Publications

Electrocardiogram (ECG) has attracted intense research interests and contests due to its great value in biometric applications, especially with the technological progress of smart instrumentation and artificial intelligence nowadays. For the issue of ECG-based human identification, distance measure in a suitable data space can be an effective solution. Almost all conventional distance measures rely on the independent data samples for classification. However, such measuring mechanism is vulnerable to the variation and bias of data distribution, which are easily caused by the noisy artifacts in this issue. To tackle this problem, we suggest doing distance measurement at the level of multiple-set bundle that consists of multiple sample sets obtained under different conditions. More specifically, we propose a novel method, “Collaborative-Set Measurement (CSM),” which creatively extends distance measure from the sample level through the set level to the bundle level. CSM not only captures the representative information from within-set distribution but also exploits the discriminative information from between-set collaboration, and meanwhile incorporates the robust information from in-bundle fusion. Attributing to these information, this method can largely enhance the identification performance in this issue despite the serious noisy artifacts influencing the data samples. The experimental results have demonstrated the capability of CSM for ECG-based human identification, by means of the public challenging database, DREAMER, on which the proposed method produces an accuracy of 91.30%. In summary, this proposal has initially brought forward a new thought of “collaborative sets.” This thought may provide inspirations for further researches on biometric recognition and general signal classification topics.

The Kalman Filter has long been one of the most widely used models in motor brain-machine interface (BMI) research due to its noise handling capabilities and real-time adaptability. However, as a model originally developed for traditional control systems, its underlying assumptions of Markov property and the designs of observation models may not always hold true in the context of BMI applications, potentially leading to oversimplifications. This paper examines the limitations that arise when applying the Kalman Filter to BMI, and proposes the Dilated Kalman Filter, which performs Gaussian multiplication between state transition distribution and observation-mapped state distribution in state space, thereby combining observation noise with BMI-specific observation model noise, and consequently incorporates historical information from both states and observations. The proposed method improves the accuracy of Kalman Filter while significantly enhancing computational efficiency, particularly when processing data from large numbers of neurons.

It is suggested that in a force telepresence teleoperation system based on virtual reality technology, two kinds of dynamic information transmission patterns of human operator, namely force pattern information transmission and movement pattern information transmission, are realized in the process where the human operator experiences, transmissions and executes the information. Dynamic model of the human operator is established, and the model is divided into three stages, namely fusion, neural muscle and execution. The methods of ascertaining the model parameters are given, and the experiment research is done. Important foundation is provided by the discussion in the paper for the analysis, design and control of the system.

Capturing walking trajectories is useful for motion planning and various location-based services. Traditionally, it is a challenging task because it is expensive to install infrastructures, especially in an outdoor setting. As another choice, the reconstructed walking trajectories suffer from the drifting problem from captured inertia data. In this work, we study the biped walking motion and propose a method to recover walking trajectories by introducing local measurements between the feet to the system, in combination with the orientation from inertia data. We design a few local measurements which can be passively captured. After analyzing these measurements, the walking trajectory is progressively recovered by solving a set of small-scale optimization problems. By comparing with the trajectories extracted from optical motion capture systems, we tested our method on different subjects, and the quality of the recovered walking trajectory is evaluated.

Stroke patients often experience hand dysfunction, requiring effective assistance. While cable-driven wearable robots have advanced, challenges persist in anchor ring design and actuation system. Specifically, a trade-off exists between material comfort and anchor point stability, as well as between actuator versatility and control simplicity. To address these challenges, this article proposes an adjustable rigid-soft wearable hand robot with a magnetorheological cable-driven mechanism. The adjustable rigid-soft coupling ring enhances both comfort and stability, demonstrating significantly improved mechanical performance compared to both the rigid-soft ring and the silicone ring of the same size. The wearable structure integrates the proposed adjustable anchor ring and a ripple strap design for better adaptability. Additionally, the magnetorheological actuation system balances versatility and control simplicity, enabling coordinated motion, adaptive grasping, independent finger control, and mechanical safety by selectively activating specific magnetorheological fluid. Finally, the proposed system's performance and effectiveness in assisting grasping have been confirmed through experiments.

It is challenging to model and control rigid and flexible structures coupled systems (RFSCS). This article presents an automated robot for drum-to-drum cable winding in industrial scenes. The cable winding process encounters unprecedented disturbances that induce tension vibrations and winding errors. We seek to stabilize cable tension with an underactuated serial manipulator, which forms an RFSCS with the cable and drum. The impedance control model further improves the tension response. The study explores the modeling, control, and stability analysis of the RFSCS. Additionally, the machine vision module is integrated to provide essential measurements and involve in coil forming control. The techniques of vision-encoder fusion and drum pose compensation improve the measurement accuracy. The vision-aided drum walking controller switches between two control modes for different winding states. The static tests in both simulation and experiment evaluated the merits of RFSCS against disturbances. The prototype can wind ordered coils from materials of varying sizes and stiffnesses. Our solution promotes the application of RFSCS and visual control systems in harsh industrial environments.