Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling
These results suggest that fall risk as determined by the STEADI tests was in most instances similar for IMU-HAs and trained observers; however, differences were observed in certain situations, suggesting improvements are needed in the algorithm to maximize accurate fall risk categorization.
Authors
- Robert K. Jackler (ORCID: https://orcid.org/0000-0003-2525-8377)
- Matthew B. Fitzgerald (ORCID: https://orcid.org/0000-0003-0126-2371)
- Kristen K. Steenerson (ORCID: https://orcid.org/0000-0003-2253-0195)
- Justin R. Burwinkel
- Myriam Srour (ORCID: https://orcid.org/0000-0003-1009-0508)
- Achintya K. Bhowmik (ORCID: https://orcid.org/0000-0003-0476-3497)
- Yifei Ma (ORCID: https://orcid.org/0000-0003-1305-3577)
- Erin Isanhart
- Daniel P. Keating (ORCID: https://orcid.org/0000-0001-6868-8006)
- David A. Fabry
- Bryn Griswold
Institutions
- Kansas City University (US)
- Starkey Hearing Technologies (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Otology & Neurotology
- Published
- 2024-12-17
- DOI
- https://doi.org/10.1097/mao.0000000000004386
- Citations
- 1
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 1.15