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. 2009 Dec;3(6):398-404.
doi: 10.1109/TBCAS.2009.2032396.

Conveying Tactile Feedback in Sensorized Hand Neuroprostheses Using a Biofidelic Model of Mechanotransduction

Free PMC article

Conveying Tactile Feedback in Sensorized Hand Neuroprostheses Using a Biofidelic Model of Mechanotransduction

Sung Soo Kim et al. IEEE Trans Biomed Circuits Syst. .
Free PMC article

Abstract

One approach to conveying tactile feedback from sensorized neural prostheses is to characterize the neural signals that would normally be produced in an intact limb and reproduce them through electrical stimulation of the residual peripheral nerves. Toward this end, we have developed a model that accurately replicates the neural activity evoked by any dynamic stimulus in the three types of mechanoreceptive afferents that innervate the glabrous skin of the hand. The model takes as input the position of the stimulus as a function of time, along with its first (velocity), second (acceleration), and third (jerk) derivatives. This input is filtered and passed through an integrate-and-fire mechanism to generate a train of spikes as output. The major conclusion of this study is that the timing of individual spikes evoked in mechanoreceptive fibers innervating the hand can be accurately predicted by this model. We discuss how this model can be integrated in a sensorized prosthesis and show that the activity in a population of simulated afferents conveys information about the location, timing, and magnitude of contact between the hand and an object.

Figures

Fig. 1
Fig. 1
Diagram of a sensorized prosthetic limb. Force signals from sensors on the fingertips of the prosthesis are converted by using the model described here (and visually represented as a microchip) into the spike trains that would be evoked in the native limb by the stimulus. The spike trains are then effected into the residual nerve fibers through electrical stimulation.
Fig. 2
Fig. 2
Diagram of the full leaky and noisy integrate-and-fire model used to predict afferent activity. The model comprises eight inputs, corresponding to the positive and negative components of position, velocity, acceleration, and jerk, each of which is passed through a linear prefilter. The summed output of the prefilters constitutes the input to the IF mechanism. When the membrane potential of the IF mechanism reaches threshold, a spike is produced and an inhibitory current is released to mimic refractoriness.
Fig. 3
Fig. 3
Measured (red) and predicted (blue) spike trains evoked by three stimuli (black traces) for (A) and SA1 and (B) an RA afferent. Each row in the red raster plot shows the response evoked on each of the five presentations of the stimulus. The blue raster plot shows the spike trains predicted by the model with parameters estimated by using the same stimuli.
Fig. 4
Fig. 4
Measured and predicted spike trains evoked by two stimuli for (A) an SA1 and (B) an RA afferent (conventions as in Fig. 3). Parameters obtained from the training set were used to derive predictions to novel stimuli. Each row in the red raster plot shows the response evoked on each of five presentations of the stimulus. The blue raster plot shows the spike trains predicted by the model with parameters estimated by using the same stimuli.
Fig. 5
Fig. 5
Distribution of Dspike for pairs of measured responses to the same stimuli (red) and for predicted responses paired up with their measured counterparts (blue), pooled across stimuli for all of the SA1 and RA fibers. Dspike is an index of the cost required to change one spike train into another; the greater the difference between the two trains, the greater the cost. The cost associated with adding or subtracting one spike is 1, and the cost for shifting a spike by 1 ms is 0.25. Other values of the cost parameter yielded similar results.
Fig. 6
Fig. 6
Illustration of the biofidelic approach to tactile feedback. A hand equipped with a sensorized glove grasps, picks up, and puts down a water bottle. Each colored trace shows the force exerted on a fingertip or on the palm as a function of time. The outputs of the force sensors on the digits and palm are used as inputs to five clones each of four SA1 and four RA afferents on each digit and on the palm, for a total of 240 simulated afferents. For the purposes of this simulation, we assumed that the force exerted on each digit was equally distributed across all of the stimulated receptors. The model is designed for more localized force sensors whose receptive fields are approximately the same size as that of mechanoreceptive afferents. Each group of colored rasters corresponds to the activity evoked in a population of afferents whose receptive fields are located on a single digit or on the palm (colors match those of the displacement traces). The peristimulus spike histograms are shown under the corresponding rasters showing the mean response across the population of SA1 and RA fibers. As can be seen from the figure, simulated SA1 and RA afferents produce qualitatively different responses during the manipulation of the bottle. SA1 afferents tend to respond throughout the contact period whereas RA afferents respond at the onset and offset of contact. Information about the location of contact on the hand, timing of contact, and force of contact are contained in the activity of the population of afferents.

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