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<p>Wearable sensors quantify gait and mobility in detail, but translating high-dimensional data into clinically actionable insights remains challenging in precision rehabilitation. We applied Topological Data Analysis (TDA) to inertial sensor recordings from multiple gait assessments in 81 individuals with stroke, Parkinson’s disease, lower-limb difference, functional neurological disorders, and healthy controls. TDA generated a unified functional map that organized participants based on shared gait patterns across conditions. Within stroke, three phenotypes, corresponding to low, moderate, and high gait function emerged and aligned with clinical outcomes, while Parkinson’s disease shows a smoother transition from low- to high-function phenotypes. Individuals with lower-limb difference were distributed across other conditions which reflected their variable and shared gait patterns. Supervised machine-learning analysis further identified gait signatures for each phenotype. This framework enables mapping new patients into interpretable functional phenotypes, supports targeted intervention planning, and permits clinical decision-making from sensor data.</p>

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Identifying unique gait phenotypes across neuromotor conditions using wearable inertial sensors and topological data analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Sajjad Daneshgar,
  • Silvia Campagnini,
  • Rebecca Macaluso,
  • Megan K. O’Brien,
  • Richard L. Lieber,
  • Arun Jayaraman

摘要

Wearable sensors quantify gait and mobility in detail, but translating high-dimensional data into clinically actionable insights remains challenging in precision rehabilitation. We applied Topological Data Analysis (TDA) to inertial sensor recordings from multiple gait assessments in 81 individuals with stroke, Parkinson’s disease, lower-limb difference, functional neurological disorders, and healthy controls. TDA generated a unified functional map that organized participants based on shared gait patterns across conditions. Within stroke, three phenotypes, corresponding to low, moderate, and high gait function emerged and aligned with clinical outcomes, while Parkinson’s disease shows a smoother transition from low- to high-function phenotypes. Individuals with lower-limb difference were distributed across other conditions which reflected their variable and shared gait patterns. Supervised machine-learning analysis further identified gait signatures for each phenotype. This framework enables mapping new patients into interpretable functional phenotypes, supports targeted intervention planning, and permits clinical decision-making from sensor data.