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情感状态与虚拟现实改善步态康复:一项初步研究。

Affective States and Virtual Reality to Improve Gait Rehabilitation: A Preliminary Study.

作者信息

Rodriguez Jafet, Del-Valle-Soto Carolina, Gonzalez-Sanchez Javier

机构信息

Universidad Panamericana, Facultad de Ingeniería, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico.

School of Computing and Augmented Intelligence, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA.

出版信息

Int J Environ Res Public Health. 2022 Aug 3;19(15):9523. doi: 10.3390/ijerph19159523.

Abstract

Over seven million people suffer from an impairment in Mexico; 64.1% are gait-related, and 36.2% are children aged 0 to 14 years. Furthermore, many suffer from neurological disorders, which limits their verbal skills to provide accurate feedback. Robot-assisted gait therapy has shown significant benefits, but the users must make an active effort to accomplish muscular memory, which usually is only around 30% of the time. Moreover, during therapy, the patients' affective state is mostly unsatisfied, wide-awake, and powerless. This paper proposes a method for increasing the efficiency by combining affective data from an Emotiv Insight, an Oculus Go headset displaying an immersive interaction, and a feedback system. Our preliminary study had eight patients during therapy and eight students analyzing the footage using the self-assessment Manikin. It showed that it is possible to use an EEG headset and identify the affective state with a weighted average precision of 97.5%, recall of 87.9%, and F1-score of 92.3% in general. Furthermore, using a VR device could boost efficiency by 16% more. In conclusion, this method allows providing feedback to the therapist in real-time even if the patient is non-verbal and has a limited amount of facial and body expressions.

摘要

在墨西哥,超过700万人患有某种损伤;其中64.1%与步态有关,36.2%是0至14岁的儿童。此外,许多人患有神经障碍,这限制了他们提供准确反馈的语言能力。机器人辅助步态治疗已显示出显著益处,但使用者必须积极努力才能形成肌肉记忆,而这通常只在大约30%的时间内发生。此外,在治疗过程中,患者的情感状态大多是不满意、清醒且无力的。本文提出一种方法,通过结合来自Emotiv Insight的情感数据、展示沉浸式交互的Oculus Go头戴设备以及一个反馈系统来提高效率。我们的初步研究在治疗期间有8名患者,并有8名学生使用自我评估人偶分析视频片段。结果表明,使用脑电图头戴设备并以97.5%的加权平均精度、87.9%的召回率和92.3%的F1分数识别情感状态总体上是可行的。此外,使用虚拟现实设备可将效率再提高16%。总之,即使患者无法言语且面部和身体表情有限,这种方法也能实时向治疗师提供反馈。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b6c5/9368422/f4e09ed33f7e/ijerph-19-09523-g001.jpg

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