Customizing Robot-Assisted Passive Neurorehabilitation Exercise Based on Teaching Training Mechanism

Passive movement is an important mean of rehabilitation for stroke survivors in the early stage or with greater paralysis. The upper extremity robot is required to assist therapists with passive movement during clinical rehabilitation, while customizing is one of the crucial issues for robot-assisted upper extremity training, which fits the patient-centeredness. Robot-assisted teaching training could address the need well. However, the existing control strategies of teaching training are usually commanded by position merely, having trouble to achieve the efficacy of treatment by therapists. And deficiency of flexibility and compliance comes to the training trajectory. This research presents a novel motion control strategy for customized robot-assisted passive neurorehabilitation. The teaching training mechanism is developed to coordinate the movement of the shoulder and elbow, ensuring the training trajectory correspondence with human kinematics. Furthermore, the motion trajectory is adjusted by arm strength to realize dexterity and flexibility. Meanwhile, the torque sensor employed in the human-robot interactive system identifies movement intention of human. The goal-directed games and feedbacks promote the motor positivity of stroke survivors. In addition, functional experiments and clinical experiments are investigated with a healthy adult and five recruited stroke survivors, respectively. The experimental results present that the suggested control strategy not only serves with safety training but also presents rehabilitation efficacy.

Memory-Replay Knowledge Distillation

Knowledge Distillation (KD), which transfers the knowledge from a teacher to a student network by penalizing their Kullback–Leibler (KL) divergence, is a widely used tool for Deep Neural Network (DNN) compression in intelligent sensor systems. Traditional KD uses pre-trained teacher, while self-KD distills its own knowledge to achieve better performance. The role of the teacher in self-KD is usually played by multi-branch peers or the identical sample with different augmentation. However, the mentioned self-KD methods above have their limitation for widespread use. The former needs to redesign the DNN for different tasks, and the latter relies on the effectiveness of the augmentation method. To avoid the limitation above, we propose a new self-KD method, Memory-replay Knowledge Distillation (MrKD), that uses the historical models as teachers. Firstly, we propose a novel self-KD training method that penalizes the KD loss between the current model’s output distributions and its backup outputs on the training trajectory. This strategy can regularize the model with its historical output distribution space to stabilize the learning. Secondly, a simple Fully Connected Network (FCN) is applied to ensemble the historical teacher’s output for a better guidance. Finally, to ensure the teacher outputs offer the right class as ground truth, we correct the teacher logit output by the Knowledge Adjustment (KA) method. Experiments on the image (dataset CIFAR-100, CIFAR-10, and CINIC-10) and audio (dataset DCASE) classification tasks show that MrKD improves single model training and working efficiently across different datasets. In contrast to the existing fancy self-KD methods with various external knowledge, the effectiveness of MrKD sheds light on the usually abandoned historical models during the training trajectory.

Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.

2. Choosing the Teaching Career by Students in the Arts

In order to build strategies for the permanent improvement of the educational offer in higher education institutions, taking into account the expectations of the beneficiaries can be an important direction to consider. Thus, the present study aims to radiograph the reasons why the students of the National University of Arts “George Enescu” in Iași (UNAGE) also choose a training trajectory for the route of the teaching career. To achieve this objective, we used in our research the linguistically adapted questionnaire “Orientations for Teaching Survey - OTS” (Ferrell, C.M., Daniel, L.G, 1993), which was answered by 140 UNAGE students. The hypothesis that the reasons why these students prepare to become teachers are intrinsic, extrinsic and altruistic was confirmed by statistical analyzes.

Vocational Employer-Sponsored Education of Employees of the Water Supply and Sewage

The provision of a complex development of an enterprise, the implementation of innovative projects, the introduction of the efficient management systems requires the assistance of the corresponding staff. The rates of engineering and technological development stipulate the demand for constant increase of the level of professional skills of the staff. The requirements to the systems of training and improvement in specialists’ skill are changing as well. Nowadays traditional requirements to the level of pedagogical staff and technical equipment are supplemented with the requirements of versatility, the ability to respond to technical innovations and socio-economic changes promptly, to foresee a possibility of individualization of the training trajectory according to the professional requirements of an employer. The experience of O.M. Beketov National University of Urban Economy in Kharkiv in cooperation with the public utility company “Kharkivvodokanal” on implementation of the project of industry-specific employer-sponsored training of an enterprise staff aimed at increasing the level of their theoretic and practical training has been described in the article.