ArticleFrontiers in sports and active living2026
Validation of AI-based markerless gait event detection during perturbed walking using smartphone videos from two camera perspectives.
Article in Frontiers in sports and active living, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Perturbation-based balance training (PBT) requires objective quantification of reactive stepping responses, yet gait event detection during perturbed walking remains methodologically challenging. Markerless smartphone-based gait analysis offers a scalable alternative to laboratory motion capture systems, but its robustness under perturbation-induced gait irregularities is insufficiently validated. This pilot study including five participants explored the validity of the SMARTGAIT markerless motion analysis system during normal and perturbed treadmill walking in older adults and investigated whether perturbation-specific model retraining could improve gait event detection. Methods: Five geriatric participants (mean age 83 ± 4.47 years; 40% female) walked on a perturbation treadmill while eight unexpected perturbations in both mediolateral and anteroposterior displacement were administered. Smartphone videos from frontal and diagonal perspectives were recorded and gait events (initial contact, final contact) were manually annotated as ground truth. Model performance was evaluated using F1 score, precision, and recall with a ± 33 ms tolerance window. Retraining was conducted using five-fold cross-validation on perturbed segments. Results: Across 2,477 annotated gait events (2,106 normal; 371 perturbed), initial detection performance was lower during perturbed walking (median F1 score: frontal 0.49; diagonal 0.88) compared to normal walking (median F1 score: frontal 0.68; diagonal 0.96), with large effect sizes despite non-significant Conclusion: This pilot study provides proof of concept for the application of smartphone-based markerless gait event detection during perturbation-induced walking. The findings further support perturbation-specific model retraining as a feasible strategy for adapting AI-based gait analysis to challenging movement conditions. Larger validation studies are required to confirm these preliminary findings before clinical implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.