Comparison of IMU set-ups for the estimation of gait spatio-temporal parameters in an elderly population

The increasing average age emphasizes the importance of gait analysis in elderly populations. Inertial Measurement Units (IMUs) represent a suitable wearable technology for the characterization of gait by estimating spatio-temporal parameters (STPs). However, the location of inertial sensors on the human body and the associated algorithms for the estimation of gait STPs play a fundamental role and are still open challenges. Accordingly, the aim of this work was to compare three IMUs set-ups (trunk, shanks, and ankles) and correspondent algorithms to a gold standard optoelectronic system for the estimation of gait STPs in a healthy elderly population. In total, 14 healthy elderly subjects walked barefoot at three different speeds. Gait parameters were assessed for each IMUs set-up and compared to those estimated with the gold standard. A statistical analysis based on Pearson correlation, Root Mean Square Error and Bland Altman plots was conducted to evaluate the accuracy of IMUs. Even though all tested set-ups produced accurate results, the IMU on the trunk performed better in terms of correlation (R >= 0.8), RMSE (0.01-0.06 s for temporal parameters, 0.03-0.04 for the limp index), and level of agreement (-0.01 s <= mean error <= 0.01 s, -0.02 s <= standard deviation error <= 0.02 s), also allowing simpler preparation of subjects and minor encumbrance during gait. From the promising results, a similar experiment might be conducted in pathological populations in the attempt to verify the accuracy of IMUs set-ups and algorithms also in non-physiological patterns.

Automated summary

The increasing average age highlights the importance of gait analysis in the elderly population. This study compared three IMU setups and algorithms with a gold standard optoelectronic system to estimate gait parameters in healthy elderly subjects. The IMU placed on the trunk showed the best performance, indicating its potential for accurate gait analysis in non-physiological patterns.