IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

Robot-aided rehabilitation effectively supports treatment of upper-limb disorders and enhances outcomes when combined with traditional therapy. Artificial intelligence enables behavioral cloning of physiotherapists' expertise to autonomously modulate robot assistance from real-time multimodal patient data. Therefore, this paper aims to propose and validate a behavioral cloning strategy, namely Physiotherapist-Supervised Parameter Adaptation (PSPA), for online tuning the robot assistance level replicating the physiotherapists' decision-making. The experimental validation was conducted in a clinical setting involving ten post-surgical orthopedic patients who participated in a robot-aided rehabilitation session using the KUKA LWR 4+ robot. The sessions were supervised by physiotherapists who could adjust the level of robotic assistance as needed, thus labelling the collected patient multimodal data. The validation aimed at i) identifying the best-performing input modality, feature set, and classifier, and ii) comparing the capability of the approach in tailoring the assistance level with respect to the established performance-based (PB) one. Combining biomechanical and physiological features significantly improved the classification performance across all classifiers, with the highest performance observed for the Multi-layer Perceptron on the present dataset. Moreover, using the optimized feature set, the proposed PSPA methodology achieved an even greater alignment with the physiotherapists' decisions with respect to the PB approach (ΔF1-score = 15.40 ± 30.33%, ρ = 0.56 ± 0.21 for PSPA, ρ = -0.12 ± 0.43 for PB).

As skeletal data can be collected non-invasively while preserving patient privacy, it is widely used in public medical datasets to document patient behavior. Autism Spectrum Disorder (ASD) is characterized by significant behavioral heterogeneity, reflected in the topological structure and dynamic evolution of skeletal movements. This complexity poses substantial challenges for skeleton-based behavioral analysis. Existing methods struggle to effectively utilize behavioral evolution for subject-specific reasoning, leading to suboptimal representations that lack diagnostic relevance for autism. To address this limitation, we propose a Behavioral Evolution-based Edge Reconstruction (BER) Strategy for learning autism-related behavioral representations. By reconstructing a high-granularity adjacency matrix that spans both spatial and temporal dimensions, utilizing dynamic evolution and spatial location information, BERGCN enhances behavioral reasoning. Specifically, we first compute channel-level spatial and temporal edge reconstruction parameters by performing feature compression and targeted convolution operations on the differences between neighboring frames. Based on these, the spatial edge reconstruction module is designed by combining a generic attention map with two personalized attention maps, while the temporal edge reconstruction module is implemented using flexible frame replace ment and weighted aggregation. Finally, we investigate both single-modal and multimodal network architectures under various fusion strategies. We evaluate BERGCN on three autism clinical tasks and a benchmark action recognition dataset. Experimental results demonstrate competitive performance, showing improved sensitivity to subject-specific behavioral patterns while maintaining computational efficiency.

The tongue is a uniquely agile muscular structure essential for vital tasks of speech, breathing, chewing, and swallowing, functions commonly disrupted following neurological injury. Yet, current rehabilitation approaches lack objective measures and techniques to characterize impairment and restore the tongue's ability. Here, we introduce a clinic-friendly method that isolates and quantifies tongue agility, defined as the ability to execute rapid and precise movements, using a wireless intraoral sensing device that provides real-time visual feedback of movement. Six participants diagnosed with dysarthria completed seven one-hour intervention sessions. Tongue movement probability distributions were generated to identify individualized deviations from neurotypical patterns. An individualized visual feedback intervention was designed to redirect movement away from over-expressed regions toward under-expressed deficient areas. Across the intervention, sensing area coverage increased significantly by 10.29 %, while over-expressed areas decreased significantly by 3.99 %, and movement velocity improved significantly by 3.85 %. This pilot study provides promising preliminary evidence that precision visual feedback rehabilitation can reshape tongue movement patterns and enhance tongue agility in individuals with oral motor disorders.

Aging alters both biomechanical and neural factors related to walking, leading to reductions in preferred gait speed with age. Biomechanical variability in human walking has been an area of great interest for aging research. Neural variability has not been well studied in this context. Electroencephalography (EEG) can measure brain activity during walking, allowing us to quantify interstride variability of electrocortical activity. We recruited younger and older adults to walk (0.25-1.0 m/s) while we measured EEG interstride variability in theta, alpha, and beta power. We hypothesized that theta, alpha, and beta variability would decrease at faster walking speeds like most gait kinematic variables. We also hypothesized that older adults would have more interstride variability compared to younger due to reduced gait automaticity. We observed sensorimotor and posterior parietal cortices for their roles in motor action and sensory processing. Interstride variability in theta power lessened with faster walking speeds in posterior parietal cortex, and Interstride variability in alpha and beta power lessened in both sensorimotor and posterior parietal cortex. Further, we found that older adults had less interstride variability than younger adults, primarily in alpha and beta. We also observed interstride phasic alignment of electrocortical activity across the gait cycle. We found broadband increases in interstride phase alignment across the gait cycle, and that older higher functioning adults had greater phase alignment in gamma (30-50 Hz) than younger adults in parietal cortex. These findings suggest that the automaticity of gait is greater at faster walking speeds, and that older adults' reduced automaticity of gait may be unrelated to electrical brain activity.