近端听觉 AR 康复:系统集成和健康应用--从听力支持到前庭康复

Jinyoung Lee, S. Ishihara, K. Ishihara, Ken Ito
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摘要

我们介绍了一种基于新原理的新型康复系统,该系统由我们独创的听觉增强现实(AR)耳机组成。听觉增强现实耳机不覆盖双耳,用户可以同时听到真实和虚拟环境的声音。它还可以与助听器结合使用。我们研究了一个支持听障人士的系统,并进行了测试评估。该系统能够为轻度听力障碍者提供类似于 "声音阅读眼镜 "的便利。此外,通过将该系统与周围的扬声器相结合,我们还创造出了一种全新的虚拟听觉幻觉,即声音图像跳入耳朵后又跑开。我们将其命名为 "近端听觉增强(PAAR)"系统。该系统直接影响到保持站立姿势的无意识反射水平,并能产生非常微妙的身体运动干扰。利用该系统,我们可以调节站立姿势,并通过压力中心(COP)测量身体摆动的稳定计,观察自律神经系统在潜意识中补偿干扰的能力。我们观察到,与传统的封闭式耳机和骨传导耳机相比,只有在使用 PAAR(与阵列扬声器和听觉 AR 耳机相结合)时,COP 的下降才有明显差异。通过机器学习分析这些身体运动的大数据,我们有望实现用于诊断、康复、功能维护和预防跌倒的新系统。
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Proximal auditory AR rehabilitation: system integration and wellness applications—from hearing support up toward vestibular rehabilitation
We present a new rehabilitation system based on novel principles, which consists of an auditory augmented reality (AR) headset we originated. The auditory AR headset, which does not cover both ears, allows users to hear both Real and Virtual environmental sounds at the same time. It can also be used in combination with Hearing Aids. We have studied a system to support hearing-impaired people and conducted a test evaluation. The system was able to provide convenience akin to “reading glasses for sound” to those who had mild hearing disabilities. Furthermore, by combining the system with surrounding speakers, a completely novel virtual auditory illusion was created in which the sound image jumps into the ear and runs away. We name this “proximal auditory AR (PAAR)” system. This system directly affects the unconscious level of reflexes for maintaining a standing position and can generate very subtle body motion disturbance. Using this system, we can modulate the standing posture and observe the autonomic nerve system's ability to subliminally compensate for the disturbance, using a stabilometer that measures body sways by center of pressure (COP). We observed a significant difference in the declination of COP only when using the PAAR, which is combined with array speakers and the auditory AR headphone, compared using a conventional closed-type and a bone-conduction headphone. By analyzing such big data of physical movement through machine learning, we expect to realize new systems for diagnosis, rehabilitation, function maintenance, and fall prevention.
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