Ghosh Ria, Chandra Shekar Ram Charan, Hansen John H L
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4221-4224. doi: 10.1109/EMBC44109.2020.9175825.
Internet of things (IoT) in healthcare, has effi-ciently accelerated medical monitoring and assessment through the real-time analysis of collected data. Hence, to support the hearing-impaired community with better calibrations to their clinical processors and hearing aids, a portable smart space interface - AURIS has been developed by the Cochlear Implant Processing Lab (CILab) at UT-Dallas. The proposed Auris interface periodically samples the acoustic space, and through a learn vs test phase, builds a Gaussian mixture model for each specific environmental locations. An effective connection is established by the Auris interface with the CRSS CCi-Mobile research platform through an android app to fine tune the con-figuration settings for cochlear implant (CI) or hearing aid (HA) users entering the room/location. Baseline objective evaluations have been performed in diverse naturalistic locations using 12 hours of audio data. The performance metrics is determined by a verified wireless communication, along with estimated acoustic environment knowledge and room classification at greater than 90% accuracy.
医疗保健领域的物联网通过对收集到的数据进行实时分析，有效地加速了医疗监测和评估。因此，为了更好地校准临床处理器和助听器，以支持听力受损群体，德克萨斯大学达拉斯分校的人工耳蜗处理实验室（CILab）开发了一种便携式智能空间接口——AURIS。所提出的AURIS接口定期对声学空间进行采样，并通过学习与测试阶段，为每个特定的环境位置建立高斯混合模型。AURIS接口通过一个安卓应用程序与CRSS CCi-Mobile研究平台建立有效连接，以便为进入房间/位置的人工耳蜗（CI）或助听器（HA）用户微调配置设置。使用12小时的音频数据，在不同的自然环境中进行了基线客观评估。性能指标由经过验证的无线通信、估计的声学环境知识以及准确率超过90%的房间分类来确定。
