Activity

Finally, a powerful fusion predictor is obtained for target recognition.

The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA).

The proposed method has great potential for developing high-speed BCIs.

The proposed method has great potential for developing high-speed BCIs.Previous work has shown that it is possible to use a mechanical phase variable to accurately quantify the progression through a human gait cycle, even in the presence of disturbances. However, mechanical phase variables are highly dependent on the behavior of the body segment from which they are measured, which can change with the human’s task or in response to different disturbances. In this study, we compare kinematic parameterization methods based on time, thigh phase angle, and tibia phase angle with motion capture data obtained from ten able-bodied subjects walking at three inclines while experiencing phase-shifting perturbations from a split-belt instrumented treadmill. The belt, direction, and timings of perturbations were quasi-randomly selected to prevent anticipatory action by the subjects and sample different types of perturbations. Statistical analysis revealed that both phase parameterization methods are superior to time parameterization, with thigh phase angle also being superior to tibia phase angle in most cases.Human postural control requires continuous modulation of ankle torque to stabilize the upright stance. The torque is generated by two components active contributions, due to central control and stretch reflex, and passive mechanisms, due to joint intrinsic stiffness. compound 3k concentration Identifying the contribution of each component is difficult, since their effects appear together, and standing is controlled in closed-loop. This article presents a novel multiple-input, single-output method to identify central and stretch reflex contributions to human postural control. The model uses ankle muscle EMGs as inputs and requires no kinematic data. Application of the method to data from nine subjects during standing while subjected to perturbations of ankle position demonstrated that active torque accounted for 84.0± 5.5% of the ankle torque. The ankle plantar-flexors collectively produced the largest portion of the active torque through central control, with large inter-subject variability in the relative contributions of the individual muscles. In addition, reflex contribution of the plantar-flexors was substantial in half of the subjects, showing its potentially important functional role; finally, intrinsic contributions, estimated as the residual of the model, contributed to 15% of the torque. This study introduces a new method to quantify the contributions of the central and stretch reflex pathways to postural control; the method also provides an estimate of noisy intrinsic torque with significantly increased signal to noise ratio, suitable for identification of intrinsic stiffness in standing. The method can be used in different experimental conditions and requires minimal a-priori assumption regarding the role of different pathways in postural control.In Virtual Reality, having a virtual body opens a wide range of possibilities as the participant’s avatar can appear to be quite different from oneself for the sake of the targeted application (e.g. for perspective-taking). In addition, the system can partially manipulate the displayed avatar movement through some distortion to make the overall experience more enjoyable and effective (e.g. training, exercising, rehabilitation). Despite its potential, an excessive distortion may become noticeable and break the feeling of being embodied into the avatar. Past researches have shown that individuals have a relatively high tolerance to movement distortions and a great variability of individual sensitivities to distortions. In this paper, we propose a method taking advantage of Reinforcement Learning (RL) to efficiently identify the magnitude of the maximum distortion that does not get noticed by an individual (further noted the detection threshold). We show through a controlled experiment with subjects that the RL method finds a more robust detection threshold compared to the adaptive staircase method, i.e. it is more able to prevent subjects from detecting the distortion when its amplitude is equal or below the threshold. Finally, the associated majority voting system makes the RL method able to handle more noise within the forced choices input than adaptive staircase. This last feature is essential for future use with physiological signals as these latter are even more susceptible to noise. It would then allow to calibrate embodiment individually to increase the effectiveness of the proposed interactions.To convey neural network architectures in publications, appropriate visualizations are of great importance. While most current deep learning papers contain such visualizations, these are usually handcrafted just before publication, which results in a lack of a common visual grammar, significant time investment, errors, and ambiguities. Current automatic network visualization tools focus on debugging the network itself and are not ideal for generating publication visualizations. Therefore, we present an approach to automate this process by translating network architectures specified in Keras into visualizations that can directly be embedded into any publication. To do so, we propose a visual grammar for convolutional neural networks (CNNs), which has been derived from an analysis of such figures extracted from all ICCV and CVPR papers published between 2013 and 2019. The proposed grammar incorporates visual encoding, network layout, layer aggregation, and legend generation. We have further realized our approach in an online system available to the community, which we have evaluated through expert feedback, and a quantitative study. It not only reduces the time needed to generate network visualizations for publications, but also enables a unified and unambiguous visualization design.