A Novel Sensory Feedback Approach to Facilitate Both Predictive and Corrective Control of Grasping Force in Myoelectric Prostheses.

Reliable force control is especially important when using myoelectric upper-limb prostheses as the force defines whether an object will be firmly grasped, damaged, or dropped. It is known from human motor control that the grasping of non-disabled subjects is based on a combination of anticipation and feedback correction. Inspired by this insight, the present study proposes a novel approach to provide artificial sensory feedback to the user of a myoelectric prosthesis using vibrotactile stimulation to facilitate both predictive and corrective processes characteristic of grasping in non-disabled people. Specifically, the level of EMG was conveyed to the subjects while closing the prosthesis (predictive strategy), whereas the actual grasping force was transmitted when the prosthesis closed (corrective strategy). To investigate if this combined EMG and force feedback is indeed an effective method to explicitly close the control loop, 16 non-disabled and 3 transradial amputee subjects performed a set of functional tasks, inspired by the "Box and Block" test, with six target force levels, in three conditions: no feedback, only EMG feedback, and combined feedback. The highest overall performance in non-disabled subjects was obtained with combined feedback (79.6±9.9%), whereas the lowest was achieved with no feedback (53±11.5%). The combined feedback, however, increased the task completion time compared to the other two conditions. A similar trend was obtained also in three amputee subjects. The results, therefore, indicate that the feedback inspired by human motor control is indeed an effective approach to improve prosthesis grasping in realistic conditions when other sources of feedback (vision and audition) are not blocked.

Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback.

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life.

Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant's own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users.

Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world conditions. We implemented a regression-based control approach that allows for simultaneous and proportional control of two degrees of freedom and evaluated it on five prosthetic end users. In the evaluation of tasks mimicking daily life activities, we included factors that limit reliability, such as tests in different arm positions and on different days. The regression approach was robust over multiple days and only slightly affected by changing in the arm position. Additionally, the regression approach outperformed two clinical control approaches in most conditions.

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.