Rizauddin Ramli

This study presents a platform-specific and subject-independent validation of an IMU-based LSTM-CNN gait-recognition pipeline on a self-developed lower-limb exoskeleton. Ten healthy participants performed five locomotion modes, including level walking, stair ascent, stair descent, standing up, and sitting down. Each participant completed five level-walking trials and ten trials for each of the remaining four locomotion modes, resulting in 450 original recording trials. IMU signals collected from the thigh, shank, and foot were segmented using a 128-sample sliding window with a 64-sample step. To prevent information leakage from overlapping windows and to evaluate inter-subject generalization, the dataset was partitioned at the participant level before window segmentation. The LSTM layers modeled temporal dependencies in the IMU sequences, while Conv1D and pooling layers extracted local discriminative features. Under the subject-independent evaluation protocol, the model achieved an overall accuracy of 94.08%, a macro-F1 score of 93.73%, and a balanced accuracy of 93.62%. These results support the preliminary feasibility of the implemented IMU-based LSTM-CNN workflow for offline gait-mode recognition on the tested lower-limb exoskeleton platform. Further validation under larger participant cohorts and online closed-loop conditions is still required.

Precise end-effector positioning of a robotic arm is critical for accurate performance in robotics, which directly impacts the system’s performance in applications requiring high precision. This paper presents an optimized tag-based inverse kinematics approach for a 6 DoF robotic arm using a Modified Biogeography-Based Optimization (MBBO) algorithm, which is an enhanced version of the original Biogeography-Based Optimization (BBO), a population-based evolutionary algorithm inspired by the natural distribution of species across habitats. The target position is identified via AprilTag visual fiducial markers and integrated into the inverse kinematics solver. Performance was evaluated against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and BBO through 3-dimensional simulations involving ten target points. Results show that MBBO achieves reduced positioning error compared to GA, PSO, and BBO, resulting in lower end-effector position errors. The findings highlight the effectiveness of combining visual tag detection with advanced optimization for precise robotic arm control.

Lower-limb rehabilitation exoskeletons are often discussed in terms of mechanics, sensing, and control, yet their rehabilitation value depends on how these elements work together during human–robot interaction. This review focuses on the integration of sensing, compliant actuation, and assist-as-needed control in lower-limb rehabilitation exoskeletons. Recent research suggests that effective assistance depends not only on actuator output, but also on reliable gait-state detection, intention-related sensing, mechanical transparency, and real-time adaptation. Current progress in sensing based on inertial measurement units (IMUs), force and pressure measurements, and electromyography (EMG) is reviewed, followed by discussion of how actuation choice and mechanical compliance influence safe and effective assistance. Major control strategies, including trajectory tracking, impedance control, hierarchical control, learning-based methods, and assist-as-needed approaches, are then compared. Remaining barriers to clinical translation include signal instability, safety and certification requirements, and the persistent gap between laboratory performance and patient-specific rehabilitation needs. Future progress will likely depend on tighter co-design of sensing, hardware compliance, and cooperative control.

In recent years, the potential of exoskeletons to enhance human capabilities has attracted significant research interest. Nevertheless, the control of Rehabilitation Lower-Limb Exoskeletons (RLLEs) is challenging because of their strong nonlinear behaviour. In the paper, a Grey Fuzzy Sliding-Mode (GFSM) controller, which is designed based on the optimization accuracy and estimation capability of the fuzzy logic system, was used for trajectory tracking of a RLLE’s joints. This paper presents the tuning of the controller parameters optimally using Grey Wolf Optimization (GWO) integrated with an Interval Type-2 Fuzzy Logic System (IT2FLS) in real-time. The GFSM was selected as the controller law, in which initially, GWO was used to tune the parameters based on the estimated RLLE’s mathematical model. The optimal tuned parameters were employed to determine the defuzzification range of the fuzzy logic system. IT2FLS was provided to tune the real-time controller parameters. The performance of the GFSM was validated by human-RLLE experiments which showed superior performance compared to other conventional controllers. The experimental results show that the controller achieved reductions in the average error of 81.8%, 82.9%, 84.1%, and 80.6%, respectively, compared with conventional adaptive control methods. These findings indicate that the GFSM can be used to improve motor function recovery in individuals with hemiplegia. By integrating biomechanically inspired motion assistance with IT2FLS, our proposed GFSM controller contributes to the development of biomimetic rehabilitation exoskeletons capable of reproducing natural human gait.

Wearable lower-limb exoskeletons have emerged as a promising solution for rehabilitation, mobility assistance, and human performance augmentation. However, their practical deployment is limited by lightweight design challenges, particularly the trade-off between structural stiffness and system mass, the increased inertial burden caused by distal mass distribution, and the difficulty of preserving torque transmission under wearable constraints. This mini-review summarizes recent advances in lightweight design from five perspectives: structural architecture, quantitative system mass, dynamic and human-exoskeleton coupled modeling, material selection, and actuation or transmission systems. Approaches such as topology optimization, hybrid architectures, high strength-to-weight materials, remote actuation, Bowden cable transmission, and high torque-density actuation are discussed in relation to these challenges. Overall, lightweight design is identified as a system-level co-design problem requiring coordinated optimization across mechanical structure, actuation, control, and user biomechanics. Future developments are expected to focus on integrated modeling, mechanism-level synthesis, and data-driven methods to improve performance and user adaptability in next-generation exoskeleton systems.

