Gait parameter and event estimation using smartphones

Background and objectives: The use of smartphones can greatly help for gait parameters estimation during daily
living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-bystep
assessment of smartphone performance in heel strike, step count, step period, and step length estimation.
The influence of smartphone placement and orientation on estimation performance is evaluated as well.
Methods: This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and
rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration
vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection
were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained,
and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute
and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation
with stereophotogrammetry on eleven healthy subjects.
Results: High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for
step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to
those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration
reference frames and heel strike detection method was found in step count.
Conclusion: This study provides detailed information about expected accuracy when smartphone is used as a gait
monitoring tool. The obtained results encourage real life applications.