The impact of objective functions on control policies in closed-loop control of grasping force with a myoelectric prosthesis

A compact solution for vibrotactile proprioceptive feedback of wrist rotation and hand aperture

BACKGROUND

Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions.

METHODS

We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback.

RESULTS

Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants.

CONCLUSIONS

The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process.Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined.Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill.Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Introduction

In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.

Methods

Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.

Results

The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

Natural grasping movement recognition and force estimation using electromyography

Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user’s experience and acceptance.

Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces

OBJECTIVE

Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed-accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces.

APPROACH

Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a one-week washout period. Importantly, both interfaces used (identical) direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). Therefore, we estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions.

MAIN RESULTS

We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands.

SIGNIFICANCE

Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed-accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces.