Diseases, disorders, and degenerations that lead to mobility impairment can have a significant impact on functional independence and quality of life. For many such conditions, early diagnosis is the key for successful intervention and maximized wellness, and accurate mobility assessment is central to monitoring progression and evaluating the impact of physical and pharmaceutical therapies. In recent years, inertial body sensor networks (BSNs) have emerged as platforms with the potential to provide continuous, non-invasive collection of high-precision motion data in any location over an extended period of time. Up to now these have been used primarily in-clinic, making absolute assessments from data collected challenging. Out-of-clinic collections introduce nuisance variables, which exacerbate the problem. However, the purpose of mobility assessments as described above does not truly depend on absolute assessments compared to a statistic norm but rather on relative assessments compared to an individual's baseline, which eliminates nuisance variables and makes out-of-clinic assessments using a BSN a more tractable problem.
This project leverages the general motivation of more personalized medicine to enable continuous, longitudinal gait assessments to be made using a BSN for two example conditions with mobility impairment symptoms: normal pressure hydrocephalus (NPH) and multiple sclerosis (MS). Specific anticipated contributions include: 1) identifying signal features that can be feasibly extracted from out-of-clinic inertial BSN data and effectively utilized for detecting individualized gait changes, 2) developing new signal processing algorithms for the individualized identification and relative quantification of gait changes, and 3) exploring techniques for implementing forms of personalized signal processing on resource-constrained BSN platforms to enable more intelligent data reduction and dynamic energy optimization strategies that can extend the battery life of such systems.
