2024-12-16 ペンシルベニア州立大学(PennState)
Xiaogang Hu researches and develops assistive technologies that could help restore movement in joints and limbs. Credit: Tyler Henderson/Penn State.
<関連情報>
- https://www.psu.edu/news/engineering/story/person-centered-bio-inspired-research-leads-improved-control-prosthetics
- https://ieeexplore.ieee.org/document/10772626
- https://ieeexplore.ieee.org/document/10772475
指屈筋における誘発動作の時空間ダイナミクス: 経皮的神経刺激の最適化への示唆 Spatial-Temporal Dynamics of Evoked Action in Finger Flexors: Implications for Optimizing Transcutaneous Nerve Stimulation
Susan K. Coltman; Luis Vargas; Xiaogang Hu
IEEE Transactions on Biomedical Engineering Published:03 December 2024
DOI:https://doi.org/10.1109/TBME.2024.3510640
Abstract:
Objective: Transcutaneous nerve stimulation (TNS) is a promising approach for the neurorehabilitation of hand function; however, its effects on muscle activation patterns remain poorly understood. To investigate the spatial and temporal distributions of H-reflexes and M-waves in finger flexor muscles using multichannel TNS and high-density electromyography. Methods: Fifteen healthy participants underwent stimulation of the median and ulnar nerves, and the muscle activity and finger forces were recorded. Recruitment curves and spatial activation maps were constructed for the H-reflexes and M-waves across stimulation intensities and locations. Results: Considerable inter-individual variability was observed in the recruitment patterns and spatial distributions. Higher spatial congruence between the H-reflex and M-wave activation patterns in extrinsic arm muscles than in intrinsic hand muscles was associated with more efficient force production. The relationship between spatial activation patterns and force outputs varied across fingers, with earlier recruitment of index finger muscles. Conclusion: This study provides new insights into the complex interplay between the afferent and efferent pathways in hand motor control. The associations between spatial congruence, recruitment patterns, and force production efficiency enhance our understanding of the neuromuscular activation mechanisms. Significance: These findings have implications for optimizing TNS protocols in neurorehabilitation and developing personalized interventions for individuals with impaired hand function.
教師なしニューラルデコーディングによる器用な多指の屈伸力の予測 Unsupervised Neural Decoding to Predict Dexterous Multi-Finger Flexion and Extension Forces
Long Meng; Xiaogang Hu
IEEE Journal of Biomedical and Health Informatics Published:02 December 2024
DOI:https://doi.org/10.1109/JBHI.2024.3510525
Abstract:
Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural signals from surface electromyogram (sEMG) signals. However, these decoders require labeled data for decoder training, which is challenging to obtain in cases such as limb loss and limits decoder generalizability. In our study, we extracted motoneuron firing information by decomposing high-density sEMG signals from both finger flexor and extensor muscles. We assigned each neuron a probability, reflecting its association with the targeted fingers, based on its temporal firing rate distribution. We then employed a probability thresholding and weighting strategy to select and prioritize neurons for finger force predictions. Our results revealed that the unsupervised neural decoder significantly outperformed both the supervised neural decoder and sEMG-amplitude approaches (R 2 : 0.74 ± 0.028 vs. 0.70 ± 0.028 vs. 0.63 ± 0.031, root mean square error: 6.74±0.60% vs. 8.41 ± 0.56% vs. 10.33 ± 0.59% of maximum force), thereby offering a promising and practical solution for accurate force controls. Our results also demonstrated high computational efficiency (96.26 ± 24.16 ms), viable for real-time implementations. The outcomes offer an unsupervised decoder with simplified data requirements for decoder training. The decoder boasts enhanced functionality and adaptability in predicting finger flexion and extension forces. In addition, our approach holds promise for broader applications in scenarios where force measurement proves challenging.
