2025 / Movement is a window into health and disease. Advanced laboratory tools like force plates have significantly advanced movement science; however, their inaccessibility in clinical settings due to cost, time, and expertise prevents clinicians from directly utilizing the measurement, diagnostic, and predictive insights these tools offer. Consequently, current approaches to the neuromotor rehabilitation of highly prevalent neurological conditions – stroke and Parkinson's disease – have limited ability to provide optimal, personalized care. Despite clinicians' desire for personalized, targeted interventions that can enhance patient outcomes, the current movement measurement gap in clinical settings often leads to suboptimal care. Given the heterogeneity of movement impairments in these patient populations, there is a pressing need for clinically accessible movement measurement tools to guide tailored treatments. This dissertation presents the development and evaluation of novel movement assessment algorithms that utilize a minimal set of wearable inertial measurement unit (IMU) sensors to accurately estimate clinically relevant gait metrics in patients with stroke or Parkinson’s disease. Additionally, using a single thigh-mounted IMU, we identified unique movement phenotypes that show promise as diagnostic biomarkers of neurological disease and gait impairment, as well as predictive biomarkers of treatment response. Together, these findings advance the translation of movement science into clinical practice, unlocking the practical use of wearable sensors by clinicians. By facilitating in-clinic estimation of gait metrics and identification of movement phenotypes, this work supports advanced clinical-decision making, including the development of targeted treatment plans and the prediction of recovery outcomes, thereby advancing movement as a true window into health and disease. / 2026-01-29T00:00:00Z