Designing a Gait Recognition Algorithm for Older Adults Using Mobility Aids: Prospective Cohort Study
Bibliographic record
Abstract
Background: Maintaining mobility is important for older adults to retain independence and reduce fall risk. Wearable technology such as fitness trackers and smartwatches can track physical activity. Unfortunately, gait recognition algorithms are often calibrated using younger adults and are not accurate for older adults, especially when using mobility aids. Objective: Our goal was to develop a gait recognition algorithm capable of detecting the walking patterns of older adults that is robust to using mobility aids. Wrist-worn wearable devices were used to maximize the ubiquity of the approach. Methods: We collected walking and other daily activity data on 17 independent older adults to develop a gait recognition algorithm. Ten participants used mobility aids (ie, 5 cane users, 4 rollator users, 1 walker user). We calibrated a heuristic-based "one-size-fits-most" algorithm leveraging the harmonic patterns associated with walking to recognize the walking patterns of our cohort. This algorithm is computationally lightweight and relies only on accelerometer data. We used hyperparameter tuning using a Parzen tree estimator to find the optimal parameters in a leave-one-subject-out fashion. Results: The calibration process was required for this algorithm to detect walking. The signal amplitude threshold lowered from 0.3g to 0.1g to detect the more subtle walking patterns of older adults. The walking frequency range widened from [1.4Hz, 2.3Hz] to [0.8Hz, 2.8Hz], showing that older adults walk more slowly. The ratio for superharmonics increased from 1.4 to 77.11. Analyzing the false positive rate for the other daily activity classes implies that these superharmonics are artifacts of back-and-forth arm motions that characterize walking in our collected data. Additionally, we report the performance metrics of sensitivity, specificity, and F1-score to evaluate our algorithm. Sensitivity increased from 0.11 to 0.73; F1-score increased from 0.15 to 0.73; and specificity decreased from 0.99 to 0.75. Conclusions: This study successfully recognized the walking patterns of older adults with or without mobility aids. The performance metrics show that this algorithm has promise for being used to monitor physical activity. This approach is computationally lightweight and explainable. Our calibration approach can be adopted to tune to new populations and has a low barrier to entry for adopting a new technology due to the sole reliance on accelerometer data which is a standard sensor in wearable devices. The most noteworthy parameter adjustment is the ratio for superharmonics. Low values for the subharmonic and superharmonic ratios cause the algorithm not to detect walking in our older adult data. We validated the algorithm on ten mobility aid users. A larger study with more participants using mobility aids is necessary to conduct a deeper analysis on what parameters work best for this population. Future work includes validating the algorithm's ability to estimate step counts and measure physical activity in real-world settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".