Tony Shu1, Shan Shan Huang2, Christopher Shallal3, Hugh M Herr4,5. 1. MIT Media Lab, 75 Amherst St, Cambridge, MA, 02139, USA. 2. Department of Mechanical Engineering, MIT, 77 Massachusetts Ave, Cambridge, MA, 02139, USA. 3. Department of Biomedical Engineering, Johns Hopkins University, 720 Rutland Ave, Baltimore, MD, 21205, USA. 4. MIT Media Lab, 75 Amherst St, Cambridge, MA, 02139, USA. hherr@media.mit.edu. 5. Physical Medicine and Rehabilitation, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. hherr@media.mit.edu.
Abstract
BACKGROUND: Neuroprosthetic devices controlled by persons with standard limb amputation often lack the dexterity of the physiological limb due to limitations of both the user's ability to output accurate control signals and the control system's ability to formulate dynamic trajectories from those signals. To restore full limb functionality to persons with amputation, it is necessary to first deduce and quantify the motor performance of the missing limbs, then meet these performance requirements through direct, volitional control of neuroprosthetic devices. METHODS: We develop a neuromuscular modeling and optimization paradigm for the agonist-antagonist myoneural interface, a novel tissue architecture and neural interface for the control of myoelectric prostheses, that enables it to generate virtual joint trajectories coordinated with an intact biological joint at full physiologically-relevant movement bandwidth. In this investigation, a baseline of performance is first established in a population of non-amputee control subjects ([Formula: see text]). Then, a neuromuscular modeling and optimization technique is advanced that allows unilateral AMI amputation subjects ([Formula: see text]) and standard amputation subjects ([Formula: see text]) to generate virtual subtalar prosthetic joint kinematics using measured surface electromyography (sEMG) signals generated by musculature within the affected leg residuum. RESULTS: Using their optimized neuromuscular subtalar models under blindfolded conditions with only proprioceptive feedback, AMI amputation subjects demonstrate bilateral subtalar coordination accuracy not significantly different from that of the non-amputee control group (Kolmogorov-Smirnov test, [Formula: see text]) while standard amputation subjects demonstrate significantly poorer performance (Kolmogorov-Smirnov test, [Formula: see text]). CONCLUSIONS: These results suggest that the absence of an intact biological joint does not necessarily remove the ability to produce neurophysical signals with sufficient information to reconstruct physiological movements. Further, the seamless manner in which virtual and intact biological joints are shown to coordinate reinforces the theory that desired movement trajectories are mentally formulated in an abstract task space which does not depend on physical limb configurations.
BACKGROUND: Neuroprosthetic devices controlled by persons with standard limb amputation often lack the dexterity of the physiological limb due to limitations of both the user's ability to output accurate control signals and the control system's ability to formulate dynamic trajectories from those signals. To restore full limb functionality to persons with amputation, it is necessary to first deduce and quantify the motor performance of the missing limbs, then meet these performance requirements through direct, volitional control of neuroprosthetic devices. METHODS: We develop a neuromuscular modeling and optimization paradigm for the agonist-antagonist myoneural interface, a novel tissue architecture and neural interface for the control of myoelectric prostheses, that enables it to generate virtual joint trajectories coordinated with an intact biological joint at full physiologically-relevant movement bandwidth. In this investigation, a baseline of performance is first established in a population of non-amputee control subjects ([Formula: see text]). Then, a neuromuscular modeling and optimization technique is advanced that allows unilateral AMI amputation subjects ([Formula: see text]) and standard amputation subjects ([Formula: see text]) to generate virtual subtalar prosthetic joint kinematics using measured surface electromyography (sEMG) signals generated by musculature within the affected leg residuum. RESULTS: Using their optimized neuromuscular subtalar models under blindfolded conditions with only proprioceptive feedback, AMI amputation subjects demonstrate bilateral subtalar coordination accuracy not significantly different from that of the non-amputee control group (Kolmogorov-Smirnov test, [Formula: see text]) while standard amputation subjects demonstrate significantly poorer performance (Kolmogorov-Smirnov test, [Formula: see text]). CONCLUSIONS: These results suggest that the absence of an intact biological joint does not necessarily remove the ability to produce neurophysical signals with sufficient information to reconstruct physiological movements. Further, the seamless manner in which virtual and intact biological joints are shown to coordinate reinforces the theory that desired movement trajectories are mentally formulated in an abstract task space which does not depend on physical limb configurations.
Entities:
Keywords:
Agonist-antagonist myoneural interface; Myoelectric prosthesis; Neural control of movement; Neuromuscular modeling
Authors: Suzanne Wendelken; David M Page; Tyler Davis; Heather A C Wark; David T Kluger; Christopher Duncan; David J Warren; Douglas T Hutchinson; Gregory A Clark Journal: J Neuroeng Rehabil Date: 2017-11-25 Impact factor: 4.262