Exoskeleton robots have become a representative class of wearable robotic systems for rehabilitation, mobility assistance, occupational support, and human performance augmentation. As the field moves from laboratory prototypes toward clinical, industrial, and daily life deployment, research priorities are shifting from device-centered performance improvement to human-centered integration. This mini review provides a structured and critically oriented synthesis of exoskeleton technologies from four interconnected perspectives: technical architecture, technological paradigm evolution, deployment barriers, and future research directions. To improve transparency and reproducibility, we adopted a narrative review strategy with explicit literature selection criteria. Publications were identified from major scientific databases using combinations of keywords related to exoskeleton robotics, actuation, control, human-robot interaction, soft robotics, neural interfaces, rehabilitation, and wearable assistance. Representative studies were selected according to relevance, technical influence, clinical or engineering significance, and coverage of major technological paradigms. The review first analyzes three core technical dimensions-actuation systems, control strategies, and human-robot interaction which jointly determine the performance, adaptability, and usability of exoskeleton systems. Rather than only summarizing these technologies, we compare their trade-offs in terms of power density, control precision, compliance, energy efficiency, personalization, safety, and deployment readiness. The review then examines the evolution from rigid exoskeletons, which provide high structural support and precise force transmission, to soft exoskeletons, which improve compliance and comfort, and further to bio-integrated systems that combine neural interfaces, functional electrical stimulation, multimodal sensing, and mechanical assistance. Based on this synthesis, we organize the review using a Human–Exoskeleton Integration Maturity Framework spanning mechanical coupling, physical compliance, functional adaptation, and cognitive/bio-integrated coupling. Persistent barriers, including energy supply, personalization, safety assurance, cost, regulatory translation, and ethical governance, are critically discussed. Finally, future directions are outlined, including neural-interface-driven control, multimodal perception, human-in-the-loop optimization, hybrid rigid-soft architectures, and socially responsible design. Overall, this review argues that the next stage of exoskeleton development will depend not merely on stronger actuators or more intelligent algorithms, but on integrated systems that are adaptive, trustworthy, affordable, and seamlessly embedded in human movement and function.

Introduction: The optimization of machining process decision-making remains a major challenge in intelligent manufacturing due to the uncertainty of process information, incompleteness of rule bases, and the tendency of traditional algorithms to converge to local optima. Therefore, enhancing the adaptability and robustness of decision-making systems is a crucial task for achieving efficient and reliable computer numerical control (CNC) process planning. Methods: This study proposes a hybrid decision-making approach that integrates fuzzy theory with support vector machines (SVM) to address uncertainty and incomplete knowledge representation in CNC turning. An Analytic Hierarchy Process (AHP) is used to determine the relative importance of influencing factors, and trapezoidal membership functions are designed to determine the credibility of fuzzy reasoning rules. When the credibility value falls below a defined threshold, a linear-kernel SVM model is activated to provide alternative decisions which formed a fuzzy-SVM collaborative reasoning mechanism. Results: Experimental validation demonstrates that the proposed hybrid fuzzy-SVM collaborative method achieves remarkable classification accuracy on the test dataset. The system maintains stable performance even under low-credibility or incomplete rule conditions. The SVM module effectively compensates for the limitations of the fuzzy reasoning process, thereby improving the robustness of decision inference compared to single-model approaches. Conclusion: The proposed fuzzy-SVM collaborative reasoning framework enhances the adaptability, stability, and interpretability of CNC machining process decision-making. These findings offer a practical and scalable solution for intelligent process planning in complex and uncertain manufacturing environments.

Legged robot locomotion remains a critical challenge in robotics, demanding control strategies that are not only dynamically stable and robust but also capable of adapting to complex and changing environments. deep reinforcement learning (DRL) has recently emerged as a powerful approach to automatically generate motion control policies by learning from interactions with simulated or real environments. This study provides a systematic overview of DRL applications in legged robot control, emphasizing experimental platforms, measurement techniques, and benchmarking practices. Following PRISMA guidelines, 27 peer-reviewed studies published between 2018 and 2025 were analyzed, covering model-free, model-based, hierarchical, and hybrid DRL frameworks. Our findings reveal that reward shaping, policy representation, and training stability significantly influence control performance, while domain randomization and dynamic adaptation methods are essential for bridging the simulation-to-real-world gap. In addition, this review highlights instrumentation approaches for evaluating algorithm effectiveness, offering insights into sample efficiency, energy management, and safe deployment. The results aim to guide the development of reproducible and experimentally validated DRL-based control systems for legged robots.

This paper proposes a novel intelligent switching control strategy for a five-bar lower limb exoskeleton. First, during the support phase, terminal sliding mode control (TSMC) is employed to ensure robust stability and high-torque amplification capabilities. Then, during the swing phase, a hybrid controller combining proportional-integral-derivative (PID) control and the adaptive neuro-fuzzy inference system (ANFIS) is implemented to generate natural and compliant leg movements. Finally, to achieve smooth transitions between phases, an intelligent switching algorithm based on multi-sensor information fusion is proposed. Simulation results demonstrate that the proposed strategy keeps trajectory tracking errors below 0.05 across all gait phases and achieves stable torque amplification ratios ranging from 1:6 to 1:10. This performance significantly reduces the user’s physical exertion. These findings validate the effectiveness of this control framework in improving the stability and comfort of human–machine interaction.