Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

The neural manifold in state space represents the mass neural dynamics of a biological system. A challenging modern approach treats the brain as a whole in terms of the interaction between the agent and the world. Therefore, we need to develop a method for this global neural workspace. The current study aimed to visualize spontaneous neural trajectories regardless of their measuring modalities (electroencephalography [EEG], functional magnetic resonance imaging [fMRI], and magnetoencephalography [MEG]). First, we examined the possible visualization of EEG manifolds. These results suggest that a spherical surface can be clearly observed within the spatial similarity space. Once valid (e.g., differentiable) and useful (e.g., low-dimensional) manifolds are obtained, the nature of the sphere, such as shape and size, becomes a possible target of interest. Because these should be practically useful, we suggest advantages of the EEG manifold (essentially continuous) or the state transition matrix (coarse-grained discrete). Finally, because our basic procedure is modality-independent, MEG and fMRI manifolds were also compared. These results strongly suggest the need to update our understanding of neural mass representations to include robust "global" dynamics.

Previous works in the literature have claimed that the characteristics of electromyography (EMG) signals depend on each person, and thus, EMG interfaces need to be carefully calibrated for each user in myoelectric control. In this study, we show that the EMG interface used to estimate the joint torques of a user can be constructed simply by incorporating other users' data without typical calibration process. To achieve this plug-and-play capability, we introduce the concept of collaborative filtering to estimate the joint torque of a novel user by exploiting the preidentified relationships between motion-body features, including EMG signals, and the joint torques of other users. To validate our proposed approach, we compare the performance of estimating joint torque by the proposed method with that by conventional linear regression models as a baseline. We considered the following two baseline methods. Linear-own: The parameters of the linear model are calibrated for each subject from his/her own training data. Linear-others: The parameters of the linear model are calibrated with the other users' data in which the novel user's data are not included. As a result, the estimated joint torques from our proposed approach reveal a better estimation performance than those from the baseline approaches. Furthermore, we also successfully demonstrate online myoelectric control of an upper limb exoskeleton robot with an attached mannequin arm.