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This paper presents a model inversion procedure for a viscoelastic compliant element contained within a rotary series elastic actuator (SEA). Model inversion plays an important role in enabling accurate model-based control of physical human-robot interaction (HRI) with SEAs. If the compliant element of the SEA is elastomeric, analytically inverting its model is non-trivial due to the presence of complex non-linear terms. This paper applies an alternative inversion procedure, by coupling a partially analytical inverse model with a disturbance observer (DOB). Results of inverting without a DOB are given as a baseline and compared to the analytical inversion + DOB with two different types of filter. To quantify the accuracy of the inversion, the output of the inversion procedure is passed back through the forward model to identify the 'actual setpoint', the signal that would be generated if the output of the inversion procedure was tracked accurately. The inversions for five desired torque signals are presented; in all cases, the root-mean square (RMS) error between the desired setpoint and actual setpoint is lower when the DOB inversion procedure is used, compared to the RMS error incurred when the DOB is omitted. The results suggest that the proposed inversion procedure provides an accurate and mathematically tractable inverse of the complex viscoelastic elastomer model.

Clinical assessment of abnormal neuromechanics is typically performed by manipulation of the affected limbs; a process with low inter- and intra-rater reliability. This paper aims at formalizing a framework that closes the loop between a clinician's expertise and computational algorithms, to enhance the clinician's diagnostic capabilities during physical manipulation. The framework's premise is that the dynamics that can be measured by manipulation of a limb are distinct between movement disorders. An a priori database contains measurements encoded in a space called the information map. Based on this map, a computational algorithm identifies which probing motions are more likely to yield distinguishing information about a patient's movement disorder. The clinician executes this movement and the resulting dynamics, combined with clinician input, is used by the algorithm to estimate which of the movement disorders in the database are most probable. This is recursively repeated until a diagnosis can be confidently made. The main contributions of this paper are the formalization of the framework and the addition of the information map to select informative movements. The establishment of the framework provides a foundation for a standardized assessment of movement disorders and future work will aim at testing the framework's efficacy.

Physical human-robot interaction (pHRI) is an important consideration in the design of rehabilitation exoskeletons. Series Elastic Actuators (SEAs) are seen as a promising method of introducing compliance and comfort to robotic exoskeletons. Recently, authors have proposed the use of elastomers in SEAs, rather than springs, but there is a dearth of literature comparing how the two compliant elements are perceived by humans. In this paper, we quantitatively and qualitatively compare human interaction with an elastomer and a spring in terms of comfort and motion smoothness, with the aim of informing future SEA design. Two mechanisms were designed to compare human interaction with a spring and elastomer, and a cross-over experimental design was used to help eliminate any learning effects. Overall, the elastomer appeared to be slightly more comfortable and allow slightly smoother motion than the spring, holding promise for the use of elastomers as a means of improving pHRI in exoskeletons actuated by SEAs.

Motor learning is a critical component of the rehabilitation process; however, it can be difficult to separate the fundamental causes of a learning deficit when physical impairment is a confounding factor. In this paper, a new technique is proposed to augment the residual ability of physically impaired patients with a robotic rehabilitation exoskeleton, such that motor learning can be studied independently of physical impairment. The proposed technique augments the velocity of an on-screen cursor relative to the restricted physical motion. Radial Basis Functions (RBFs) are used to both model velocity and derive a function to scale velocity as a function of workspace position. Two variations of the algorithm are presented for comparison. In a cross-over pilot study, healthy participants were recruited and subjected to a simulated impairment to constrain their motion, imposed by the cable-driven wrist exoskeleton. Participants then completed a sinusoidal tracking task, in which the algorithms were statistically shown to augment the cursor velocity in the constrained state such that it matched position-dependent velocities recorded in the healthy state. A kinematic task was then designed as a motor-learning case study where the algorithms were statistically shown to allow participants to achieve the same performance when their motion was constrained as when unconstrained. The results of the pilot study provide motivation for further research into the use of this technique, thus providing a tool with which motor-learning can be studied in neurologically impaired populations. This could be used to give physiotherapists greater insight into underlying causes of motor learning deficits, consequently facilitating and enhancing subject-specific therapy regimes.