Evaluating the Accuracy of Step Tracking and Fall Detection in the Starkey Livio Artificial Intelligence Hearing Aids: A Pilot Study
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The primary purpose of this study was to examine the efficacy and the effectiveness of Starkey Livio Artificial Intelligence hearing aids in tracking step count. A secondary purpose was to investigate the accuracy of the fall detection and alert system of Livio hearing aids in detecting fall maneuvers. Method A participant wore Binaural Starkey Livio receiver-in-the-canal style hearing aids, a Sportline pedometer, and a Fitbit Charge 3 concurrently during both real-world and treadmill walking conditions. The real-world condition was conducted over a 5-day period. Step count for the treadmill protocol was assessed at six different treadmill speeds (2 mph, 2.5 mph, 3 mph, 3.5 mph, 4 mph, 4.5 mph, and 5 mph). The fall detection and alert system were assessed through falling maneuvers of activities of daily living. Results In the real-world condition, Livio, Sportline, and Fitbit recorded steps within 1 SD of each other. In addition, Livio recorded the most accurate steps compared to actual physical steps taken. In the treadmill condition, Livio recorded the least number of steps across all walking paces compared to the rest of the devices. Also, Livio hearing aids detected majority of the engaged falling maneuvers. Conclusions The Livio was found to be feasible, consistent, and sensitive in detecting steps and falls. Further research of higher sample size and recruitment of individuals with hearing loss are suggested.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it