SCH: collaborative research: rehabilitation evaluation of neurologic motor disorders using wearable sensors

The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded New Mexico State University $499,999 on October 1, 2026, to develop and validate wearable sensor systems and patient-specific machine learning methods for rehabilitation monitoring of neurologic motor disorders including traumatic brain injury and autism spectrum disorder.

The research addresses the gap between clinic-based recovery assessments and real-world functional outcomes by combining multimodal wearable sensors with artificial intelligence to track movement, balance, and coordination changes during extended rehabilitation periods. Deliverables include energy-efficient sensing approaches, feature extraction methods from wearable sensor signals, machine learning techniques for analyzing high-dimensional time-series data, and quantitative biomarkers of rehabilitation progress. Clinical studies will examine relationships between wearable sensor measurements and established clinical evaluations in individuals with traumatic brain injury and autism spectrum disorder. The work supports national priorities in artificial intelligence and digital health and integrates undergraduate and graduate research training alongside high school outreach in engineering and health sciences.

The award is structured as a Project Grant under NSF's Engineering program (CFDA 47.041). Performance runs through September 30, 2030, with work conducted at New Mexico State University in Las Cruces, New Mexico.

Abstract

Traumatic brain injury, autism spectrum disorder, and other neurologic motor disorders can affect movement, balance, and coordination. These changes can make daily life harder and reduce quality of life. Rehabilitation may last for months or years. However, recovery is often checked only during clinic visits. This makes IT hard to know how patients move and function at home. This smart and connected health (SCH) project studies wearable sensors and patient-specific artificial intelligence for rehabilitation monitoring outside the clinic. The research aims to create objective measures of recovery. These measures can add to current clinical evaluations and help guide individualized rehabilitation. The project may improve access to remote rehabilitation and help identify delayed recovery earlier. IT also supports national priorities in artificial intelligence and digital health. The project will provide research opportunities for undergraduate and graduate students and engage high school students through outreach in engineering and health sciences. The project will investigate fundamental methods for combining multimodal wearable sensors with patient-specific analysis of complex movement data. The research includes the development of energy-efficient sensing approaches, methods for extracting informative features from wearable sensor signals, and machine learning techniques for analyzing high-dimensional time-series data collected during rehabilitation. These methods will be studied to identify movement patterns associated with neurologic motor disorders, develop quantitative biomarkers of rehabilitation progress, and improve patient-specific assessment of recovery. Clinical studies involving individuals with traumatic brain injury and autism spectrum disorder will examine relationships between wearable sensor measurements and established clinical evaluations while assessing the feasibility of the proposed framework for rehabilitation monitoring. The expected outcomes include new knowledge in wearable sensing, patient-specific machine learning, rehabilitation science, and human biomechanics, providing a foundation for future smart and connected health technologies for personalized rehabilitation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.