Optimizing upper-limb prosthesis control
using computational motor control
using computational motor control
Lead Institution: UNB
Openings: Currently accepting postdocs and Masters/PhD students
Prostheses give people with an amputation hope that they can restore lost abilities needed for activities of daily life. The field has made significant advances in understanding the EMG signals amputees use to control robotic prostheses [1–7] and improving actuators and feedback devices [8–10]. Nonetheless, existing devices are rejected by half of upper-limb amputees [11], [12], who perceive the largest failing of these devices to be lack of intuitive control and inadequate sensory feedback [13]. Indeed, amputee movement with robotic prostheses remains slow, imprecise, and cognitively burdensome [14], [15], and surprisingly, haptic sensory substitution provides little improvement if vision is available [16–18]. Human control signals and feedback sources are time-varying and high-dimensional. Hence it is difficult for engineers and clinicians to develop efficient control schemes and feedback sources. The key to solving this problem is to develop a theory-driven framework of how the amputee’s brain processes their limited sensory cues and noisy control signals. Computational motor control models have had promising success describing how able-bodied subjects balance the uncertainty of sensory cues and control signals across a variety of experimental paradigms to achieve efficient motor control [19–28]. We need computational models of the relative contribution amputees place on their more limited sensory cues and their noisy control signals to predict which control schemes and feedback will enable the greatest improvement in precise, efficient control of robotic prostheses.
Myoelectric control provides a unique application in that it is extremely noisy, with limited feedback, and the application has caused us to reassess several tenants of the general theory of CMC. Towards this end, we have assessed the generalization of cost structures [29] (finding that they generalize across interfaces and from static to dynamic movements). We have also improved models of internal model strength and provided analytical contributions to measuring adaptation rate [30-35], and extended this work to related concepts such as agency / incorporation [36-37].
We have used these models to gain insight into why augmented sensory feedback so often fails to improve the performance of myoelectric interfaces, and what feedback would be useful in machine-learning techniques. Our results have achieved our field’s longstanding goal of showing improvements in performance even in the presence of vision [38, c.f. 39]. We have extended this work through several international collaborations [40-44].
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[29] Sensinger J, Aleman A, Englehart K. (2015). Do cost-functions for tracking error generalize across tasks with different noise levels? PLoS One
[30] Blustein D, Shehata A, Englehart K, Sensinger J. (2018). Conventional analysis of trial-by-trial adaptation is biased: empirical and theoretical support using a Bayesian estimator. PLoS Computational Biology.
[31] Shehata A, Scheme E, Sensinger J. (2018). Evaluating internal model strength and performance of myoelectric prosthesis control strategies. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[32] Johnson R, Kording K, Hargrove L, Sensinger J. (2017). Adapting to random and systematic errors: How amputees using myoelectric control differ from intact-limbed subjects using reaching movements. PLoS One
[33] Johnson R, Kording K, Hargrove L, Sensinger J. (2017). EMG versus torque control of human-machine systems: equalizing control signal variability does not equalize error or uncertainty.IEEE Transactions on Neural Systems and Rehabilitation Engineering
[34] Johnson R, Sensinger J. (2014). Comparing functional EMG characteristics between zero-order and first order interface dynamics. IEEE Transactions on Neural Systems and Rehabilitation Engineering
[35] Johnson R, Kording K, Hargrove L, Sensinger J. (2014). Does EMG control lead to distinct motor adaptation? Frontiers in Neuroscience
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[37] Blustein D, *Wilson A, Sensinger J. (2018). Assessing the quality of supplementary sensory feedback using the crossmodal congruency task. Scientific Reports
[38] Shehata A, Scheme E, Sensinger J. (2018). Audible feedback improves internal model strength and performance of myoelectric prosthesis control. Scientific Reports
[39] Sensinger J, Dosen S. (2020). A review of sensory feedback in upper-limb prostheses from the perspective of human motor control. Frontiers in Neuroscience
[40] Marasco, P, Hebert, J, Sensinger, J, Beckler, D, Thumser, Z, Shehata, A, Williams, H, Wilson, K. (2021). Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors. Science Robotics
[41] Engels L, Shehata A, Scheme E, Sensinger J, Cipriani C. (2019). When less is more – discrete tactile feedback dominates continuous audio biofeedback in the integrated percept while controlling a myoelectric prosthetic hand. Frontiers in Neuroscience
[42] Early E, Johnson R, Hargrove L, Sensinger J. (2018). Joint speed discrimination and augmentation for prosthesis feedback
[43] Shehata A, Engels L, Controzzi M, Cipriani C, Scheme E, Sensinger J. (2018). Improving internal model strength and performance of prosthetic hands using augmented feedback. Journal of Neural Engineering and Rehabilitation.
[44] Marasco, Hebert, Sensinger, Shell, Schofield, Thumser, Nataraj, Beckler, Dawson, Blustein, *Gill, Mensh, Granja-Vazquez, Newcomb, Carey, Orzell. (2018). Illusory movement perception improves motor control for prosthetic hands. Science Translational Medicine