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<p>Standing up from sitting is a common yet mechanically demanding activity of daily living. Those with motor impairments often have difficulties performing sit-to-stand transfers independently. Robotic devices can help them to perform and train this movement, providing mechanical assistance and high numbers of task-specific repetitions. Our limited understanding of human-machine interaction at the neural and muscular levels during robotic-assisted sit-to-stand transfers hinders the development of responsive sit-to-stand devices. Therefore, this study investigates independent and robotic-assisted sit-to-stand transitions, comparing synchronised kinematic, electromyography (EMG) and electroencephalography (EEG) data. Neuro-intact participants (<i>N</i> = 10, 3 males) performed sit-to-stand repetitions, firstly when performed independently and secondly with assistance from a robotic-assisted gait training device. Kinematic data were recorded using a three-dimensional motion capture camera system. EMG was recorded at the bilateral vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius muscles. Surface EEG activity was recorded using eight focused bipolar channels over the sensorimotor cortex. Kinematic data identified sit-to-stand with robotic assistance took significantly longer compared to the independent condition (median difference 4.5&#xa0;s; <i>p</i> = 0.038). The flexion phase was prolonged by 3&#xa0;s, although variability was high (<i>p</i> = 0.064). EMG data suggested a general trend towards reduced lower limb muscle activity during robotic-assisted trials for most muscle groups and phases of sit-to-stand. The exception was the gastrocnemius muscle where peak activity was significantly increased compared to independent trials (Left: q = 0.032; Right: q = 0.040). EEG data highlighted differences in cortical activity between the sit-to-stand conditions, notably where the distinct ERSP patterns evident during independent sit-to-stand were absent or inconsistent in the robotic-assisted condition. The largest differences were observed in alpha and beta bands, at the central electrodes and during upright standing post-movement. This study identified longer times to complete the exoskeleton-assisted task, a trend towards reduced muscle activation, and altered cortical patterns of movement intent and execution. The findings indicate that robotic assistance may modulate neural and muscular processes associated with motor learning, underscoring the importance of developing devices that are more adaptive and responsive to individual user needs. The results inform future brain-computer interface rehabilitation system design, suggesting that a system may need to be trained with the person in the device to ensure accurate cortical pattern recognition.</p>

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Human-machine interaction during robotic-assisted sit-to-stand

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Caitlin McDonald,
  • John Jairo Villarejo-Mayor,
  • Olive Lennon

摘要

Standing up from sitting is a common yet mechanically demanding activity of daily living. Those with motor impairments often have difficulties performing sit-to-stand transfers independently. Robotic devices can help them to perform and train this movement, providing mechanical assistance and high numbers of task-specific repetitions. Our limited understanding of human-machine interaction at the neural and muscular levels during robotic-assisted sit-to-stand transfers hinders the development of responsive sit-to-stand devices. Therefore, this study investigates independent and robotic-assisted sit-to-stand transitions, comparing synchronised kinematic, electromyography (EMG) and electroencephalography (EEG) data. Neuro-intact participants (N = 10, 3 males) performed sit-to-stand repetitions, firstly when performed independently and secondly with assistance from a robotic-assisted gait training device. Kinematic data were recorded using a three-dimensional motion capture camera system. EMG was recorded at the bilateral vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius muscles. Surface EEG activity was recorded using eight focused bipolar channels over the sensorimotor cortex. Kinematic data identified sit-to-stand with robotic assistance took significantly longer compared to the independent condition (median difference 4.5 s; p = 0.038). The flexion phase was prolonged by 3 s, although variability was high (p = 0.064). EMG data suggested a general trend towards reduced lower limb muscle activity during robotic-assisted trials for most muscle groups and phases of sit-to-stand. The exception was the gastrocnemius muscle where peak activity was significantly increased compared to independent trials (Left: q = 0.032; Right: q = 0.040). EEG data highlighted differences in cortical activity between the sit-to-stand conditions, notably where the distinct ERSP patterns evident during independent sit-to-stand were absent or inconsistent in the robotic-assisted condition. The largest differences were observed in alpha and beta bands, at the central electrodes and during upright standing post-movement. This study identified longer times to complete the exoskeleton-assisted task, a trend towards reduced muscle activation, and altered cortical patterns of movement intent and execution. The findings indicate that robotic assistance may modulate neural and muscular processes associated with motor learning, underscoring the importance of developing devices that are more adaptive and responsive to individual user needs. The results inform future brain-computer interface rehabilitation system design, suggesting that a system may need to be trained with the person in the device to ensure accurate cortical pattern recognition.