No abstract

The motor system has the flexibility to update motor plans according to systematic changes in the environment or the body. This capacity is studied in the laboratory through sensorimotor adaptation paradigms imposing sustained and predictable motor demands specific to the task at hand. However, these studies are tied to the laboratory setting. Thus, we asked if a portable device could be used to elicit locomotor adaptation outside the laboratory. To this end, we tested the extent to which a pair of motorized shoes could induce similar locomotor adaptation to split-belt walking, which is a well-established sensorimotor adaptation paradigm in locomotion. We specifically compared the adaptation effects (i.e. after-effects) between two groups of young, healthy participants walking with the legs moving at different speeds by either a split-belt treadmill or a pair of motorized shoes. The speeds at which the legs moved in the splitbelt group was set by the belt speed under each foot, whereas in the motorized shoes group were set by the combined effect of the actuated shoes and the belts' moving at the same speed. We found that the adaptation of joint motions and measures of spatial and temporal asymmetry, which are commonly used to quantify sensorimotor adaptation in locomotion, were indistinguishable between groups. We only found small differences in the joint angle kinematics during baseline walking between the groupspotentially due to the weight and height of the motorized shoes. Our results indicate that robust sensorimotor adaptation in walking can be induced with a paired of motorized shoes, opening the exciting possibility to study sensorimotor adaptation during more realistic situations outside the laboratory.

Based on the assumption of the local spherical symmetric ionosphere, we derive two inversion methods with which ionospheric density profiles are extracted from the dual frequencies and single frequency radio occultation data. The estimation errors of the dual‐frequencies method come from errors of the carrier phase measurements, while the errors of the single frequency method are mainly determined by the precision of the pseudo‐range measurements. Since the pseudo‐range measurements tend to be about 100 times noisier than those of the carrier phase, the retrieval precision with the dual‐frequencies method may be generally higher than that with the single frequency. However, slightly different paths of carrier L1 and L2 will bring errors to estimations of the dual‐frequencies method. The radio occultation simulation results with a three‐dimensional ray‐tracing program are used to evaluate these inversion methods. Results show that the inversion profiles agree very well with the given model ionosphere, which suggests reliabilities and accuracies of both methods. Then, two inversion methods are applied to the processing of GPS/MET data for ionospheric occultation. The inversion results indicate reasonable ionospheric profiles. Inversion profiles by the single‐frequency method are consistent with those by the dual‐frequencies method. It provides the theoretical inversion base for ionosphere radio occultation observations with a single frequency GPS receiver onboard LEO.

[1] Artificial neural network (ANN) is used for assimilating of GPS ionospheric occulted data in order to take full advantage of the abundant GPS occulted data. A feedforward, full-connected network is chosen based on the back-propagation algorithm. Universal time, latitude, longitude, height, Kp index, and F 10.7 solar flux are chosen as the input vectors of the network while the electron density as the output vectors. The GPS occultation data on May 24th, 1996 were taken as training samples to train an ANN, and then the well-trained ANN was used to predict the electron density on 25th. Comparison of the predicted results and observed data demonstrated that ANN is a promising method in assimilating the GPS occulted data to establish the ionospheric weather prediction model. Furthermore, the accurate and abundant observations are essential for ensuring the good performance of ANN.