Seifer Ann-Kristin, Küderle Arne, Strobel Kaja, Hannemann Ronny, Eskofier Björn M
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.
WS Audiology, 91058 Erlangen, Germany.
Audiol Res. 2025 Apr 23;15(3):45. doi: 10.3390/audiolres15030045.
: Hearing loss, particularly in older adults, is associated with reduced physical functioning; increased fall risk; and altered gait patterns, including slower walking speed and shorter step length. While the underlying mechanisms are not fully understood, one possibility is that these gait changes result from an additional cognitive load due to hearing difficulties. Prior research suggests that hearing aids may improve balance; however, their impact on gait remains less well explored. : This study investigated gait parameters in individuals with hearing loss as they walked with and without hearing aid amplification under different dual-task conditions. Additionally, we showed the potential of ear-worn sensors for detecting relevant gait changes. To achieve this, we used a hearing-aid-integrated accelerometer and our open-source EarGait framework comprising gait-related algorithms specifically developed for ear-worn sensors. : Our findings revealed no significant differences in gait velocity or step length between the unaided and aided conditions. For stride time, we observed a significant interaction effect; however, the effect size was negligible. The dual-task costs were lower than in previous reports, indicating that the applied dual-task paradigm did not induce the expected cognitive demand. The ear-worn gait analysis system showed strong performance compared to foot-worn sensors. : Our findings indicate that in controlled, low-cognitive-demand settings, hearing aid amplification does not affect gait performance and, therefore, neither hinders nor improves walking performance. Additionally, the high accuracy of the ear-worn gait analysis system highlights the strong potential of ear-mounted wearable devices ("earables") for real-world mobility assessments. Future research should explore more complex real-world conditions to better understand the impact of hearing aids on walking behavior. Our proposed earable-based system offers a promising tool for continuous, unobtrusive gait monitoring in everyday environments.
听力损失，尤其是在老年人中，与身体机能下降、跌倒风险增加以及步态模式改变有关，包括步行速度减慢和步长缩短。虽然其潜在机制尚未完全了解，但一种可能性是这些步态变化是由听力困难导致的额外认知负荷引起的。先前的研究表明助听器可能会改善平衡；然而，它们对步态的影响仍有待进一步探索。
本研究调查了听力损失患者在不同双任务条件下佩戴和不佩戴助听器时行走的步态参数。此外，我们展示了耳戴式传感器检测相关步态变化的潜力。为实现这一目标，我们使用了集成在助听器中的加速度计以及我们的开源EarGait框架，该框架包含专门为耳戴式传感器开发的与步态相关的算法。
我们的研究结果显示，在未佩戴助听器和佩戴助听器的情况下，步态速度或步长没有显著差异。对于步幅时间，我们观察到了显著的交互作用；然而，效应大小可以忽略不计。双任务成本低于先前的报告，表明所应用的双任务范式没有引发预期的认知需求。与足部佩戴的传感器相比，耳戴式步态分析系统表现出色。
我们的研究结果表明，在受控的、低认知需求的环境中，助听器放大不会影响步态表现，因此既不会阻碍也不会改善行走性能。此外，耳戴式步态分析系统的高精度突出了耳戴式可穿戴设备（“可穿戴设备”）在现实世界移动性评估中的巨大潜力。未来的研究应该探索更复杂的现实世界条件，以更好地理解助听器对行走行为的影响。我们提出的基于可穿戴设备的系统为日常环境中持续、不引人注意的步态监测提供了一个有前景的工具。
