Assessment of input-output properties and control of neuroprosthetic hand grasp
- 1 June 1992
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 39 (6) , 610-623
- https://doi.org/10.1109/10.141199
Abstract
Three tests have been developed to evaluate rapidly and quantitatively the input-output properties and patient control of neuroprosthetic hand grasp. Each test utilizes a visual pursuit tracking task during which the subject controls the grasp force and grasp opening (position) of the hand. The first test characterizes the static input-output properties of the hand grasp, where the input is a slowly changing patient generated command signal and the outputs are grasp force and grasp opening. Nonlinearities and inappropriate slopes have been documented in these relationships, and in some instances the need for system returning has been indicated. For each subject larger grasp forces were produced when grasping larger objects, and for some subjects the shapes of the relationships also varied with object size. The second test quantifies the ability of the subject to control the hand grasp outputs while tracking steps and ramps. Neuroprosthesis users had rms errors two to three times larger when tracking steps versus ramps, and had rms errors four to five times larger than normals when tracking ramps. The third test provides an estimate of the frequency response of the hand grasp system dynamics, from input and output data collected during a random tracking task. Transfer functions were estimated by spectral analysis after removal of the static input-output nonlinearities measured in the first test. The dynamics had low-pass filter characteristics with 3 dB cutoff frequencies from 1.0 to 1.4 Hz. The tests developed in this study provide a rapid evaluation of both the system and the user. They provide information to 1) help interpret subject performance of functional tasks, 2) evaluate the efficacy of system features such as closed-loop control, and 3) screen the neuroprosthesis to indicate the need for retuning.Keywords
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