Robotic assessment of neuromuscular characteristics using musculoskeletal models: A pilot study. (1st July 2017)

• Record Type: Journal Article
• Authors: Jayaneththi, V.R.; Viloria, J.; Wiedemann, L.G.; Jarrett, C.; McDaid, A.J.
• Is Part Of: Computers in biology and medicine. Volume 86(2017)
• Page Start: 82
• Page End: 89
• Publication Date: 2017-07-01
• Subjects: Electromyography (EMG) -- HD-sEMG -- Musculoskeletal modeling -- Neuromuscular characterization -- Robotic rehabilitation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.05.007
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: Non-invasive neuromuscular characterization aims to provide greater insight into the effectiveness of existing and emerging rehabilitation therapies by quantifying neuromuscular characteristics relating to force production, muscle viscoelasticity and voluntary neural activation. In this paper, we propose a novel approach to evaluate neuromuscular characteristics, such as muscle fiber stiffness and viscosity, by combining robotic and HD-sEMG measurements with computational musculoskeletal modeling. This pilot study investigates the efficacy of this approach on a healthy population and provides new insight on potential limitations of conventional musculoskeletal models for this application. Methods: Subject-specific neuromuscular characteristics of the biceps and triceps brachii were evaluated using robot-measured kinetics, kinematics and EMG activity as inputs to a musculoskeletal model. Results: Repeatability experiments in five participants revealed large variability within each subjects evaluated characteristics, with almost all experiencing variation greater than 50% of full scale when repeating the same task. Conclusion: The use of robotics and HD-sEMG, in conjunction with musculoskeletal modeling, to quantify neuromuscular characteristics has been explored. Despite the ability to predict joint kinematics with relatively high accuracy, parameter characterization was inconsistent i.e. many parameter combinations gave rise to minimal kinematic error. Significance: The proposed technique is a novel approach for in vivo neuromuscular characterization and is a step towards the realization of objective in-home robot-assisted rehabilitation. Importantly, the results have confirmed the technical (robot and HD-sEMG) feasibility while highlighting the need to develop new musculoskeletal models and optimization techniques capable of achieving consistent results across a range of dynamic tasks. Highlights: A novel approach for robotic assessment of neuromuscular characteristics is proposed. The integration of the robotic exoskeleton with HD-sEMG was validated. High assessment variation is present even within trials for the same movement task. Many neuromuscular parameter combinations can produce a minimal kinematic error.