By 2050, a quarter of the US population will be over the age of 65 with greater than a 40% risk of developing life-altering neuromusculoskeletal pathologies. The potential of wearables, such as Apple AirPods and hearing aids, to provide personalized preventative and predictive health monitoring outside of the clinic is nascent, but large quantities of open-ended data that capture movement in the physical world now exist. Algorithms that leverage existing wearable technology to detect subtle changes to walking mechanics, an early indicator of neuromusculoskeletal pathology, have successfully been developed to determine population-level statistics, but individual-level variability is more difficult to parse from population-level data. Like genetic sequencing, the individual's gait pattern can be discerned by decomposing the movement signal into its fundamental features from which we can detect "mutations" or changes to the pattern that are early indicators of pathology - movement-based biomarkers. We have developed a novel approach to quantify "normal baseline movement" at an individual level, combining methods from gait laboratories with methods used to characterize stellar oscillations. We tested our approach by asking participants to complete an outdoor circuit while wearing a pair of AirPods, using orthopaedic braces to simulate pathology. We found that the novel features we propose are sensitive enough to distinguish between normal walking and brace walking at the population level and at the individual level in all sensor directions (both p $<$ 0.05). We also perform principal component analysis on our population-level and individual-level models, and find significant differences between individuals as well as between the overall population model and most individuals. We also demonstrate the potential of these gait features in deep learning applications.

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