MD-Vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy

We introduce a footstep-induced floor vibration sensing system that enables us to quantify the gait pattern of individuals with Muscular Dystrophy (MD) in non-clinical settings. MD is a neuromuscular disorder causing progressive loss of muscle, which leads to symptoms in gait patterns such as toe-walking, frequent falls, balance difficulty, etc. Existing systems that are used for progressive tracking include pressure mats, wearable devices, or direct observation by healthcare professionals. However, they are limited by operational requirements including dense deployment, users' device carrying, special training, etc. To overcome these limitations, we introduce a new approach that senses floor vibrations induced by human footsteps. Gait symptoms in these footsteps are reflected by the vibration signals, which enables monitoring of gait health for individuals with MD. Our approach is non-intrusive, unrestricted by line-of-sight, and thus suitable for in-home deployment. To develop our approach, we characterize the gait pattern of individuals with MD using vibration signals, and infer the health state of the patients based on both symptom-based and signal-based features. However, there are two main challenges: 1) different aspects of human gaits are mixed up in footstep-induced floor vibrations; and 2) structural heterogeneity distorts vibration propagation and attenuation through the floor medium. To overcome the first challenge, we characterize the symptom-based gait features of the footstep-induced floor vibration specific to MD. To minimize the performance inconsistency across different sensing locations in the building, we reduce the structural effects by removing the free-vibration phase due to structural damping. With these two challenges addressed, we evaluate our system performance by conducting a real-world experiment with six patients with MD and seven healthy participants. Our approach achieved 96% accuracy in predicting whether the footstep was from a patient with MD.