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多模态运动预测——为患者提供个性化的辅助。

Multimodal movement prediction - towards an individual assistance of patients.

机构信息

Robotics Lab, University of Bremen, Bremen, Germany ; Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany.

Robotics Lab, University of Bremen, Bremen, Germany.

出版信息

PLoS One. 2014 Jan 8;9(1):e85060. doi: 10.1371/journal.pone.0085060. eCollection 2014.

Abstract

Assistive devices, like exoskeletons or orthoses, often make use of physiological data that allow the detection or prediction of movement onset. Movement onset can be detected at the executing site, the skeletal muscles, as by means of electromyography. Movement intention can be detected by the analysis of brain activity, recorded by, e.g., electroencephalography, or in the behavior of the subject by, e.g., eye movement analysis. These different approaches can be used depending on the kind of neuromuscular disorder, state of therapy or assistive device. In this work we conducted experiments with healthy subjects while performing self-initiated and self-paced arm movements. While other studies showed that multimodal signal analysis can improve the performance of predictions, we show that a sensible combination of electroencephalographic and electromyographic data can potentially improve the adaptability of assistive technical devices with respect to the individual demands of, e.g., early and late stages in rehabilitation therapy. In earlier stages for patients with weak muscle or motor related brain activity it is important to achieve high positive detection rates to support self-initiated movements. To detect most movement intentions from electroencephalographic or electromyographic data motivates a patient and can enhance her/his progress in rehabilitation. In a later stage for patients with stronger muscle or brain activity, reliable movement prediction is more important to encourage patients to behave more accurately and to invest more effort in the task. Further, the false detection rate needs to be reduced. We propose that both types of physiological data can be used in an and combination, where both signals must be detected to drive a movement. By this approach the behavior of the patient during later therapy can be controlled better and false positive detections, which can be very annoying for patients who are further advanced in rehabilitation, can be avoided.

摘要

辅助设备,如外骨骼或矫形器,通常利用生理数据来检测或预测运动的开始。运动开始可以在执行部位,即骨骼肌处检测到,例如通过肌电图(EMG)。运动意图可以通过分析大脑活动来检测,例如通过脑电图(EEG)记录,或者通过主体的行为来检测,例如通过眼动分析。这些不同的方法可以根据神经肌肉障碍的类型、治疗状态或辅助设备来使用。在这项工作中,我们在健康受试者执行自主发起和自主调节的手臂运动时进行了实验。虽然其他研究表明多模态信号分析可以提高预测性能,但我们表明,脑电图(EEG)和肌电图(EMG)数据的合理组合有可能提高辅助技术设备的适应性,以满足例如康复治疗早期和晚期的个体需求。在患者肌肉较弱或与运动相关的大脑活动较弱的早期阶段,重要的是要实现高阳性检测率,以支持自主发起的运动。从脑电图(EEG)或肌电图(EMG)数据中检测到大多数运动意图可以激发患者的积极性,并促进其康复进展。在患者肌肉或大脑活动较强的后期阶段,更重要的是要可靠地预测运动,以鼓励患者更准确地运动,并在任务中投入更多的努力。此外,还需要降低假检测率。我们提出这两种类型的生理数据可以组合使用,其中必须同时检测到两个信号才能驱动运动。通过这种方法,可以更好地控制患者在后期治疗期间的行为,并避免对康复进展较快的患者来说非常烦人的假阳性检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae8/3885685/10f2a98f9ad6/pone.0085060.g001.jpg

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