Multimodal fusion and human-robot interaction control of an intelligent robot

Introduction: Small-scaled robotic walkers play an increasingly important role in Activity of Daily Living (ADL) assistance in the face of ever-increasing rehab requirements and existing equipment drawbacks. This paper proposes a Rehabilitation Robotic Walker (RRW) for walking assistance and body weight support (BWS) during gait rehabilitation. Methods: The walker provides the patients with weight offloading and guiding force to mimic a series of the physiotherapist's (PT's) movements, and creates a natural, comfortable, and safe environment. This system consists of an omnidirectional mobile platform, a BWS mechanism, and a pelvic brace to smooth the motions of the pelvis. To recognize the human intentions, four force sensors, two joysticks, and one depth-sensing camera were used to monitor the human-machine information, and a multimodal fusion algorithm for intention recognition was proposed to improve the accuracy. Then the system obtained the heading angle E, the pelvic pose F, and the motion vector H via the camera, the force sensors, and the joysticks respectively, classified the intentions with feature extraction and information fusion, and finally outputted the motor speed control through the robot's kinematics. Results: To validate the validity of the algorithm above, a preliminary test with three volunteers was conducted to study the motion control. The results showed that the average error of the integral square error (ISE) was 2.90 and the minimum error was 1.96. Discussion: The results demonstrated the efficiency of the proposed method, and that the system is capable of providing walking assistance.

A novel integrated quasi-zero stiffness vibration isolator for coupled translational and rotational vibrations

Quasi-zero stiffness (QZS) vibration isolators can provide better isolation performance in the low frequency range than linear vibration isolators. Currently, most of the designed QZS isolators perform vibration isolation only in one direction and few papers are focused on simultaneously isolating the vibrations in two directions. In this paper, an integrated translational-rotational QZS vibration isolator is designed by using the cam-roller mechanism. The proposed QZS system is able to provide the high-static-low-dynamic stiffness in two directions simultaneously. The excitations in both translational and rotational directions are considered independent but with mutual interaction to their induced vibration response. The workable ranges of the QZS system and its limitations are first numerically identified. Then the static characteristics and typical nonlinear dynamic response with jump phenomena are theoretically investigated. The jump-down frequencies for small amplitude oscillations are determined from their amplitude-frequency relationships. Furthermore, the force transmissibility and moment transmissibility of the proposed QZS system are compared with those of the corresponding linear system without the cam-roller mechanism, which clearly demonstrate better isolation performance in both translational and rotational directions.

An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures

The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE). Secondly, in order to improve the performance of differential evolution algorithm for feature selection, a new variant of binary DE called BDE is proposed. Three optimization strategies are incorporated into the BDE. The first strategy is the heuristic method in initial stage, the second one is the self-adaptive parameter control, and the third one is the minimum change value to improve the exploration behaviour thus enhance the diversity. Finally, Support vector machine (SVM) is used as the classifier in 10 fold cross-validation method. The experimental results of our proposed algorithm on some benchmark datasets demonstrate the effectiveness of our algorithm. In addition, the BDE forged in this study will be of great potential in feature selection problems.