The problem is simple to state. DTW works by constructing a grid in which one time series runs along the horizontal axis and the other along the vertical axis, then searching through that grid for the cheapest path from one corner to the opposite one, where the cost of each step measures how poorly the two signals match at that point. The result of this search is a single number, the total cost of the optimal path, and that number is what nearly every pipeline keeps. The path itself, a winding trace that encodes precisely where the two signals agree perfectly, where they drift apart, and where they must be violently stretched to line up, is discarded as an intermediate artifact. For clinical gait analysis, researchers now argue, this is a costly habit, because the local alignment structure of the path is where the biomechanical story actually lives.

In a paper published in the journal Data Mining and Knowledge Discovery, a team at Université Bourgogne Europe in Dijon, France, introduces DiaSeg, a framework designed to rescue that discarded information. Led by Tresor Y. Koffi, with Amel Hidouri, Corentin Legrand and Aurélie Bertaux as co-contributors, the method takes the optimal warping path produced by DTW and decomposes it into a sequence of diagonal segments, the stretches where the two signals track each other closely and the path climbs steadily along the diagonal of the alignment grid. Breaks between these segments, where the path veers horizontally or vertically to absorb a timing mismatch, are allowed but controlled, so that the segmentation captures meaningful deviations rather than fragmenting into noise.

Each extracted diagonal segment is then characterized by five geometric features, capturing properties such as its length and its position within the alignment. The crucial design choice is that these features require no domain-specific engineering: no biomechanics expert needs to define what a heel strike or a swing phase looks like in advance. The framework turns a black-box distance measure into a vocabulary of temporal primitives that can be fed directly into unsupervised learning algorithms, letting the data reveal its own structure. The source code implementing the entire pipeline has been made publicly available by the authors, lowering the barrier for other groups to adopt and extend the approach.

To test whether this recovered structure actually means anything clinically, the team validated DiaSeg on data from 91 subjects spanning six clinical conditions: healthy aging, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, brain tumor, and stroke. This is a demanding test bed, because these conditions damage the nervous system in very different ways and would be expected to disrupt gait coordination at different points in the walking cycle. If the diagonal segments carried no real information, one would expect their patterns to look like noise. Instead, the opposite happened.

The first finding is that diagonal segments form consistent unsupervised patterns even when the algorithm is given no labels at all. Clustering the segment features produced groupings with a silhouette score of 0.33, a moderate but meaningful level of structure, and these data-driven groupings aligned with biomechanical phase annotations supplied independently by domain knowledge. More striking still, external validation confirmed a near-perfect separation of healthy and pathological gait, with an adjusted Rand index reaching 0.986, a measure of clustering agreement where values close to one indicate almost identical partitions. In other words, without being told who was sick and who was well, the method recovered a structure that essentially matched the clinical labels.

The second finding concerns how pathology actually expresses itself in these features. When used in a supervised setting, the segments discriminated between conditions with an accuracy of 69 percent, and unsupervised clustering at the patient level reached 75 percent. But the researchers observed something subtle: disease did not typically manifest through the properties of any single segment. Instead, pathology appeared as a distributional shift in segment length, an alteration in the overall statistics of how coordination is parcelled across the gait cycle rather than a single dramatic defect. This suggests that neurological disorders degrade movement in a diffuse, statistical way, reshaping the whole texture of the walk rather than breaking one identifiable component. When segment-level features were combined with conventional cycle-level features, classification accuracy climbed to 91.7 percent.

The third finding addresses an honest trade-off. Purely cycle-based methods, which summarize an entire stride into a single representation, achieved a slightly higher accuracy of 91 percent in this study. So why bother with segments at all? The answer is interpretability. A global representation can tell a clinician that two walks differ, but it cannot say where they differ. Diagonal segments, by contrast, provide phase-specific localization: they can pinpoint where within the gait cycle coordination breaks down, which is precisely the kind of information a neurologist or physical therapist needs when assessing a patient with Parkinson’s disease or recovering from a stroke. The two approaches are therefore complementary rather than competing, one optimizing raw discrimination and the other optimizing explanation.

The implications extend beyond gait. DTW is arguably the dominant similarity measure for time series across domains, from speech and handwriting to sensor streams and financial data, and the standard practice of discarding the warping path is equally ubiquitous in all of them. DiaSeg demonstrates a general recipe: extract the diagonal structure of the alignment, characterize it geometrically, and use those primitives for pattern discovery. The work also connects to a broader tradition in signal analysis, including recurrence plots and their quantification, which likewise seek to mine the internal geometry of comparison operations rather than collapsing everything into a single scalar. There is a growing recognition in the machine learning community that interpretability and accuracy need not be enemies, and this study offers a concrete, tested example of having both.

For the clinic, the near-term promise is a diagnostic and monitoring tool that can explain itself. Neurodegenerative diseases such as Parkinson’s and Huntington’s are progressive, and clinicians increasingly rely on quantitative movement measures to track their course and evaluate interventions, including emerging tele-rehabilitation approaches. A framework that localizes where coordination degrades within each stride, and that does so without hand-crafted features, could make such monitoring more informative and more transparent. The work emerged in part from the ENABLE project, a European collaborative effort on evaluating motor capacities and tele-rehabilitation in children with neuromotor disorders, underscoring the applied motivation behind what might otherwise look like an abstract data mining contribution. The method has limitations that future work must address, including its dependence on the quality of the underlying DTW alignment and the moderate size of the validation cohort, but the central message stands. The warping path that scientists have been discarding for nearly half a century turns out to be not a by-product, but a map, and DiaSeg provides the first systematic guide to reading it.

Subject of Research: Interpretable gait analysis using diagonal segment extraction from Dynamic Time Warping paths for neurological disease assessment

Article Title: DiaSeg: diagonal segment extraction from DTW paths for interpretable gait analysis

Article References: Koffi, T. Y., Hidouri, A., Legrand, C., & Bertaux, A. (2026). DiaSeg: diagonal segment extraction from DTW paths for interpretable gait analysis. Data Mining and Knowledge Discovery, 40(6), Article 107. https://doi.org/10.1007/s10618-026-01276-x

Image Credits: AI Generated

DOI: 10.1007/s10618-026-01276-x

Keywords: Dynamic Time Warping, gait analysis, DiaSeg, time series clustering, unsupervised pattern discovery, neurodegenerative disease, interpretable machine learning, Parkinson's disease, data mining, biomechanics, diagonal segments, pattern recognition