AI Learns to Read Shoe Treads to Predict Who Might Slip

Every year, falls claim hundreds of thousands of lives worldwide and send countless more people to hospitals, particularly older adults whose balance and bone strength can no longer absorb the shock of an unexpected tumble. The World Health Organization identifies falls as the second leading cause of unintentional injury deaths globally, and surveillance work published by the United States Centers for Disease Control and Prevention has documented that nonfatal falls and fall-related injuries among adults aged sixty-five and older in the United States rose steadily between 2012 and 2018. The demographic backdrop sharpens the concern: populations in the United States, Canada, and much of the developed world are aging rapidly, with the ranks of the very old expanding faster than any other age group, which means the population most vulnerable to falls is growing at precisely the moment when prevention matters most. In workplaces, slips and trips represent one of the most stubborn categories of occupational injury, and research in ergonomics and forensic biomechanics has repeatedly shown that the interaction between a shoe’s outsole and the walking surface is a decisive factor in whether a person stays upright or goes down. Studies of the biomechanics of slips have traced how a shoe that fails to grip converts an ordinary step into a fall within a fraction of a second, leaving little time for muscular recovery. Now a team of Canadian researchers has turned to one of the most powerful artificial intelligence systems ever built to attack this problem from an unexpected angle: the underside of the shoe itself. Their work, published in the Annals of Biomedical Engineering, demonstrates that a carefully adapted version of the Segment Anything Model can automatically map the complex geometry of footwear outsoles and, in doing so, help predict how slippery a shoe will be before anyone takes a single risky step.

The Segment Anything Model, or SAM, was introduced by Meta AI researchers in 2023 as a foundation model for image segmentation, trained on more than a billion masks across eleven million images. Its promise was audacious: the ability to segment, or precisely outline, virtually any object in any image without task-specific training. In medical imaging, remote sensing, pathology, and even planetary geology, researchers have rushed to harness SAM’s general-purpose vision capabilities. But SAM has a well-documented Achilles heel. When confronted with images dominated by dense, repetitive, fine-grained textures—the branching vessels of a retina, the tangled architecture of a surgical field, or in this case the grooves, channels, and tread patterns of a shoe sole—the model’s performance drops sharply. Footwear outsoles are exactly this kind of challenge. Their tread designs combine geometric regularity with manufacturing variation, wear patterns, and material contrasts that confound a model trained mostly on natural scenes and everyday objects. Shaghayegh Chavoshian, Ali Barzegar Khanghah, and Atena Roshan Fekr, based at the KITE Research Institute of the Toronto Rehabilitation Institute and the Institute of Biomedical Engineering at the University of Toronto, set out to close that gap.

Their strategy was transfer learning, a technique in which a model pretrained on a vast general dataset is retrained, or fine-tuned, on a smaller but highly specific dataset so that its general visual knowledge is redirected toward a narrow task. Fine-tuning foundation models has already proven fruitful in medical image segmentation, where adapted SAM variants have been used for anatomical structures, tumor delineation, and surgical video analysis. The Toronto team reasoned that the same principle should apply to footwear science, where manual annotation of outsole images is notoriously slow and expensive. Segmenting an outsole by hand requires a trained expert to trace, pixel by pixel or polygon by polygon, the boundaries between tread features and the spaces between them—a process that consumes substantial time per image and becomes prohibitive when applied across the many shoes a serious slip-resistance study requires. Automating this bottleneck would open the door to analyzing footwear at a scale previously impractical, potentially transforming how safety standards are written and how shoes are evaluated for consumers, workers, and older adults. The motivation is reinforced by a long line of prior work showing that outsole features such as tread groove geometry, sole hardness, and material wear all influence the friction available at the shoe-floor interface, which makes accurate, scalable measurement of outsole geometry a genuinely valuable scientific target rather than a mere convenience.

To fine-tune the model, the researchers assembled a dataset of forty footwear outsoles, each manually annotated using a graphical annotation tool that allows precise polygonal outlining of image features. Forty shoes may sound modest next to SAM’s eleven-million-image pretraining corpus, but that is precisely the point of transfer learning: the general model already understands edges, shapes, and textures; it needs only a comparatively small volume of domain-specific examples to learn what matters in a shoe sole. The ground truth against which the model was judged came not from mechanical friction devices alone but from human-centered data, in which footwear had been classified as having either low or high slip resistance based on real human testing. Some of that human-centered footwear data was drawn from open access material available through the Rate My Treads website, a resource that aggregates winter footwear performance information. This choice of labels matters. Mechanical slip testers measure friction under controlled conditions, but human slip resistance emerges from an interplay of gait biomechanics, loading rates, and perception that benchtop devices only approximate. By anchoring the labels to how shoes actually perform on people’s feet, the researchers kept the machine learning pipeline aligned with the outcome that ultimately counts—whether a person slips.

