A Novel Method to Generate Amplitude-Frequency Modulated Vibrotactile Stimulation.

The natural interaction of humans with their environment involves the harmonious coordination of the body, for which multi-modal feedback including vision, proprioception, and tactile perception is essential. Most human-machine interfaces, however, rely on the visual feedback only, and this can lead to considerable cognitive burden. Additional haptic feedback can increase the intuitiveness of the man-machine interaction. Therefore, we propose here a new device able to generate complex vibrotactile stimulation by simultaneously modulating the amplitude and frequency of vibration. Physical measurements were first performed in eight healthy subjects to assess the capability of the device to generate vibrations. The results indicated that the vibration frequency and amplitude can be independently modulated and that the device response to the full-range step-change in the amplitude/frequency commands is almost instantaneous and symmetric. In addition, psychophysical assessments were conducted in four healthy subjects using a standard psychophysical procedure (SIAM). The outcomes indicated that the proposed device can produce approximately 400 vixels (discriminable stimuli), which allow for the generation of a high diversity of vibrotactile patterns. The proposed method allows producing different kinds of stimulation patterns using motor types that are suited for specific applications, with adjustable trade-off between vibration intensity, size, and power consumption.

Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping.

Prosthesis users usually agree that myoelectric prostheses should be equipped with somatosensory feedback. However, the exact role of feedback and potential benefits are still elusive. The current study investigates the nature of human control processes within a specific context of routine grasping. Although the latter includes a fast feedforward control of the grasping force, the assumption was that the feedback would still be useful; it would communicate the outcome of the grasping trial, which the subjects could use to learn an internal model of feedforward control. Nine able-bodied subjects produced repeatedly a desired level of grasping force using different control configurations: feedback versus no-feedback, virtual versus real prosthetic hand, and joystick versus myocontrol. The outcome measures were the median and dispersion of the relative force errors. The results demonstrated that the feedback was successful in limiting the variability of the routine grasping due to uncertainties in the system and/or the command interface. The internal models of feedforward control could be employed by the subjects to control the prosthesis without the loss of performance even after the force feedback was removed. The models were, however, unstable over time, especially with myocontrol. Overall, the study demonstrates that the prosthesis system can be learned by the subjects using feedback. The feedback is also essential to maintain the model, and it could be delivered intermittently. This approach has practical advantages, but the level to which this mechanism can be truly exploited in practice depends directly on the consistency of the prosthesis control interface.

Closed-loop control of grasping with a myoelectric hand prosthesis: which are the relevant feedback variables for force control?

In closed-loop control of grasping by hand prostheses, the feedback information sent to the user is usually the actual controlled variable, i.e., the grasp force. Although this choice is intuitive and logical, the force production is only the last step in the process of grasping. Therefore, this study evaluated the performance in controlling grasp strength using a hand prosthesis operated through a complete grasping sequence while varying the feedback variables (e.g., closing velocity, grasping force), which were provided to the user visually or through vibrotactile stimulation. The experiments were conducted on 13 volunteers who controlled the Otto Bock Sensor Hand Speed prosthesis. Results showed that vibrotactile patterns were able to replace the visual feedback. Interestingly, the experiments demonstrated that direct force feedback was not essential for the control of grasping force. The subjects were indeed able to control the grip strength, predictively, by estimating the grasping force from the prosthesis velocity of closing. Therefore, grasping without explicit force feedback is not completely blind, contrary to what is usually assumed. In our study we analyzed grasping with a specific prosthetic device, but the outcomes are also applicable for other devices, with one or more degrees-of-freedom. The necessary condition is that the electromyography (EMG) signal directly and proportionally controls the velocity/grasp force of the hand, which is a common approach among EMG controlled prosthetic devices. The results provide important indications on the design of closed-loop EMG controlled prosthetic systems.