| Autor: |
Roberto Di Marco, Maria Rubega, Olive Lennon, Asja Vianello, Stefano Masiero, Emanuela Formaggio, Alessandra Del Felice |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2381-2390 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1558-0210 |
| DOI: |
10.1109/TNSRE.2023.3273819 |
| Popis: |
Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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