The results of the fine-tuning were substantial. Compared with the original, out-of-the-box SAM, the adapted model reduced segmentation loss by 8.11 percent and lifted the intersection over union, the standard overlap metric between predicted and true segmentation masks, to 70.45 percent. Perhaps more strikingly, pixel accuracy climbed from 56.90 to 78.10 percent, and the F1 score, which balances precision and recall, rose from 53.70 to 66.30 percent. Those numbers tell a clear technical story: without task-specific adaptation, SAM could barely delineate outsole features better than chance on some measures, but after fine-tuning it captured roughly two-thirds to three-quarters of the relevant structure. The researchers also examined which image quality factors influenced performance, finding that resolution, contrast, and intensity all significantly affected segmentation quality. This is consistent with a growing literature showing that deep segmentation networks are sensitive to the spectral and spatial characteristics of their inputs; a model fine-tuned on images of one resolution or contrast profile may degrade when fed imagery that differs. For anyone hoping to deploy such systems in the field—photographing shoes in a store, a clinic, or a workplace—the finding underscores that image capture protocols must be standardized for reliable results.

The segmentation outputs were then put to work. From each predicted outsole mask, the pipeline estimated the outsole-ground contact areas—the regions of the sole that would actually press against a floor during walking. These contact features were fed into a downstream machine learning classifier tasked with predicting the slip resistance category of the shoe, low or high, using the human-derived ground truth labels. With an 80/20 train-test split, the classification model achieved 70 percent accuracy and a 64 percent F1 score. While these figures are a distance from clinical certainty, they represent a meaningful proof of concept: the geometry of a shoe’s contact patch, extracted automatically by an adapted foundation model, carries enough information to forecast, with better-than-chance reliability, how the shoe will behave under a human foot on a slippery surface. Earlier work by the same group and collaborators had shown that convolutional neural networks and machine learning models could predict slip resistance from engineered tread features, including studies of winter footwear on glycerol-contaminated surfaces; the new study shortens that pipeline by replacing manual feature extraction with learned segmentation.

The broader context makes the advance more than an academic exercise. Winter footwear rated for slip resistance is still evaluated largely through mechanical tests and, increasingly, through human-centered trials such as those conducted with the maximum achievable incline method, in which participants walk up progressively steeper icy slopes until they slip. Portable slip simulators, cart-type friction measurement devices, and computational models of shoe-floor friction have each expanded the toolkit, but these approaches remain expensive, time-consuming, and difficult to scale to the flood of new footwear models reaching the market each year. Rating programs that rely on such testing simply cannot keep pace. If a vision model can screen outsole designs computationally, flagging promising candidates for human testing and discouraging poor performers before they reach consumers, the entire evaluation ecosystem could accelerate. The authors point to implications for two populations in particular: older adults, for whom a fall can trigger a cascade of fractures, hospitalization, loss of independence, and mortality, and workers in occupations—from construction and healthcare to marine and winter industries—who depend on protective footwear to stay safe in hazardous environments.

The study also contributes to a rapidly evolving conversation about when and how to adapt foundation models. Across computer vision research, SAM has been shown to struggle in concealed scenes, camouflaged object detection, and numerous specialized domains, prompting a wave of adaptation techniques ranging from lightweight adapter layers and parameter-space reconstruction to knowledge distillation, in which a compact student model learns to mimic a large teacher. The Toronto study adds footwear science to the list of domains where modest fine-tuning unlocks outsized gains, and its finding that image quality variables materially shape performance echoes parallel observations in brain tissue segmentation and satellite imagery classification. The researchers acknowledge the inherent constraints of their dataset size—forty annotated outsoles leaves the classifier with limited statistical power—and the 70 percent classification accuracy should be read as an early benchmark rather than a deployable performance ceiling. Scaling the annotated corpus, standardizing image acquisition, and refining the contact-area features that bridge segmentation and prediction are the obvious next steps.

Funded by the Digital Research Alliance of Canada and reviewed by the University Health Network Research Ethics Board, the work forms part of a broader research program at KITE that spans mechanical and human-centered slip testing, gait analysis with multimodal transformers, and slip detection during real human walking trials, alongside earlier investigations of how the edge of a footwear sole influences measured slip resistance. Together these strands sketch a future in which the slip resistance of a shoe could be assessed rapidly, cheaply, and at scale—by photographing its sole, letting a fine-tuned foundation model trace its contact geometry, and letting a classifier render a verdict before the shoe ever meets an icy sidewalk. For the millions of people who navigate winter sidewalks, wet kitchens, oily factory floors, and slick hospital corridors every day, that future could translate into fewer falls, fewer fractures, and a far more transparent footwear market in which slip resistance is not a marketing slogan but a measurable, machine-verified property.

Subject of Research: Fine-tuning the Segment Anything Model for automated footwear outsole segmentation to predict slip resistance.

Article Title: Transfer Learning on Segment Anything Model for Footwear Outsole Segmentation to Predict Footwear Slip Resistance

Article References: Chavoshian, S., Khanghah, A. B., & Fekr, A. R. (2026). Transfer Learning on Segment Anything Model for Footwear Outsole Segmentation to Predict Footwear Slip Resistance. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04331-2

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04331-2

Keywords: Segment Anything Model, transfer learning, footwear outsole segmentation, slip resistance, machine learning, computer vision, fall prevention, biomedical engineering, slip and fall injuries, image segmentation, footwear safety, deep learning