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

Mamidanna, Pranav; Dideriksen, Jakob Lund; Došen, Strahinja

doi:10.1088/1741-2552/ac23c1

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…A crossover design has been selected to control inter-group variability. In each session, the participants were instructed to perform the box and blocks test with two additional constraints, i.e., in each trial, they were required to (1) apply a specified level of force on the object (two levels of force were chosen as target forces -levels 4 and 5, see [10]), and (2) reach the target force within a specific time window. Thereby, we determined the speed-accuracy trade-off in a prosthesis force-matching task.…”

Section: Experimental Designmentioning

confidence: 99%

“…The time windows have been defined to capture the relevant domains of the SAF curve. Previous studies suggest that participants in a fast routine grasping task spend around 2s to achieve required force while they attain close to 100% accuracy at around the 6s mark [10]. In effect, we used a time-band methodology to derive the SAF [24].…”

Section: Experimental Designmentioning

confidence: 99%

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

Mamidanna¹,

Dideriksen²,

Došen³

2022

J. Neural Eng.

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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.

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

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

Mamidanna¹,

Dideriksen²,

Došen³

2022

J. Neural Eng.

Self Cite

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“…These will bring new insights to our next research: To study the influence of different force application methods on the force estimation of EMG. The work of Mamidanna et al (2021) on the grasping force of EMG prostheses also showed that better force estimation could be achieved by sacrificing the speed of grasping movement. Bog et al (2011) also designed different task profiles for grasping when studying the influence of different EMG characteristics on the estimation of palmar grasp, and found that the slope of the profile might lead to changes in the regression effect.…”

Section: Discussionmentioning

confidence: 99%

Natural grasping movement recognition and force estimation using electromyography

Zhang²,

Yang³

et al. 2022

Front. Neurosci.

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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.

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“…The sensory substitution process can be exploited non-invasively, involving the connection of a certain event with specific feedback that is not the natural one, such as tactile sensory feedback (Clemente et al, 2015 ; Dosen et al, 2016 ; Patel et al, 2016 ; Štrbac et al, 2016 ; Sensinger and Dosen, 2020 ). For example, the subject can be taught to associate a certain vibratory stimulus with the contact of the prosthesis with an object (Antfolk et al, 2012b ; Clemente et al, 2015 , 2019 ; Dosen et al, 2016 ; Štrbac et al, 2016 ; De Nunzio et al, 2017 ; Nemah et al, 2020 ; Mamidanna et al, 2021 ). In contrast, superficial stimulation could target portions of the missing limb's skin that are innervated by afferent neurons after the amputation, the so-called referred touch, to stimulate the phantom limb and improve the embodiment (Antfolk et al, 2013 ; Masteller et al, 2021 ), such as kinesthetic sensory feedback.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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IntroductionIn 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.MethodsBased 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.ResultsThe 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).

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

Cited by 13 publications

References 35 publications

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

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

Natural grasping movement recognition and force estimation using electromyography

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

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