MACSManual Ability Classification System AIM The aim of this study was to analyze the cursor trajectories of adolescents with cerebral palsy (CP) when using a mouse for point-and-click computer tasks. By identifying some of the factors limiting cursor movement and gaining a better understanding of human movement, it will be possible to design more accessible computer interfaces.METHOD This study evaluated cursor trajectories of 29 individuals with bilateral CP who had different levels of upper limb function as measured by the Manual Ability Classification System, and compared the results with those of 12 adolescents with typical development.RESULTS Among adolescents with typical development, movement time increases linearly as the index of difficulty increases (Fitts' law); however, this linearity was not apparent in adolescents with bilateral CP.

This paper provides a detailed model for analyzing movement time performance during rapid goal-directed point- and-click motions with a computer mouse. Twelve typically developed individuals and eleven youths with cerebral palsy conducted point and click computer tasks from which the model was developed. The proposed model is nonlinear and based on both system (target width and movement amplitude) and human effects (erroneous clicks, number of submovements, number of slip-offs, curvature index, and average speed). To ensure successful targeting by youths with cerebral palsy, the index of difficulty was limited to a range of 1.58 - 3.0 bits. For consistency, the same range was used with both groups. The most significant contributing human effect to movement time was found to be the curvature index for both typically developed individuals and individuals with cerebral palsy. This model will assist in algorithm development to improve cursor speed and accuracy for youths with cerebral palsy.

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A novel tool of bio signal processing is proposed to identify human muscle action through sEMG. The tool is based on Integration of continuous wavelet transforms, wavelet time entropy and wavelet frequency entropy to identify muscle actions through sEMG. The experiments are carried out on triceps, biceps and flexor digitorum superficial (FDS) muscles. sEMG signals are measured at different intensities of FDS muscle contractions in order to verify the consistency of results. By taking the average entropies and based on lowest average wavelet entropy, it is found in calibrated experiment that complex Shannon wavelet family is the best candidate to identify the muscle activities among: Derivative of Gaussians wavelet family, Derivative of Complex Gaussians wavelet family, Complex Morlet family, Symlets, Coiflets and Daubechies wavelet families. Moreover, the results are consistent over the time-variant signal. The results presented in this paper have futuristic engineering implication in biomedical engineering and bio-robotic applications.