Integrating functional wrist articulation in prosthetic robot arms is crucial for enhancing natural movement and reducing compensatory upper limb motions. However, two significant challenges remain in electromyography (sEMG)-based prosthetic control: (1) real-time processing via efficient model design and (2) cross-subject generalization to address the individual variability in muscle signals. This study employs the MAMBA2 architecture to address the first challenge, leveraging Structured State Space Models (SSM) for efficient long-sequence inference. This enables real-time control with minimal computational overhead, making it well-suited for prosthetic robot arm applications. To tackle the second challenge, we implement a Representation Subspace Distance (RSD)-based Unsupervised Domain Adaptation (UDA), which preserves feature scale while aligning inter-subject variations, mitigating domain shift effects, and improving subject-independent wrist movement estimation. The model is trained on the Ninapro DB2 dataset, utilizing multi-channel sEMG signals and corresponding wrist kinematics. Evaluation results demonstrate that the MAMBA architecture outperforms conventional recurrent neural networks, achieving lower Mean Squared Error (MSE) and higher R2values, with the Attention variant exhibiting the best prediction performance. Furthermore, this study highlights that the proposed UDA approach, combined with RSD-based alignment, significantly enhances cross-subject performance, reducing the need for extensive calibration. By enabling real-time processing through a computationally efficient model structure and effectively addressing cross-subject variability, this study contributes to developing a more reliable and generalizable sEMG-based robotic prosthesis controller, ultimately improving its applicability across diverse individuals.

Ordering-based approaches to causal discovery identify topological orders of causal graphs, providing scalable alternatives to combinatorial search methods. Under the Additive Noise Models (ANMs) assumption, recent causal ordering methods based on score matching require an accurate estimation of the Hessian diagonal of the log-densities. However, previous approaches mainly use Stein gradient estimators, which are computationally expensive and memory-intensive. Although DiffAN addresses these limitations by substituting kernel-based estimates with diffusion models, it remains numerically unstable due to the second-order derivatives of score models. To alleviate these problems, we propose Score-informed Neural Operator (SciNO), a probabilistic generative model in smooth function spaces designed to stably approximate the Hessian diagonal and to preserve structural information during the score modeling. Empirical results show that SciNO reduces order divergence by 42.7% on synthetic graphs and by 31.5% in real-world datasets on average compared to DiffAN, while maintaining memory efficiency and scalability. Furthermore, we propose a probabilistic control algorithm for causal reasoning with autoregressive models that integrates SciNO's probability estimates with autoregressive model priors, enabling reliable data-driven causal ordering informed by semantic information. Consequently, the proposed method enhances causal reasoning abilities of LLMs without additional fine-tuning or prompt engineering.

Due to lack of mobility, children are limited in their interaction with others. This lack of interaction with peers leads to emotional problems in children with special needs, behavior disorders, and social maladjustment. These social problems continue even when these children become adults. Hence, it is important to promote social skills at a very early age. In this study, we suggest a new training paradigm for young infants and toddlers seated on mobile robots, using force feedback joystick, to bring them closer to their peers so that it can facilitate interaction. Four healthy children participated in a game as they chase a caregiver. A haptic force feedback strategy teaches how to follow the caregiver. The force feedback guidance strategy is tested as a training tool to bring the children in close proximity to their peers.

Cerebral palsy (CP) is a disorder of movement and posture in children caused by non-progressive insult of the immature brain. The characteristic features are weakness, spasticity, muscle contractures, and poor motor coordination. The gait patterns of children with CP are slow, uncoordinated, and unstable. Our hypothesis is that these impaired children will benefit from robot enhanced walkers to improve their balance, coordination, and speed during gait. In addition, this experience will also impact their clinical scores that relate to their functional performance and caregiver assistance. In this study, we used a specially-designed robotic walker which children used to perform a series of walking tasks, in increasing order of difficulty. This study was performed in 30 training sessions over a period of 3 months. Each training session lasted for 20 minutes. The outcome measures were variables recorded by the robot such as travel distance, average speed, and clinical measured variables that characterize their disability profiles.