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

Abstract

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.

Advanced Intelligent Control through Versatile Intelligent Portable Platforms

“…An alternative to the process of rehabilitation training for stroke patients, compared to most of the methods, which process EMG signals or oxygen consumption for patients’ participation measurements, uses high cost and high complexity robotic devices, a multi-sensor system robot with torque and six-dimensional force sensors integrated in advanced intelligent control, applying the support vector machines [ 4 ]. The support vector classifiers and regression machines were used to predict the degree of the patient’s task participation, taking into account the small sample and non-linear data of the patients’ training and questionnaire data.…”

Design and Analysis of an Intelligent Toilet Wheelchair Based on Planar 2DOF Parallel Mechanism with Coupling Branch Chains

“…Before and after going to the toilet, there is also the problem of transferring a patient from a bed to the toilet. However, due to the need for privacy during the toilet process, the presence of nursing staff during this process will create a great psychological burden for patients [ 7 ]. Therefore, it is of social value and practical significance to develop a safe and stable wheelchair that can enable patients to complete the toilet process independently.…”

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

“…In terms of model optimization for robotic applications, the studies reveal different approaches such as developing advanced control systems for the upper and lower limb [19,20], applying Dezert-Smarandache Theory (DSmT) for decision-making algorithms [21], Extenics control [22], and fuzzy dynamic modelling [23].…”