Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses

Patel, Gauravkumar K.; Hahne, Janne M.; Castellini, Claudio; Farina, Dario; Došen, Strahinja

doi:10.1088/1741-2552/aa7e82

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…Semi-autonomous control using computer vision can allow simple control of advanced bionic limbs, thereby improving user experience and potentially decreasing rejection rates. In addition, recent studies in conventional human-machine interfacing also propose to equip a prosthesis with advanced sensors that might require complex data processing; e.g., inertial measurement units [50], electronic skins [51], musculoskeletal modeling [52], high-density EMG [53]. To support these developments as well as general user demands for better control and functionality [54], modern prostheses will need to integrate improved processing capabilities (see [5]).…”

Section: Discussionmentioning

confidence: 99%

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Ragusa¹,

Gianoglio

Došen

et al. 2021

IEEE Access

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Affordance detection in computer vision allows segmenting an object into parts according to functions that those parts afford. Most solutions for affordance detection are developed in robotics using deep learning architectures that require substantial computing power. Therefore, these approaches are not convenient for application in embedded systems with limited resources. For instance, computer vision is used in smart prosthetic limbs, and in this context, affordance detection could be employed to determine the graspable segments of an object, which is a critical information for selecting a grasping strategy. This work proposes an affordance detection strategy based on hardware-aware deep learning solutions. Experimental results confirmed that the proposed solution achieves comparable accuracy with respect to the state-ofthe-art approaches. In addition, the model was implemented on real-time embedded devices obtaining a high FPS rate, with limited power consumption. Finally, the experimental assessment in realistic conditions demonstrated that the developed method is robust and reliable. As a major outcome, the paper proposes and characterizes the first complete embedded solution for affordance detection in embedded devices. Such a solution could be used to substantially improve computer vision based prosthesis control but it is also highly relevant for other applications (e.g., resource-constrained robotic systems).

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Section: Discussionmentioning

confidence: 99%

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Ragusa¹,

Gianoglio

Došen

et al. 2021

IEEE Access

Self Cite

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“…It has been shown that a trade-off has to be made when deciding the level of sharing of control between the user and the hardware, and an intermediate level of interaction between the two was favored (Cipriani et al, 2008 ). Following this idea, context-dependent switching scheme can be found in several recent studies to control kinematics of the prosthetic hands based on sEMG pattern (Amsuess et al, 2016 ), limb kinematics and/or grasp force (Jiang et al, 2013 ; Patel et al, 2017 ), or vision (Markovic et al, 2014 , 2015 ; Ghazaei et al, 2017 ). It has been argued that such semi-autonomous shared control can help to shield some low level execution details and decreases cognitive burden while maintaining high level function (Castellini et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

Santello

2018

Front. Neurorobot.

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The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.

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“…This is because their reliance on a potentially incorrect underlying model may result in a context that is isolated from, and possibly in disagreement with, the real-world environment. Others have sought to improve situational context by including additional sensors, such as cameras embedded within the prosthesis [37][38][39]. These are examples of contextaware myoelectric control systems that incorporate environmental information into their classifier inputs (see Fig.…”

Section: Contextmentioning

confidence: 99%

Context-informed incremental learning improves both the performance and resilience of myoelectric control

Campbell,

Eddy,

Bateman

et al. 2024

J NeuroEngineering Rehabil

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Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model’s parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

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Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses

Cited by 15 publications

References 32 publications

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

Context-informed incremental learning improves both the performance and resilience of myoelectric control

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