D-Optimality-Guided Reinforcement Learning for Efficient Open-Loop Calibration of a 3-DOF Ankle Rehabilitation Robot

Accurate alignment of multi-degree-of-freedom rehabilitation robots is essential for safe and effective patient training. This paper proposes a two-stage calibration framework for a self-designed three-degree-of-freedom (3-DOF) ankle rehabilitation robot. First, a Kronecker-product-based open-loop calibration method is developed to cast the input-output alignment into a linear parameter identification problem, which in turn defines the associated experimental design objective through the resulting information matrix. Building on this formulation, calibration posture selection is posed as a combinatorial design-of-experiments problem guided by a D-optimality criterion, i.e., selecting a small subset of postures that maximises the determinant of the information matrix. To enable practical selection under constraints, a Proximal Policy Optimization (PPO) agent is trained in simulation to choose 4 informative postures from a candidate set of 50. Across simulation and real-robot evaluations, the learned policy consistently yields substantially more informative posture combinations than random selection: the mean determinant of the information matrix achieved by PPO is reported to be more than two orders of magnitude higher with reduced variance. In addition, real-world results indicate that a parameter vector identified from only four D-optimality-guided postures provides stronger cross-episode prediction consistency than estimates obtained from a larger but unstructured set of 50 postures. The proposed framework therefore improves calibration efficiency while maintaining robust parameter estimation, offering practical guidance for high-precision alignment of multi-DOF rehabilitation robots.

Human Machine Co-Adaptation Model and Its Convergence Analysis

The key to robot-assisted rehabilitation lies in the design of the human-machine interface, which must accommodate the needs of both patients and machines. Current interface designs primarily focus on machine control algorithms, often requiring patients to spend considerable time adapting. In this paper, we introduce a novel approach based on the Cooperative Adaptive Markov Decision Process (CAMDPs) model to address the fundamental aspects of the interactive learning process, offering theoretical insights and practical guidance. We establish sufficient conditions for the convergence of CAMDPs and ensure the uniqueness of Nash equilibrium points. Leveraging these conditions, we guarantee the system's convergence to a unique Nash equilibrium point. Furthermore, we explore scenarios with multiple Nash equilibrium points, devising strategies to adjust both Value Evaluation and Policy Improvement algorithms to enhance the likelihood of converging to the global minimal Nash equilibrium point. Through numerical experiments, we illustrate the effectiveness of the proposed conditions and algorithms, demonstrating their applicability and robustness in practical settings. The proposed conditions for convergence and the identification of a unique optimal Nash equilibrium contribute to the development of more effective adaptive systems for human users in robot-assisted rehabilitation.

A Field Calibration Approach for Triaxial MEMS Gyroscopes Based on Gravity and Rotation Consistency

This paper developed an efficient method for calibrating triaxial MEMS gyroscopes, which can be effectively utilized in the field environment. The core strategy is to utilize the criterion that the dot product of the measured gravity and the rotation speed in a fixed frame remains constant. To eliminate the impact of external acceleration, the calibration process involves separate procedures for measuring local gravity and rotation speed. Moreover, unlike existing approaches for auto calibration of triaxial sensors that often result in nonlinear optimization problems, the proposed method simplifies the estimation of the gyroscope scale factor by employing a linear least squares algorithm. Extensive numerical simulations have been conducted to analyze the proposed method's performance in calibrating the six-parameter triaxial gyroscope model, taking into consideration measurements corrupted by simulated noise. Experimental validation was also carried out using two commercially available MEMS inertial measurement units (LSM9DS1) and a servo motor. The experimental results effectively demonstrate the efficacy of the proposed calibration approach.

Human-Machine Co-Adaptation for Robot-Assisted Rehabilitation via Dual-Agent Multiple Model Reinforcement Learning (DAMMRL)

This study introduces a novel approach to robot-assisted ankle rehabilitation by proposing a Dual-Agent Multiple Model Reinforcement Learning (DAMMRL) framework, leveraging multiple model adaptive control (MMAC) and co-adaptive control strategies. In robot-assisted rehabilitation, one of the key challenges is modelling human behaviour due to the complexity of human cognition and physiological systems. Traditional single-model approaches often fail to capture the dynamics of human-machine interactions. Our research employs a multiple model strategy, using simple sub-models to approximate complex human responses during rehabilitation tasks, tailored to varying levels of patient incapacity. The proposed system's versatility is demonstrated in real experiments and simulated environments. Feasibility and potential were evaluated with 13 healthy young subjects, yielding promising results that affirm the anticipated benefits of the approach. This study not only introduces a new paradigm for robot-assisted ankle rehabilitation but also opens the way for future research in adaptive, patient-centred therapeutic interventions.

EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification

Brain-Computer Interfaces connect the brain to external control devices, necessitating the accurate translation of brain signals such as from electroencephalography (EEG) into executable commands. Graph Neural Networks (GCN) have been increasingly applied for classifying EEG Motor Imagery signals, primarily because they incorporates the spatial relationships among EEG channels, resulting in improved accuracy over traditional convolutional methods. Recent advances by GCNs-Net in real-time EEG MI signal classification utilised Pearson Coefficient Correlation (PCC) for constructing adjacency matrices, yielding significant results on the PhysioNet dataset. Our paper introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative technique for constructing adjacency matrices for EEG channels. It does not require pre-existing knowledge of inter-channel relationships, and it can be tailored to suit both individual subjects and GCN model architectures. Our findings demonstrated that the PCC method outperformed the Geodesic approach by 9.65% in mean accuracy, while our EEG_GLT matrix consistently exceeded the performance of the PCC method by a mean accuracy of 13.39%. Also, we found that the construction of the adjacency matrix significantly influenced accuracy, to a greater extent than GCN model configurations. A basic GCN configuration utilising our EEG_GLT matrix exceeded the performance of even the most complex GCN setup with a PCC matrix in average accuracy. Our EEG_GLT method also reduced MACs by up to 97% compared to the PCC method, while maintaining or enhancing accuracy. In conclusion, the EEG_GLT algorithm marks a breakthrough in the development of optimal adjacency matrices, effectively boosting both computational accuracy and efficiency, making it well-suited for real-time classification of EEG MI signals that demand intensive computational resources.

Human Machine Co-adaption Interface via Cooperation Markov Decision Process System

This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-based reinforcement learning. Previous studies focus more on robot assistance, i.e., to improve the control strategy so as to fulfill the objective of Assist-As-Needed. In this study, we treat the full process of robot-assisted rehabilitation as a co-adaptive or mutual learning process and emphasize the adaptation of the user to the machine. To this end, we proposed a Co-adaptive MDPs (CaMDPs) model to quantify the learning rates based on cooperative multi-agent reinforcement learning (MARL) in the high abstraction layer of the systems. We proposed several approaches to cooperatively adjust the Policy Improvement among the two agents in the framework of Policy Iteration. Based on the proposed co-adaptive MDPs, the simulation study indicates the non-stationary problem can be mitigated using various proposed Policy Improvement approaches.