cyclic movements of the upper limb for movement analysis systems

Analysing the movement of the upper limb is difficult due to the variability and complexity of the mechanics available to complete any given task. When combining a clinical hand function assessment with the analysis of cyclic movement patterns clinicians are able to generate an independent assessment of function, in conjunction with waveforms of movement during a prehensile task, which is clinically relevant.Applying repetitive tasks to facilitate movement analysis is quite common and usually incorporates the use of Activities of Daily Living (ADL) [1,2] or reach-to-grasp tasks [3,4]. In 2001, Fowler, et al., employed an adapted version of a hand function test in order to provide more stable movement patterns [5]

Effect of gait cycle selection on EMG analysis during walking in adults and children with gait pathology

This paper presents the results of a project to evaluate different methods of gait cycle selection on the analysis of electromyography recorded during gait. Electromyography (EMG) describes the electrical activity associated with the muscle and is often interpreted in gait analysis using a simultaneously obtained signal to identify phases of the gait cycle. Phase transitions are often selected manually from reference signals derived from additional instrumentation, such as pressure platforms, footswitches and video cameras. We propose two methods (automatic and semi-automatic) as an alternative to the more traditional manual selection, and analyse how the gait cycle selection affects the EMG analysis. To quantify the differences between the gait cycles obtained using each method and to classify each cycle, three indices have been introduced. The effect of the gait cycle selection has been evaluated with respect to the EMG step profiles and temporal gait descriptors. An asymptomatic adult, an asymptomatic child and two children with cerebral palsy were examined using telemetric EMG devices and pressure footswitches. The results obtained showed that the method of gait cycle selection did not have a major influence for the adult, but it altered considerably the analysis in the case of the children with cerebral palsy