Special purpose vehicles for sustainable finance of innovation in Romania - the case of intelligent robotic systems

The intelligent robotic systems IRS integrates artificial intelligence (AI) and intelligent autonomous control based on special features like: decision making, multispectral sensorial information, advanced monitoring and execution capabilities. There is an impressive rate of success of IRS projects that benefits from the Industry 4.0. The design of strategies for IRS financing is very important for Romania, because the governmental funding is far from the critical mass and the access to capital market is insufficient. In this contribution the interest is to design special purpose vehicles (SPV) for sustainable finance of innovation in the case of IRS, adapted to Romanian emerging market, like venture capital financing (VCF) or thematic exchanged traded funds (ETF)

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients’ participation in training. By establishing the static equation of the mechanical leg, the man–machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers’ motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm