By Georgios Kouroupetroglou
Individuals with disabilities that hamper their variety of movement usually have hassle getting access to applied sciences. With using computer-based assistive expertise; units, instruments, and prone can be utilized to keep up and enhance the sensible functions of motor disabilities.
Assistive applied sciences and machine entry for Motor Disabilities investigates recommendations to the problems of impaired know-how entry by means of highlighting the rules, equipment, and complicated technological recommendations for people with motor impairments. This reference resource is useful to academia, undefined, and numerous pros in disciplines akin to rehabilitation technological know-how, occupational treatment, human-computer interface improvement, ergonomics, and educating in inclusive and certain schooling. This booklet is built-in with its pair publication incapacity Informatics and internet Accessibility for Motor Limitations.
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Additional info for Assistive Technologies and Computer Access for Motor Disabilities
Electromyographic (EMG) Signal: Biomedical signal that measures electrical currents generated in muscles during contraction, which represent neuromuscular activities. Gesture: Movement of the face or the hand that expresses something. Guided Under-Determined Source Signal Separation (GUSSS): Technique to identify a particular signature within an observed mixed signal. Haptics: Term that relates to sensing and manipulating objects and the environment through the sense of touch. Independent Component Analysis (ICA): Technique for identifying statistically independent signals that are linearly mixed together.
The features are the GUSSS ratio, which will be introduced shortly, and the MAV of the sEMG signals. The MAV is a commonly used time domain feature, as stated before. As in other systems, here it is also assumed that a sEMG signal is a mixture of MUAPT originating from different muscles (Webster, 2006). In other words, the sensed signals are linear combinations of independent MUAPTs due to cross-talk inside the subject’s limb. In the proposed Guided Under-determined Source Signal Separation, let x1 be such linear combination of N independent MUAPTs.
Et al. (2011). A tailored exercise of manipulation of virtual tools to treat upper limb impairment in multiple sclerosis. In Proceedings of the 12th IEEE International Conference on Rehabilitation Robotics,1112-1116. Zurich, Switzerland: IEEE Press. , & Knaflitz, M. (1998). A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Transactions on BioMedical Engineering, 45(3), 287–299. , & Krebs, H. (2009). MIT-skywalker. In Proceedings of the 11th IEEE International Conference on Rehabilitation Robotics, 542-549.