Sanford G. Meek

Quantitation of human shoulder anatomy for prosthetic arm control-I. Surface modelling

Anatomical data and models for the human shoulder musculo-skeletal system are developed with the intent of quantifying physiological subcomponents of a model-based multi-axis prosthetic limb control scheme which has heretofore been implemented empirically. Part I presents the controller formulation, the surface descriptions of the muscles (and bones), and the centroidal trajectory data of the muscles. The data partially quantify the muscle modelling components of the controller, and set the stage for the analysis of the force-to-moment anatomical conversion factors of Part II.

Inertial-Force Feedback for the Treadport Locomotion Interface

The inertial force due to the acceleration of a locomotion interface is identified as a difference between virtual and real-world locomotion. To counter the inertial force, inertial-force feedback was implemented for the Treadport, a locomotion interface. A force controller was designed for a mechanical tether to apply the feedback force to the user. For the case of the user accelerating forward from rest, psychophysical ex periments showed that subjects preferred inertial-force feedback to a spring-feedback force proportional to position or to position control, where the force feedback maintained a force of zero on the subject.

Object Discrimination With an Artificial Hand Using Electrical Stimulation of Peripheral Tactile and Proprioceptive Pathways With Intrafascicular Electrodes

Trans-radial amputee subjects were implanted with intrafascicular electrodes in the stumps of the median and ulnar nerves. Electrical stimulation through these electrodes was used to provide sensations of touch and finger position referred to the amputated hand. Two subjects were asked to identify different objects as to size and stiffness by manipulating them with a myo-electric hand without visual or auditory cues. Both subjects were provided with information about contact force with the objects via tactile sensations referred to their phantom hands. One subject, who was provided with information about finger position in the prosthetic hand via a different tactile sensation referred to his phantom hand, was unable to correctly identify the objects. The other subject, who received information about finger position via a proprioceptive sensation referred to his phantom hand, correctly identified the objects at a level statistically significantly above chance performance.

Adaptive Sliding Mode Control for Prosthetic Hands to Simultaneously Prevent Slip and Minimize Deformation of Grasped Objects

Adaptive sliding mode and integral sliding mode grasped object slip prevention controllers are implemented for a prosthetic hand and compared to a proportional derivative shear force feedback slip prevention controller as well as a sliding mode controller without slip prevention capabilities. Slip of grasped objects is detected by band-pass filtering the shear force derivative to amplify high frequency vibrations that occur as the grasped object slides relative to the fingers. The integral sliding mode slip prevention controller provides a robust design framework for slip prevention while addressing the issue of reducing the amount of deformation that the grasped object experiences to prevent slip. Averaged results from bench top experiments show that the integral sliding mode slip prevention controller produces the least amount of deformation to the grasped object while simultaneously preventing the object from being dropped.

Hybrid Force–Velocity Sliding Mode Control of a Prosthetic Hand

Four different methods of hand prosthesis control are developed and examined experimentally. Open-loop control is shown to offer the least sensitivity when manipulating objects. Force feedback substantially improves upon open-loop control. However, it is shown that the inclusion of velocity and/or position feedback in a hybrid force-velocity control scheme can further improve the functionality of hand prostheses. Experimental results indicate that the sliding mode controller with force, position, and velocity feedback is less prone to unwanted force overshoot when initially grasping objects than the other controllers.

Fatigue compensation of the electromyographic signal for prosthetic control and force estimation

During a sustained muscle contraction, the amplitude of electromyographic (EMG) signals increases and the spectrum of the EMG signal shifts toward lower frequencies. These effects are due to muscular fatigue and can cause problems in the control of myoelectric prostheses and in the estimation of contraction level from the EMG signal. It has been well known that the fatigue effects can be explained by the conduction velocity changes during the fatigue process and by the idea that the conduction velocity is linearly proportional to the median frequency of EMG signals. Hence the fatigue process can be monitored by measuring the median frequency. A fatigue compensation preprocessor has been developed. It uses the widely accepted power spectrum density model of EMG signals that contains the conduction velocity as a measure of fatigue. It was verified that the preprocessor scales down the amplitude of the fatigued EMG signal and decompresses the spectrum. Hence, the preprocessor eliminates the increase in amplitude and the shift in frequency and enables consistent EMG signals to be used to control prostheses.<<ETX>>

Adaptive filtering of the electromyographic signal for prosthetic control and force estimation

An adaptive time constant filter is derived for electromyographic (EMG) signal processing in prosthetic control applications. The analysis indicates that the mean-squared estimation error can be reduced by varying the time constant of the filter as a function of the signal and its derivative. Results of several experiments indicated this filter provides faster response and smaller estimation error than several previously available filters.<<ETX>>

Mechatronics education in the Department of Mechanical Engineering at the University of Utah

The Department of Mechanical Engineering at the University of Utah has three approaches to mechatronics education. First, a basic course that is a required class for all mechanical engineering students has been developed. Second, a Certificate of Mechatronics which provides a recognition for the student with more advanced work in the area is offered. Third, an outreach program to provide mechatronic courses to working engineers is being developed.

Improved Grasp Force Sensitivity for Prosthetic Hands Through Force-Derivative Feedback

Sensitivity of applied grasp force is improved for a myoelectrically controlled prosthetic hand under force control through normal force-derivative feedback. Benchtop experiments and results from 12 human test subjects indicate that normal force-derivative feedback can be used in prosthetic hands to help prevent accidental damage to delicate objects.

Comparison of signal-to-noise ratio of myoelectric filters for prosthesis control

A comparison of signal-to-noise ratios and rise times was performed on several myoelectric filters used for muscle-force estimation and prosthesis control. Linear, averaging, and adaptive filters were compared using single as well as multiple electrode pairs (spatial filtering). The filters were matched for having the same rise time (0-95%) and the signal-to-noise ratios were measured off-line using the same myoelectric signal recording. The linear filter was a low-pass filter with a time constant of 80 ms. The averaging filter had an averaging time of 250 ms. The adaptive filter was the same as is used in the Utah Artificial Arm. The adaptive filter varied its time constant according to the rate of change of the signal mean. If the rate was high, the time constant was set low. If the rate was low, the time constant was set high. Spatial filtering is where the myoelectric signals from four cutaneous sites over the same muscle were summed, that is, spatially filtered, and the resultant signal was smoothed by the linear, averaging, or adaptive filter. Significant improvement in the signal-to-noise ratio has been shown over conventional linear or averaging filters when using spatial and adaptive filtering, both when used separately and when used together.