Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis

Objective: To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. Design: Cross-sectional study. Setting: General community setting (ie, institutional research laboratory and community free -living). Participants: Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free -living. Interventions: Not applicable. Main Outcome Measures: Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. Results: All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). Conclusions: Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications. Archives of Physical Medicine and Rehabilitation 2024;105:546-57 (c) 2023 by the American Congress of Rehabilitation Medicine. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)