Gait evaluation requires no examination gowns, sensors, or markers—patients simply walk in front of a camera in everyday clothing. This non-invasive, contact-free method minimizes patient burden.

Using deep learning, the system analyzes walking videos to measure gait parameters, including Walking Speed, Step Length, Two-Step Value During Walking, Gait Cycle, Cadence, Step Length Symmetry, Ground Reaction Force, and Knee Range of Motion During Walking. It also assesses gait similarity to locomotive syndrome and estimates mobility decline risk using a model trained on gait patterns of individuals with locomotive syndrome^*¹.

*¹: Kushioka J, Moriguchi Y, et al. (2024) Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study. PLOS Digit Health 3(11): e0000668.
https://doi.org/10.1371/journal.pdig.0000668

Walking video analysis is completed in under 30 seconds^*², enabling results to be reviewed almost immediately after testing. Results can be compared to previous evaluations or to reference values from individuals of the same sex and age group. Reports can be generated for integration into electronic medical records and for patient explanations.

*²: Assuming a minimum upload speed of 2 Mbps and download speed of 25 Mbps.