Towards personalized environment-aware outdoor gait analysis using a smartphone

Automatic gait analysis in free-living environments using inertial sensors requires individualized approach as local acceleration and velocity profiles vary with the walker and the topological properties of the environment (e.g., walking in the forest vs. walking on sand). Here, we propose a smartphone-based gait assessment architecture which consists of two data processing modules. The first module employs a set of personalized classifiers for automatic recognition of the walking environment. The second module provides accurate step time estimates by selecting the optimal filtering frequency tailored to the predicted environment. The performance of the architecture was evaluated using experimental data collected from 10 participants walking in 10 different conditions typically encountered during daily living. Compared with ground truth data, the architecture successfully recognized the walking environments; the percentage of correctly classified instances was above 92%. It also estimated step time with high accuracy; the mean absolute error was less than 10 ms, outperforming or at the very least matching the performance levels achieved in controlled laboratory trials (indoor flat surface walking). Compared with using one filtering frequency for all environments, using optimal frequency tailored to each environment reduced step time estimation error by more than 39%. To the best of our knowledge, this is the first study which successfully demonstrates that parameter tuning can improve gait characterization in outdoor environments. However, further research using a larger data set (including more participants with varying demographics and degree of impairment) is needed to confirm this result. Our findings highlight the importance of environment-aware gait analysis, and lay the groundwork for a smartphone-based technology that can be used in the community.