Abstract-Training coordinated hand and wrist movement is invaluable during post-neurological injury due to the anatomical, biomechanical, and functional couplings of these joints. This paper presents a novel rehabilitation device for coordinated hand and wrist movement. As a first step towards validating the new device as a measurement tool, the device transparency was assessed through kinematic analysis of a redundant finger pointing task requiring synergistic movement of the wrist and finger joints. The preliminary results of this new methodology showed that wearing the robot affects the kinematic coupling of the wrist and finger for unconstrained pointing tasks. However, further experiments specifying a subset of the solution manifold did not exhibit the same difference between robot and no robot trials. The experiments and analysis form a promising method for the characterization of multi-articular wearable robots as measurement tools in robotic rehabilitation.
We present an exoskeleton capable of assisting the human thumb through a large range of motion. Our novel thumb exoskeleton has the following unique features: (i) an underlying kinematic mechanism that is optimized to achieve a large range of motion, (ii) a design that actuates four degrees of freedom of the thumb, and (iii) a series elastic actuation based on a Bowden cable, allowing for bidirectional torque control of each thumb joint individually. We present a kinematic model of the coupled thumb exoskeleton system and use it to maximize the range of motion of the thumb. Finally, we carry out tests with the designed device on four subjects to evaluate its workspace and kinematic transparency using a motion capture system and torque control performance. Results show that the device allows for a large workspace with the thumb, is kinematically transparent to natural thumb motion to a high degree, and is capable of accurate torque control.
The field of rehabilitation robotics has emerged to address the growing desire to improve therapy modalities after neurological disorders, such as a stroke. For rehabilitation robots to be successful as clinical devices, a number of mechanical design challenges must be addressed, including ergonomic interactions, weight and size minimization, and cost–time optimization. We present additive manufacturing (AM) as a compelling solution to these challenges by demonstrating how the integration of AM into the development process of a hand exoskeleton leads to critical design improvements and substantially reduces prototyping cost and time.
Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (rrm=−0.86) and ratings of perceived exertion (RPE) (rrm=0.87), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.
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