Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by the internal model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the internal model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief.

Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person's response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person's perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person's learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.

Robotic prostheses can improve walking ability in persons with transfemoral amputations by closely matching kinetics and kinematics of the intact leg during walking. However, achieving this goal requires the prosthesis to adapt to walking speed, a function that no powered transfemoral prosthesis has yet achieved. In this paper, we present, and perform initial testing on a new control framework that allows biologically accurate leg function at varying walking speeds, without the need for tuning. The proposed framework comprises two novel controllers that rely on quasi-stiffness modulation in stance phase and minimum jerk trajectory in swing phase. Preliminary testing was conducted in an able-bodied subject using a bypass adapter to walk on a robotic prosthesis at five different walking speeds (from 0.62 to 1.16 m/s). Experimental results demonstrated the ability of the proposed controller to approximate intact leg function at different walking speeds.

Recent powered (or robotic) prosthetic legs independently control different joints and time periods of the gait cycle, resulting in control parameters and switching rules that can be difficult to tune by clinicians. This challenge might be addressed by a unifying control model used by recent bipedal robots, in which virtual constraints define joint patterns as functions of a monotonic variable that continuously represents the gait cycle phase. In the first application of virtual constraints to amputee locomotion, this paper derives exact and approximate control laws for a partial feedback linearization to enforce virtual constraints on a prosthetic leg. We then encode a human-inspired invariance property called effective shape into virtual constraints for the stance period. After simulating the robustness of the partial feedback linearization to clinically meaningful conditions, we experimentally implement this control strategy on a powered transfemoral leg. We report the results of three amputee subjects walking overground and at variable cadences on a treadmill, demonstrating the clinical viability of this novel control approach.

Harmonic and cycloid drives are both compact, high ratio transmissions appropriate for use in anthropomorphic robots, although cycloid drives are rarely used in the field. This paper describes the design parameters for cycloid drives and shows the results of six cycloid models designed to match corresponding harmonic drives. Cycloid drive models were compared with manufacturing data from corresponding harmonic drives with respect to maximum gear ratio, transmission thickness, efficiency, backlash/gear ratio ripple, and reflected inertia. Cycloid drive designs were found to be thinner, more efficient, and to have lower reflected inertia than corresponding harmonic drives. However, the cycloid designs had larger gear ratio ripple and substantial backlash, and they could not meet the maximum gear ratio provided by the corresponding harmonic drives in two out of six models for equal applied torques. Two cycloid drives were manufactured to confirm efficiency predictions and demonstrated moderate to high efficiency across a range of output torques. Cycloid drives should be considered for robotic and prosthetic applications where smaller thickness/higher efficiency requirements dominate over low backlash/gear ratio ripple considerations.

Many robotic actuators must generate high torques and accelerations. Exterior-rotor brushless motors seem better suited for these tasks than conventional interior rotor brushless motors. Models of these two motor types were used to calculate benchmarks and evaluate motor performance in simulated tasks for different motor geometries. Exterior rotor motors had higher motor constants and efficiency, but lower rated power compared with interior-rotor motors. They required less energy to perform typical robotic tasks. Four exterior-rotor motors were fabricated and compared with manufacturing data from the best existing brushless DC motors. The exterior-rotor motors had more than a 250% increase in stall torque density compared with the best known brushless DC motor (RoboDrive) and more than a 740% increase in stall torque density compared with more common motors such as those produced by MicroMo and Maxon. These exterior-rotor motors appear promising for robotic applications.

Robotic actuators must frequently be designed to provide both substantial torque and acceleration in a lightweight package. Their ballistic trajectories require new optimization guidelines, in contrast to constant-speed or low-torque conventional motor design. A framework was derived in this study based on trajectories that incorporated the effect of velocity on torque and acceleration. This framework suggested that speed rate (or mechanical motor constant) was the best benchmark of robotic motor performance. Implications of this framework were evaluated using simulations of 37 optimized motor models. Simulation results confirmed the framework predictions, suggesting that speed rate was a better predictor of motor success than either motor constant or rated power. Optimum winding/gear ratio, supply voltage, and implications for design choices such as continuously variable transmissions are discussed in light of these findings.