  Gait abnormality detection is critical for the early discovery and
progressive tracking of musculoskeletal and neurological disorders, such as
Parkinson's and Cerebral Palsy. Especially, analyzing the foot-floor contacts
during walking provides important insights into gait patterns, such as contact
area, contact force, and contact time, enabling gait abnormality detection
through these measurements. Existing studies use various sensing devices to
capture such information, including cameras, wearables, and force plates.
However, the former two lack force-related information, making it difficult to
identify the causes of gait health issues, while the latter has limited
coverage of the walking path. In this study, we leverage footstep-induced
structural vibrations to infer foot-floor contact profiles and detect gait
abnormalities. The main challenge lies in modeling the complex force transfer
mechanism between the foot and the floor surfaces, leading to difficulty in
reconstructing the force and contact profile during foot-floor interaction
using structural vibrations. To overcome the challenge, we first characterize
the floor vibration for each contact type (e.g., heel, midfoot, and toe
contact) to understand how contact forces and areas affect the induced floor
vibration. Then, we leverage the time-frequency response spectrum resulting
from those contacts to develop features that are representative of each contact
type. Finally, gait abnormalities are detected by comparing the predicted
foot-floor contact force and motion with the healthy gait. To evaluate our
approach, we conducted a real-world walking experiment with 8 subjects. Our
approach achieves 91.6% and 96.7% accuracy in predicting contact type and time,
respectively, leading to 91.9% accuracy in detecting various types of gait
abnormalities, including asymmetry, dragging, and midfoot/toe contacts.
