Biodesign Innovation Center, Department of Parasitology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Siriraj Integrative Center for Neglected Parasitic Diseases, Department of Parasitology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
PLoS One. 2024 Nov 1;19(11):e0312761. doi: 10.1371/journal.pone.0312761. eCollection 2024.
Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cannot detect the continuous gait phase in real time, hindering their effectiveness. To address this limitation, researchers have attempted to develop gait phase detection in general using fuzzy logic algorithms and neural networks. However, there is a paucity of research on real-time and continuous gait phase detection. In light of this gap, we propose an unsupervised learning method for real-time and continuous gait phase detection. This method employs windows of real-time trajectories and a pre-trained model, utilizing trajectories from treadmill walking data, to detect the real-time and continuous gait phase of human on overground locomotion. The neural network model that we have developed exhibits an average time error of less than 11.51 ms across all walking conditions, indicating its suitability for real-time applications. Specifically, the average time error during overground walking at different speeds is 11.20 ms, which is comparatively lower than the average time error observed during treadmill walking, where it is 12.42 ms. By utilizing this method, we can predict the real-time phase using a pre-trained model from treadmill walking data collected with a full motion capture system, which can be performed in a laboratory setting, thereby eliminating the need for overground walking data, which can be more challenging to obtain due to the complexity of the setting.
中风或脊髓损伤后下肢功能障碍的个体需要康复，但传统方法对患者和治疗师都具有挑战性。已经开发出机器人系统来提供帮助；然而，它们目前无法实时检测连续的步态阶段，从而限制了它们的效果。为了解决这个限制，研究人员已经尝试使用模糊逻辑算法和神经网络来开发通用的步态阶段检测。然而，实时和连续步态阶段检测的研究相对较少。针对这一差距，我们提出了一种用于实时和连续步态阶段检测的无监督学习方法。该方法使用实时轨迹窗口和预训练模型，利用跑步机行走数据中的轨迹，来检测人类在地面行走中的实时和连续步态阶段。我们开发的神经网络模型在所有行走条件下的平均时间误差都小于 11.51 毫秒，表明其适用于实时应用。具体来说，在不同速度下的地面行走中的平均时间误差为 11.20 毫秒，这比在跑步机行走中的平均时间误差（12.42 毫秒）要低。通过使用这种方法，我们可以使用从全运动捕捉系统收集的跑步机行走数据的预训练模型来预测实时阶段，这可以在实验室环境中进行，从而无需使用更难以获取的地面行走数据，因为设置更为复杂。
