No abstract

Spinal cord injured (SCI) subjects lack sensorimotor functions. Neuromuscular electrical stimulation (NMES) systems have been used to artificially restore motor functions, but without proprioceptive feedback, SCI subjects can control NMES systems only when they can see their limbs. In a gait restoration system, the subject looks down to the ground to be aware of where his foot is while in a grasping activity, maximum grip strength is employed regardless of the force that is required to perform tasks. This report focuses on artificial sensorimotor integration. Multichannel stimulation was used to restore motor functions while encoded tactile sensation (moving fused phantom images) relating to artificially generated movements was provided by electrotactile stimulation during walking and grasping activities. The results showed that the sensorimotor integration attained yielded both the recognition of artificial grasp force patterns and a technique to be used by paraplegics allowing spatial awareness of their limb while walking.

BackgroundMyoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity.MethodsExperiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive–Negative Performance Measurement Index (PNM), generated by a series of confusion matrices.ResultsWhen using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion.ConclusionThis work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.

Neuromuscular electrical stimulation (NMES) has been used in upper limb rehabilitation towards restoring motor hand function. Quantitative evaluation of the artificially generated movement is necessary to achieve proper muscle activation. Custom-made gloves instrumented with force and position transducers were used to evaluate artificial quadriplegic grasping for a drinking activity. In spite of different sensor position, stimulation parameter dependence and lack of repeatability, grasp patterns achieved with the application of NMES follow the same patterns previously obtained with normal subjects, regarding force distribution among fingers and the shape of force curves. Larger forces were exerted by the thumb (average ranged from 2.8 to 4.5 N) following by index or long finger (average ranged from 1.8 to 3 N). The forces exerted ranged within the same interval as those previously measured and were sufficient to grasp an object of 10 N. Finger position achieved by interphalangeal joint status indicated the opening size of the hand throughout the range of movement. The instrumented gloves offer an alternative force and position feedback system for use in cylindrical grasp evaluation. The gloves can be used in a closed-loop control system, allowing on-line adjustment or in a clinical application to evaluate the results of a rehabilitation programme.