Quantifying Passive Biomechanical Stability Using an Industrial Robot: Development and Experimental Validation of a Task Space Motion Framework

This paper presents a methodology and generalized motion framework for quantifying passive biomechanical stability and Range of Motion of human cadaveric specimens, using a position-controlled industrial robot and a wrist-mounted force/torque sensor. Many biomechanical studies on diarthrodial joints using human cadaveric specimens are published in the literature, using various test protocols and machines to apply the loading conditions. In these studies, laxity or mobility of the joints are quantified by measuring the magnitude of translations and rotations with respect to force and torque. The protocols and anatomical motions of the specimens are usually described high-level, textually, and from a medical perspective to a broad audience. The present paper aims to describe, from a technical perspective to a robotics audience, our method to perform biomechanical studies and how existing protocols can be replicated through parameterization using the existing textual descriptions. To accomplish this, we propose a generalized task space motion framework for performing biomechanical studies on diarthrodial joints. The generalization is made by defining the robot Tool Center Point at the cadaveric joint rotation center and aligning the specimen so the anatomical motions can be modeled in world frame or tool frame. The framework was successfully evaluated in a technical pilot study on the shoulder, using one cadaveric shoulder specimen and an established protocol from the literature. The specimen was tested in the intact state and in an injury state, with increased passive instability observed for the injury state compared to intact state.

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State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat’s primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.

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